# Beyond Traditional Methods: Data Mining for
Next-Generation Reliability Assessment

Hireche Zoulikha, Souabni Chaima, Zemmouchi-Ghomari Leila, Rezgui Wail

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Hireche Zoulikha, Souabni Chaima, Zemmouchi-Ghomari Leila, Rezgui Wail. Beyond Traditional Methods: Data Mining for Next-Generation Reliability Assessment. Troisième édition de la journée scientifique du département Génie Industriel et Maintenance, GIM, JSGIM'24, 15 Mai 2024, ENSTA, Alger, May 2024, Algiers, Algeria. hal-04595149

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# Beyond Traditional Methods: Data Mining for Next-Generation Reliability Assessment

Hireche Zoulikha, Souabni Chaima, Zemmouchi-Ghomari Leila, Rezgui Wail

Industrial Engineering and Maintenance Department,

Ecole Nationale Supérieure des Technologies Avancées, ENSTA, Algiers, Algeria

**Abstract:** The advent of Industry 4.0, marked by intricate machinery and systems, urgently requires robust reliability assessment methods. These methods are crucial to ensure the uninterrupted performance of industrial systems. While traditional techniques have been the go-to for reliability assessment, they often fall short of capturing the wealth of data those modern systems generate. This study delves into the potential of data mining, a powerful tool that can uncover hidden patterns and insights within this data. Unlike traditional methods, data mining operates without preconceived assumptions, leading to a more comprehensive understanding of system behavior. By harnessing these techniques, the aim is to enhance the reliability of industrial systems by revealing these hidden insights and patterns, sparking intrigue in the possibilities of data mining.

**Key-Words:** Data Mining, Reliability Assessment, Maintenance Management, Predictive Analytics, Machine Learning.

## 1. Introduction

The industrial landscape is undergoing various transformations characterized by faster innovation, personalized goods, greater flexibility, reduced hierarchies, and enhanced resource efficiency. To address these changes, industries have embraced a new level of value chain organization and control called Industry 4.0 [1]. As a result, industries have shown a heightened dependence on complex machinery and systems, ranging from software applications to mechanical engineering. This increased complexity underscores the urgent need for a strong focus on reliability, emphasizing the importance of our research in the context of Industry 4.0.

Reliability assessment is a systematic process that evaluates the performance of a product, system, or service under specific conditions. Its goal is to determine the ability of the entity to perform its intended function without failure over a defined period [1]. Since its inception in the 1950s, reliability theory has rapidly evolved and has become pivotal in advancing critical sectors such as aviation, aerospace, and nuclear energy. Importantly, it has significantly improved the quality of everyday items like computers, appliances, and vehicles, directly impacting people's lives. The ability to manufacture high-end equipment with high dependability and extended life has emerged as a critical strategic indicator of a country's global power and competitiveness [2]. Therefore, ensuring and enhancing system reliability is paramount for industries and society.

Traditionally, reliability assessment has relied on well-established statistical and probabilistic techniques to understand and quantify a system's reliability, availability, and maintainability. However, in today's data-rich environment, the growing volume and complexity of data generated by modern systems present challenges and opportunities for reliability assessment. While traditional methods provide a foundation, they may only partially capture the rich information hidden within this data.

Therefore, data mining emerges as a powerful tool to address these limitations of traditional methods. Data mining is a multidisciplinary field designed to handle various data's vast and complex characteristics. In the reliability assessment context, data mining is a vital facilitator. By leveraging data mining algorithms, organizations can discover hidden patterns (or rules) and information from existing data [3]. These insights can then be used to assess and improve system reliability. Furthermore, data mining techniques offer distinct advantages over traditional methods. Unlike conventional approaches that rely on pre-conceived assumptions or specific data distribution, data mining can uncover hidden patterns without strict preconditions [4] [5].

This study aims to explore the diverse data mining techniques for reliability assessment. This will involve identifying a set of data mining algorithms suitable for analyzing the data generated by industrial machinery. We will also include a description of significant works addressing the application of these techniques, aiming to improve the reliability of industrial systems by uncovering hidden patterns and insights.
# Beyond Traditional Methods: Data Mining for Next-Generation Reliability Assessment

Hireche Zoulikha, Souabni Chaima, Zemmouchi-Ghomari Leila, Rezgui Wail

Industrial Engineering and Maintenance Department,

Ecole Nationale Supérieure des Technologies Avancées, ENSTA, Algiers, Algeria

**Abstract:** The advent of Industry 4.0, marked by intricate machinery and systems, urgently requires robust reliability assessment methods. These methods are crucial to ensure the uninterrupted performance of industrial systems. While traditional techniques have been the go-to for reliability assessment, they often fall short of capturing the wealth of data those modern systems generate. This study delves into the potential of data mining, a powerful tool that can uncover hidden patterns and insights within this data. Unlike traditional methods, data mining operates without preconceived assumptions, leading to a more comprehensive understanding of system behavior. By harnessing these techniques, the aim is to enhance the reliability of industrial systems by revealing these hidden insights and patterns, sparking intrigue in the possibilities of data mining.

**Key-Words:** Data Mining, Reliability Assessment, Maintenance Management, Predictive Analytics, Machine Learning.

## 1. Introduction

The industrial landscape is undergoing various transformations characterized by faster innovation, personalized goods, greater flexibility, reduced hierarchies, and enhanced resource efficiency. To address these changes, industries have embraced a new level of value chain organization and control called Industry 4.0 [1]. As a result, industries have shown a heightened dependence on complex machinery and systems, ranging from software applications to mechanical engineering. This increased complexity underscores the urgent need for a strong focus on reliability, emphasizing the importance of our research in the context of Industry 4.0.

Reliability assessment is a systematic process that evaluates the performance of a product, system, or service under specific conditions. Its goal is to determine the ability of the entity to perform its intended function without failure over a defined period [1]. Since its inception in the 1950s, reliability theory has rapidly evolved and has become pivotal in advancing critical sectors such as aviation, aerospace, and nuclear energy. Importantly, it has significantly improved the quality of everyday items like computers, appliances, and vehicles, directly impacting people's lives. The ability to manufacture high-end equipment with high dependability and extended life has emerged as a critical strategic indicator of a country's global power and competitiveness [2]. Therefore, ensuring and enhancing system reliability is paramount for industries and society.

Traditionally, reliability assessment has relied on well-established statistical and probabilistic techniques to understand and quantify a system's reliability, availability, and maintainability. However, in today's data-rich environment, the growing volume and complexity of data generated by modern systems present challenges and opportunities for reliability assessment. While traditional methods provide a foundation, they may only partially capture the rich information hidden within this data.

Therefore, data mining emerges as a powerful tool to address these limitations of traditional methods. Data mining is a multidisciplinary field designed to handle various data's vast and complex characteristics. In the reliability assessment context, data mining is a vital facilitator. By leveraging data mining algorithms, organizations can discover hidden patterns (or rules) and information from existing data [3]. These insights can then be used to assess and improve system reliability. Furthermore, data mining techniques offer distinct advantages over traditional methods. Unlike conventional approaches that rely on pre-conceived assumptions or specific data distribution, data mining can uncover hidden patterns without strict preconditions [4] [5].

This study aims to explore the diverse data mining techniques for reliability assessment. This will involve identifying a set of data mining algorithms suitable for analyzing the data generated by industrial machinery. We will also include a description of significant works addressing the application of these techniques, aiming to improve the reliability of industrial systems by uncovering hidden patterns and insights.
The remaining part of the paper is organized as follows: section 2 is about the fundamentals of reliability assessment, highlighting traditional methods and their associated challenges. Section 3 delves into data mining and the exploration of its diverse techniques, emphasizing their relevance to reliability assessment. This is The fourth section is followed by the fifth section, which discusses related works in data mining algorithms for reliability assessment. The fifth section is a discussion section in which we present our findings of data mining analysis in the context of reliability assessment. Lastly, we conclude this study in section 6 by highlighting its conclusions and limitations.

# 1. Reliability

Reliability, a fundamental engineering and statistical analysis concept, is essential in industry and technology. It has always been critical in assessing industrial products and/or equipment [6], ensuring that numerous equipment, processes, and systems can perform their required functions under required conditions and over specific durations. This ability is commonly measured using probabilities. - Reliability is, therefore, the probability that the complementary event will occur to failure, resulting in:

$$Reliability = 1 - Failure Probability [7] \quad (1)$$

Reliability is vital for maintaining operational continuity, ensuring safety, and upholding quality standards. Inadequate reliability considerations can result in severe consequences, leading to catastrophic events such as civil aviation disasters, nuclear power plant accidents, spacecraft launch failures, power system shutdowns, and other significant accidents [2]. These incidents highlight the crucial role of robust reliability assessments and maintenance protocols in mitigating risks and protecting against future breakdowns and potential failures.

Over the years, various reliability assessment methods, including qualitative and quantitative methods, have been developed to mitigate these risks and ensure smooth operation of complex systems.

**Qualitative methods** employ models, diagrams, or other visual representations to analyze and understand the reliability of a manufacturing system and its components [1]. They provide a structured framework for identifying probable failure modes, evaluating their consequences, and prioritizing mitigation activities based on severity and the likelihood of occurrence. These models are typically based on the experience of experts in the field. They may involve the use of Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), its extension, Failure Mode and Effects and Criticality Analysis (FMECA), and Hazard and Operability Study (HAZOP), or other similar techniques to evaluate the likelihood of different failure scenarios [1].

On the other hand, **quantitative methods** involve mathematical models, statistical analysis, and probabilistic techniques to assess the reliability of a system and its components [1]. Examples of these quantitative methods include Reliability Block Diagram (RBD), Markov Analysis, and Weibull Analysis. These methods are more data-driven since they rely heavily on the availability and quality of historical data. This allows for the calculation of specific metrics like Mean Time Between Failures (MTBF), Mean Time to Failure (MTTF), failure rate, and availability, providing numerical values that, in turn, offer insights into the reliability performance of a system, allowing engineers to make informed decisions regarding maintenance schedules, design improvements, and overall system optimization.

One significant advantage of qualitative approaches is their capacity to detect probable failure modes and evaluate their influence on system performance. In contrast, quantitative approaches give a more thorough and exact assessment of system dependability based on actual data and statistical analysis [1]. However, combining qualitative and quantitative methodologies can provide the most complete and valuable insights into a system's reliability in many cases and practices.

Figure 1 provides a comprehensive illustration of various reliability assessment methods, highlighting their significance within the context of this study.

The limitations of traditional reliability assessment methods, particularly their dependence on readily available and high-quality data, highlight the need for more robust approaches. In the reliability engineering realm, data's significance cannot be overstated. However, the quality of data collected in many industrial settings can be unreliable, hindering the effectiveness of traditional methods.

This is where **data mining** emerges as a powerful tool. Data mining techniques can address the challenge of uncertain and inconsistent data commonly found in CMMS (Computerized Maintenance Management
The remaining part of the paper is organized as follows: section 2 is about the fundamentals of reliability assessment, highlighting traditional methods and their associated challenges. Section 3 delves into data mining and the exploration of its diverse techniques, emphasizing their relevance to reliability assessment. This is The fourth section is followed by the fifth section, which discusses related works in data mining algorithms for reliability assessment. The fifth section is a discussion section in which we present our findings of data mining analysis in the context of reliability assessment. Lastly, we conclude this study in section 6 by highlighting its conclusions and limitations.

# 1. Reliability

Reliability, a fundamental engineering and statistical analysis concept, is essential in industry and technology. It has always been critical in assessing industrial products and/or equipment [6], ensuring that numerous equipment, processes, and systems can perform their required functions under required conditions and over specific durations. This ability is commonly measured using probabilities. - Reliability is, therefore, the probability that the complementary event will occur to failure, resulting in:

$$Reliability = 1 - Failure Probability [7] \quad (1)$$

Reliability is vital for maintaining operational continuity, ensuring safety, and upholding quality standards. Inadequate reliability considerations can result in severe consequences, leading to catastrophic events such as civil aviation disasters, nuclear power plant accidents, spacecraft launch failures, power system shutdowns, and other significant accidents [2]. These incidents highlight the crucial role of robust reliability assessments and maintenance protocols in mitigating risks and protecting against future breakdowns and potential failures.

Over the years, various reliability assessment methods, including qualitative and quantitative methods, have been developed to mitigate these risks and ensure smooth operation of complex systems.

**Qualitative methods** employ models, diagrams, or other visual representations to analyze and understand the reliability of a manufacturing system and its components [1]. They provide a structured framework for identifying probable failure modes, evaluating their consequences, and prioritizing mitigation activities based on severity and the likelihood of occurrence. These models are typically based on the experience of experts in the field. They may involve the use of Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), its extension, Failure Mode and Effects and Criticality Analysis (FMECA), and Hazard and Operability Study (HAZOP), or other similar techniques to evaluate the likelihood of different failure scenarios [1].

On the other hand, **quantitative methods** involve mathematical models, statistical analysis, and probabilistic techniques to assess the reliability of a system and its components [1]. Examples of these quantitative methods include Reliability Block Diagram (RBD), Markov Analysis, and Weibull Analysis. These methods are more data-driven since they rely heavily on the availability and quality of historical data. This allows for the calculation of specific metrics like Mean Time Between Failures (MTBF), Mean Time to Failure (MTTF), failure rate, and availability, providing numerical values that, in turn, offer insights into the reliability performance of a system, allowing engineers to make informed decisions regarding maintenance schedules, design improvements, and overall system optimization.

One significant advantage of qualitative approaches is their capacity to detect probable failure modes and evaluate their influence on system performance. In contrast, quantitative approaches give a more thorough and exact assessment of system dependability based on actual data and statistical analysis [1]. However, combining qualitative and quantitative methodologies can provide the most complete and valuable insights into a system's reliability in many cases and practices.

Figure 1 provides a comprehensive illustration of various reliability assessment methods, highlighting their significance within the context of this study.

The limitations of traditional reliability assessment methods, particularly their dependence on readily available and high-quality data, highlight the need for more robust approaches. In the reliability engineering realm, data's significance cannot be overstated. However, the quality of data collected in many industrial settings can be unreliable, hindering the effectiveness of traditional methods.

This is where **data mining** emerges as a powerful tool. Data mining techniques can address the challenge of uncertain and inconsistent data commonly found in CMMS (Computerized Maintenance Management
System) systems, as highlighted by [4]. By leveraging sophisticated algorithms like K-means and association rules, data mining can extract valuable insights and hidden patterns even from unreliable datasets, as demonstrated by [8] and [9] in [4].

<figure><img src="image_1.png" /><figcaption>Figure 1: Reliability Assessment Methods.</figcaption></figure>

Data mining offers a pathway to transform unreliable data into a valuable asset for reliability assessment. The following section will explore data mining, its stages, and its techniques in more detail.

## 2. Data mining

Since the 80s, more data has been generated, driven by technological advancements, the advent of the Internet, and the digitization of various industries. Moreover, data alone is useless, given that it consists of raw, unrefined, and commonly unfiltered information [10].

[11] [12]. Thus, data transformation into useful and valuable information is needed to discover insights and create knowledge and information-based decision-making [10] [13] [14]. Knowledge discovery from data, described as data mining, involves finding and extracting critical information from the gathered data [10] [15].

At a granular level, data mining represents a pivotal step in Knowledge Discovery in Databases (KDD), extracting previously unknown information and standable hidden patterns in data [16]. While data mining and KDD are occasionally used interchangeably, they are commonly recognized as distinct entities. Data mining is a common catchphrase for data analysis. Since the evolution of data warehousing technology, the acceptance of data mining methods has quickly accelerated over the last ten years [3]. So, data mining is the process of sorting large data sets to identify patterns and relationships that can help solve business problems through data analysis. This entails exploring large batches of raw data to reveal concealed trends, employing advanced analytics techniques within the broader field of data science. Moreover, Data mining improves decision-making ability. It can target datasets and predict outcomes using machine-learning techniques [3]. Thus, this synergy between reliability and Data mining (DM) signifies a paradigm shift in industrial maintenance practices.

### 2.1. Data Mining Steps

The data mining process is broadly described through four steps that support the data analysis process [17]. Table 1 summarizes the different stages of the data mining process.

**Data gathering:** The data-gathering stage is essential to initiate the process, as collecting relevant data involves three steps. Data is generally stored unorganized in data warehouses or big data environments. Hence, appropriate data collection is initiated in the data-gathering step [18]. It is important to collect relevant data to achieve a reliable result.
System) systems, as highlighted by [4]. By leveraging sophisticated algorithms like K-means and association rules, data mining can extract valuable insights and hidden patterns even from unreliable datasets, as demonstrated by [8] and [9] in [4].

<figure><img src="image_2.png" /><figcaption>Figure 1: Reliability Assessment Methods.</figcaption></figure>

Data mining offers a pathway to transform unreliable data into a valuable asset for reliability assessment. The following section will explore data mining, its stages, and its techniques in more detail.

## 2. Data mining

Since the 80s, more data has been generated, driven by technological advancements, the advent of the Internet, and the digitization of various industries. Moreover, data alone is useless, given that it consists of raw, unrefined, and commonly unfiltered information [10].

[11] [12]. Thus, data transformation into useful and valuable information is needed to discover insights and create knowledge and information-based decision-making [10] [13] [14]. Knowledge discovery from data, described as data mining, involves finding and extracting critical information from the gathered data [10] [15].

At a granular level, data mining represents a pivotal step in Knowledge Discovery in Databases (KDD), extracting previously unknown information and standable hidden patterns in data [16]. While data mining and KDD are occasionally used interchangeably, they are commonly recognized as distinct entities. Data mining is a common catchphrase for data analysis. Since the evolution of data warehousing technology, the acceptance of data mining methods has quickly accelerated over the last ten years [3]. So, data mining is the process of sorting large data sets to identify patterns and relationships that can help solve business problems through data analysis. This entails exploring large batches of raw data to reveal concealed trends, employing advanced analytics techniques within the broader field of data science. Moreover, Data mining improves decision-making ability. It can target datasets and predict outcomes using machine-learning techniques [3]. Thus, this synergy between reliability and Data mining (DM) signifies a paradigm shift in industrial maintenance practices.

### 2.1. Data Mining Steps

The data mining process is broadly described through four steps that support the data analysis process [17]. Table 1 summarizes the different stages of the data mining process.

**Data gathering:** The data-gathering stage is essential to initiate the process, as collecting relevant data involves three steps. Data is generally stored unorganized in data warehouses or big data environments. Hence, appropriate data collection is initiated in the data-gathering step [18]. It is important to collect relevant data to achieve a reliable result.
<table><caption>Table 1: Stages of Data Mining.</caption><thead><tr><td>Data Mining Steps</td><td>Description</td></tr></thead><tbody><tr><td>Data gathering</td><td>Helps in the collection of relatable data from data warehouses or data lakes.</td></tr><tr><td>Data preparation</td><td>Prepare the data for mining through error-fixing.</td></tr><tr><td>Mining the data</td><td>Implement AI and automation to sort the data according to the analysis.</td></tr><tr><td>Analyzing the collected data and making interpretations</td><td>It helps to establish relations and find patterns for improved decision-making.</td></tr></tbody></table>

For analysis. Therefore, the significance of the step is related to the overall data extensive data analysis process. After completing the step, a data scientist moves a data set to a data pool with associates' access to further data processing [19].

**Data Preparation:** The data preparation steps become decisive when preparing the data for mining. The steps begin with exploring the collected data and making a precise data set for further processing [20]. Additionally, a consistent data set is achieved through data preparation. Moreover, a data set is filtered in the data preparation process for further analysis by removing errors and arranging it to support the expected outcome of the study [21] [19].

**Mining the data:** After the data set is prepared, it is handled with algorithmic operations to verify the collected data and prepare it for further analysis [19]. Machine learning and artificial intelligence are implemented in data mining to achieve results related to the analysis. Hence, data mining is significant for sorting a data set and extracting reliable data for analysis with the help of artificial intelligence [22] [19].

**Analyzing the collected data and making interpretations:** The last step of data mining is extracting valuable insights from the collected and processed data. Moreover, the decision-making process for the business depends on the steps of analysis and interpretation of the collected data. Finding relations and patterns is essential to make data-driven decisions for a company [23]. Therefore, achieving sustainability for the business patterns and data relationships is analyzed in this step. Besides that, the presentation of patterns and relations through business intelligence tools is conducted to understand the ladder and make decisions accordingly [24] [19].

Figure 2 offers an overview of the data mining and analytics process, as referenced in [25], showcasing its integral role in the research framework.

## 2.2. Data Mining Techniques

This subsection will explore the principal data mining techniques used in reliability assessment. Each method offers unique strengths in uncovering hidden patterns and insights. Those techniques can be classified into two main categories based on their learning approach: supervised and unsupervised. This classification is based on the nature of the data used for training and the learning goals of the algorithms employed. In the following, we will explore some primary data mining techniques used in reliability analysis.

Figure 3 below illustrates a hierarchy of data mining techniques mentioned in the section.

### 2.2.1 Supervised Methods

Supervised Learning Methods involve only labeled data (training patterns with known category labels) [26]. According to [27], supervised algorithms are performed for classification tasks when the goal is to predict the categorical class label of new instances based on past observations; on the other hand, they are used for regression tasks to predict a continuous numerical value based on input features.

**Classification:** It aims to categorize each item in a set of input data into one of a predefined set of classes or groups. The data analysis task classification is where a model or classifier is constructed to predict categorical labels (the class label attributes) [28]. Within the context of reliability analysis, it is commonly used for generic failure prediction [29], prediction of gradual states of degradation [30], and simultaneous failure prediction. Notable algorithms within this domain include Decision Trees, Random Forest, and Support Vector Machines (SVM).

* **Decision Trees:** Decision trees consist of nodes that specify a particular attribute of the data, branch that represent a test of each attribute value, and leaves that correspond to the terminal decision of class assignment, for instance, in the dataset [31]. It facilitates understanding how individuals decide by adhering to the tree's structure. In the DT, the inner nodes represent the property, the branch represents the decision rule, and the leaf represents the outcomes [29].

* **Random Forest:** At its core, the RF method combines an ensemble of unpruned decision trees to
<table><caption>Table 1: Stages of Data Mining.</caption><thead><tr><td>Data Mining Steps</td><td>Description</td></tr></thead><tbody><tr><td>Data gathering</td><td>Helps in the collection of relatable data from data warehouses or data lakes.</td></tr><tr><td>Data preparation</td><td>Prepare the data for mining through error-fixing.</td></tr><tr><td>Mining the data</td><td>Implement AI and automation to sort the data according to the analysis.</td></tr><tr><td>Analyzing the collected data and making interpretations</td><td>It helps to establish relations and find patterns for improved decision-making.</td></tr></tbody></table>

For analysis. Therefore, the significance of the step is related to the overall data extensive data analysis process. After completing the step, a data scientist moves a data set to a data pool with associates' access to further data processing [19].

**Data Preparation:** The data preparation steps become decisive when preparing the data for mining. The steps begin with exploring the collected data and making a precise data set for further processing [20]. Additionally, a consistent data set is achieved through data preparation. Moreover, a data set is filtered in the data preparation process for further analysis by removing errors and arranging it to support the expected outcome of the study [21] [19].

**Mining the data:** After the data set is prepared, it is handled with algorithmic operations to verify the collected data and prepare it for further analysis [19]. Machine learning and artificial intelligence are implemented in data mining to achieve results related to the analysis. Hence, data mining is significant for sorting a data set and extracting reliable data for analysis with the help of artificial intelligence [22] [19].

**Analyzing the collected data and making interpretations:** The last step of data mining is extracting valuable insights from the collected and processed data. Moreover, the decision-making process for the business depends on the steps of analysis and interpretation of the collected data. Finding relations and patterns is essential to make data-driven decisions for a company [23]. Therefore, achieving sustainability for the business patterns and data relationships is analyzed in this step. Besides that, the presentation of patterns and re-nations through business intelligence tools is conducted to understand the ladder and make decisions accordingly [24] [19].

Figure 2 offers an overview of the data mining and analytics process, as referenced in [25], showcasing its integral role in the research framework.

## 2.2. Data Mining Techniques

This subsection will explore the principal data mining techniques used in reliability assessment. Each method offers unique strengths in uncovering hidden patterns and insights. Those techniques can be classified into two main categories based on their learning approach: supervised and unsupervised. This classification is based on the nature of the data used for training and the learning goals of the algorithms employed. In the following, we will explore some primary data mining techniques used in reliability analysis.

Figure 3 below illustrates a hierarchy of data mining techniques mentioned in the section.

### 2.2.1 Supervised Methods

Supervised Learning Methods involve only labeled data (training patterns with known category labels) [26]. According to [27], supervised algorithms are performed for classification tasks when the goal is to predict the categorical class label of new instances based on past observations; on the other hand, they are used for regression tasks to predict a continuous numerical value based on input features.

**Classification:** It aims to categorize each item in a set of input data into one of a predefined set of classes or groups. The data analysis task classification is where a model or classifier is constructed to predict categorical labels (the class label attributes) [28]. Within the context of reliability analysis, it is commonly used for generic failure prediction [29], prediction of gradual states of degradation [30], and simultaneous failure prediction. Notable algorithms within this domain include Decision Trees, Random Forest, and Support Vector Machines (SVM).

* **Decision Trees:** Decision trees consist of nodes that specify a particular attribute of the data, branch that represent a test of each attribute value, and leaves that correspond to the terminal decision of class assignment, for instance, in the dataset [31]. It facilitates understanding how individuals decide by adhering to the tree's structure. In the DT, the inner nodes represent the property, the branch represents the decision rule, and the leaf represents the outcomes [29].

* **Random Forest:** At its core, the RF method combines an ensemble of unpruned decision trees to
achieve more efficient results with more than one decision maker, as in other methods [29] [32].

* **Support Vector Machine:** Shortened as SVM, it is widely known to optimize the expected solution. Decision functions are determined directly from the training data using SVM to maximize the separation (margin) between the decision borders in a highly dimensional feature space. This classification strategy minimizes the classification errors of the training data and obtains a better generalization ability. The classification skills of SVMs and Other techniques differ significantly, especially when the number of input data is small [33].

* **Regression:** Regression in data mining is a predictive modeling technique that compares past successes and failures and then uses those formulas to predict future outcomes [34]. This is achieved by determining the relationship between one dependent variable and a series of other mutable (independent variables) [35]. Among the many algorithms that fall within the regression category, two stand out: Linear Regression (LiR) and Symbolic Regression (SR).

* **Linear Regression (LiR)** refers to a multivariate linear combination of regression coefficients (i.e., constants and weights of input variables). The generalized least square technique estimates the coefficients. Given that linear regression is deterministic and parameterless, there is no need to configure anything other than a data split for model training and testing [36].

* **Symbolic Regression (SR)** refers to models as syntax trees consisting of arbitrary mathematical symbols (terminals: constants and variables, non-terminals: mathematical functions) that can be seamlessly translated into plain mathematical functions. For target estimation, syntax trees are evaluated top-down. Syn-tax trees are developed using evolutionary algorithms' stochastic genetic programming technique [36].

<figure><img src="image_2.png" /><figcaption>Figure 2: Overview of the data mining and analytics methodology [25].</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Figure 3: Data Mining Techniques.</figcaption></figure>
achieve more efficient results with more than one decision maker, as in other methods [29] [32].

* **Support Vector Machine:** Shortened as SVM, it is widely known to optimize the expected solution. Decision functions are determined directly from the training data using SVM to maximize the separation (margin) between the decision borders in a highly dimensional feature space. This classification strategy minimizes the classification errors of the training data and obtains a better generalization ability. The classification skills of SVMs and Other techniques differ significantly, especially when the number of input data is small [33].

* **Regression:** Regression in data mining is a predictive modeling technique that compares past successes and failures and then uses those formulas to predict future outcomes [34]. This is achieved by determining the relationship between one dependent variable and a series of other mutable (independent variables) [35]. Among the many algorithms that fall within the regression category, two stand out: Linear Regression (LiR) and Symbolic Regression (SR).

* **Linear Regression (LiR)** refers to a multivariate linear combination of regression coefficients (i.e., constants and weights of input variables). The generalized least square technique estimates the coefficients. Given that linear regression is deterministic and parameterless, there is no need to configure anything other than a data split for model training and testing [36].

* **Symbolic Regression (SR)** refers to models as syntax trees consisting of arbitrary mathematical symbols (terminals: constants and variables, non-terminals: mathematical functions) that can be seamlessly translated into plain mathematical functions. For target estimation, syntax trees are evaluated top-down. Syn-tax trees are developed using evolutionary algorithms' stochastic genetic programming technique [36].

<figure><img src="image_3.png" /><figcaption>Figure 2: Overview of the data mining and analytics methodology [25].</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 3: Data Mining Techniques.</figcaption></figure>
### 2.2.2 Unsupervised Learning

Unsupervised Learning methods, unlike Supervised Learning, involve only unlabeled data [26], or we can say that in unsupervised learning, the desired output is not given [37]. The primary goal is to uncover inherent patterns within the unlabeled and then assign a label to each data value without the guidance of known outcomes. That means the techniques are appropriate for the clustering and association rule mining tasks. They are suitable for creating the labels in the data that are subsequently used to implement supervised learning tasks [38].

**Clustering:** In data mining, clustering is more challenging than classification. An operational definition of clustering can be stated as follows: Given a representation of n objects, find K groups based on a similarity measure such that the similarities between objects in the same group are high. In contrast, the similarities between objects in different groups are low [26]. Clustering is mainly used to detect anomalies by identifying patterns in the provided data.

The most common algorithms used for clustering tasks are K-means, Hierarchical clustering, and density-based clustering, with Hierarchical clustering being the oldest of the three.

* **K-means clustering:** It is the most popular and used clustering technique. K-means clustering is a method that partitions n data points into k clusters. Each data point is assigned to the cluster with the nearest mean, the cluster center. K-means clustering aims to minimize the within-cluster variance, measured by the squared Euclidean distance [4]. The iterative process of K-means ends when the centroids no longer move, indicating convergence.

* **Hierarchical clustering:** As the name indicates, it provides a hierarchical decomposition of the data, with a typical representation being a dendrogram. Hierarchical clustering techniques recursively find nested clusters in an agglomerative or divisive manner. Agglomerative clustering is one where each data point starts in its cluster and, after that, merges a similar pair of clusters successively, resulting in a hierarchy. Alternatively, divisive clustering begins with all the data points in one cluster and repeatedly divides each cluster into smaller ones. Once divisions or fusions are made, they cannot be reversed; thus, re-adjustment is impossible with hierarchical clustering [39].

* **Density-based clustering:** Unlike the K-means explained previously, it does not divide the data into a predefined number of clusters. Instead, it focuses on identifying clusters based on the density of data points and then grouping densely packed points. The most popular implementation of Density-based clustering is the applications with Noise (DBSCAN).

**Association Rule:** Association analysis, also known as association rule learning, aims to identify the association between seemingly uncorrelated data by measuring the degree of association between two variables [4]. It is generally ideal for extensive data handling and can model complex multi-way relationships given an adequate data set. Among the prominent association rule mining algorithms, the Apriori algorithm is one of the most well-known and utilized.

## 3. Related Works

This section addresses a literature review of existing research on applying data mining techniques for reliability assessment. The methodology employed for this review adhered to a systematic approach, starting with a comprehensive literature search carried out across well-known electronic scientific databases, including Google Scholar, ScienceDirect, IEEE Xplore, and Scopus.

The choice of keywords was based on terms that are common in the literature and related to this review, such as ("data mining" OR "data mining techniques" OR "data analysis") AND ("reliability" OR "central- tenancy reliability" OR "reliability assessment" OR "reliability analysis").

The quality assessment criteria that guide the search were:

Inclusion: Papers written in English.

Inclusion: Recent Works within the last 15 years. Inclusion: Papers that include the application of data mining techniques.

Exclusion: theses (ongoing works) and books (mature works).

Exclusion: Papers about reliability but not from a data mining perspective.

The survey results were collected in scientific databases using keywords and applying the research criteria.

They included 59 research papers.

In what follows, we will highlight the contributions of ten selected works (the most relevant ones) on applying data mining techniques for reliability assessment.
### 2.2.2 Unsupervised Learning

Unsupervised Learning methods, unlike Supervised Learning, involve only unlabeled data [26], or we can say that in unsupervised learning, the desired output is not given [37]. The primary goal is to uncover inherent patterns within the unlabeled and then assign a label to each data value without the guidance of known outcomes. That means the techniques are appropriate for the clustering and association rule mining tasks. They are suitable for creating the labels in the data that are subsequently used to implement supervised learning tasks [38].

**Clustering:** In data mining, clustering is more challenging than classification. An operational definition of clustering can be stated as follows: Given a representation of n objects, find K groups based on a similarity measure such that the similarities between objects in the same group are high. In contrast, the similarities between objects in different groups are low [26]. Clustering is mainly used to detect anomalies by identifying patterns in the provided data.

The most common algorithms used for clustering tasks are K-means, Hierarchical clustering, and density-based clustering, with Hierarchical clustering being the oldest of the three.

* **K-means clustering:** It is the most popular and used clustering technique. K-means clustering is a method that partitions n data points into k clusters. Each data point is assigned to the cluster with the nearest mean, the cluster center. K-means clustering aims to minimize the within-cluster variance, measured by the squared Euclidean distance [4]. The iterative process of K-means ends when the centroids no longer move, indicating convergence.

* **Hierarchical clustering:** As the name indicates, it provides a hierarchical decomposition of the data, with a typical representation being a dendrogram. Hierarchical clustering techniques recursively find nested clusters in an agglomerative or divisive manner. Agglomerative clustering is one where each data point starts in its cluster and, after that, merges a similar pair of clusters successively, resulting in a hierarchy. Alternatively, divisive clustering begins with all the data points in one cluster and repeatedly divides each cluster into smaller ones. Once divisions or fusions are made, they cannot be reversed; thus, re-adjustment is impossible with hierarchical clustering [39].

* **Density-based clustering:** Unlike the K-means explained previously, it does not divide the data into a predefined number of clusters. Instead, it focuses on identifying clusters based on the density of data points and then grouping densely packed points. The most popular implementation of Density-based clustering is the applications with Noise (DBSCAN).

**Association Rule:** Association analysis, also known as association rule learning, aims to identify the association between seemingly uncorrelated data by measuring the degree of association between two variables [4]. It is generally ideal for extensive data handling and can model complex multi-way relationships given an adequate data set. Among the prominent association rule mining algorithms, the Apriori algorithm is one of the most well-known and utilized.

## 3. Related Works

This section addresses a literature review of existing research on applying data mining techniques for reliability assessment. The methodology employed for this review adhered to a systematic approach, starting with a comprehensive literature search carried out across well-known electronic scientific databases, including Google Scholar, ScienceDirect, IEEE Xplore, and Scopus.

The choice of keywords was based on terms that are common in the literature and related to this review, such as ("data mining" OR "data mining techniques" OR "data analysis") AND ("reliability" OR "central- tenancy reliability" OR "reliability assessment" OR "reliability analysis").

The quality assessment criteria that guide the search were:

Inclusion: Papers written in English.

Inclusion: Recent Works within the last 15 years. Inclusion: Papers that include the application of data mining techniques.

Exclusion: theses (ongoing works) and books (mature works).

Exclusion: Papers about reliability but not from a data mining perspective.

The survey results were collected in scientific databases using keywords and applying the research criteria.

They included 59 research papers.

In what follows, we will highlight the contributions of ten selected works (the most relevant ones) on applying data mining techniques for reliability assessment.
[4] investigates the causes of low-quality maintenance data in the petrochemical industry's Computerized Maintenance Management Systems (CMMS). They argue that despite the potential of CMMS data for reliability analysis and equipment health assessments, poor data quality can significantly hinder these efforts. The main focus of this study is to apply data mining technology, including quality metrics, the association rule, and clustering, to identify the root causes of low-quality maintenance data. First, they establish a reference standard for CMMS function modules and data columns specific to the petrochemical industry. Second, they employ data quality metrics to assess the completeness and accuracy of maintenance records. Finally, data mining techniques like K-means clustering and association rule analysis explore operational and management practices that contribute to poor data quality. The authors find that a combination of ineffective maintenance policies, the low integrity of key system columns, nonadherence to the policy, and misunderstanding of column definitions were the primary reasons behind the low-quality data in their case study. They conclude by proposing that their data mining-based method offers a systematic approach to identifying areas for improvement in CMMS data quality, ultimately leading to more reliable equipment health assessments and informed maintenance decisions.

The research in [40] delves into reliability modeling for CNC systems, addressing a common challenge: incorporating failure correlations between different failure modes and causes. They argue that traditional methods relying solely on lifetime failure data miss these de tendencies, leading to inaccurate models. Their work proposes a data mining-based approach for CNC systems that leverages association rule mining to identify these correlations. Using the Apriori algorithm, the study first establishes the relationship between failure positions and causes. This information is then used to define a failure correlation factor integrated into a Weibull distribution model for reliability estimation. The results demonstrate that this approach accurately represents the system's reliability more than models that neglect failure correlations. This work highlights the potential of data mining techniques like association rule mining to improve the accuracy of reliability models, particularly in scenarios with complex failed dependencies.

[41] contributes to the reliability analysis in agricultural machinery, explicitly focusing on grain combine harvesters, by proposing an innovative approach based on Failure Mode, Effects, and Criticality Analysis (FMECA) coupled with Data Mining Technology. This study responds to the limitations of traditional FMECA methods, which can be subjective and expensive due to reliance on expert judgment and limited test data. The authors' approach leverages data mining techniques implemented in Python to collect and analyze fault data from the grain harvester header.

Specifically, they use the Apriori algorithm to identify frequently occurring failure modes and the Analytic Hierarchy Process (AHP) to prioritize critical failure causes. Their results indicate that the cutter blade is the most vital component within the header assembly, highlighting the importance of prioritizing inspections and maintenance for this component. This study demonstrates the potential of data mining to improve the objectivity, efficiency, and cost-effectiveness of FMECA for agricultural machinery reliability analysis.

In [42], a novel system identification methodology that integrates data mining techniques to enhance the reliability of identification processes was introduced. Traditionally, system identification involves comparing predicted and observed responses to determine the state of a system and its parameters. However, the reliability of such identification methods has yet to be studied in prior research. The proposed methodology addresses this gap by generating a population of candidate models and assessing their characteristics to gauge identification reliability. By leveraging data mining techniques, the methodology extracts features from the candidate models, providing insights into identification quality and facilitating improvements. This approach extends beyond structural engineering applications, offering potential utility across various domains. The study demonstrates the effectiveness of correlation measurements, principal component analysis (PCA), and decision trees in identifying key variables and separating good and bad models. Overall, this methodology is a valuable tool for engineers involved in monitoring and maintaining engineering systems, showcasing the potential of data mining in enhancing reliability analysis across diverse fields.

While data mining is commonly used to build software fault prediction models, [43] explores its potential for broadly improving software reliability. They argue that traditional development practices rely on poorly documented APIs, leading to misuse and potential security vulnerabilities. Their work highlights the applicability of data mining techniques beyond just fault prediction. The authors propose leveraging data mining to identify usage patterns and relationships within Application Programming Interfaces (APIs). Specifically, they suggest techniques like association rule mining and clustering to uncover patterns that indicate correct API usage. This information can guide development and improve software reliability by reducing API misuse and associated errors.
[4] investigates the causes of low-quality maintenance data in the petrochemical industry's Computerized Maintenance Management Systems (CMMS). They argue that despite the potential of CMMS data for reliability analysis and equipment health assessments, poor data quality can significantly hinder these efforts. The main focus of this study is to apply data mining technology, including quality metrics, the association rule, and clustering, to identify the root causes of low-quality maintenance data. First, they establish a reference standard for CMMS function modules and data columns specific to the petrochemical industry. Second, they employ data quality metrics to assess the completeness and accuracy of maintenance records. Finally, data mining techniques like K-means clustering and association rule analysis explore operational and management practices that contribute to poor data quality. The authors find that a combination of ineffective maintenance policies, the low integrity of key system columns, nonadherence to the policy, and misunderstanding of column definitions were the primary reasons behind the low-quality data in their case study. They conclude by proposing that their data mining-based method offers a systematic approach to identifying areas for improvement in CMMS data quality, ultimately leading to more reliable equipment health assessments and informed maintenance decisions.

The research in [40] delves into reliability modeling for CNC systems, addressing a common challenge: incorporating failure correlations between different failure modes and causes. They argue that traditional methods relying solely on lifetime failure data miss these de tendencies, leading to inaccurate models. Their work proposes a data mining-based approach for CNC systems that leverages association rule mining to identify these correlations. Using the Apriori algorithm, the study first establishes the relationship between failure positions and causes. This information is then used to define a failure correlation factor integrated into a Weibull distribution model for reliability estimation. The results demonstrate that this approach accurately represents the system's reliability more than models that neglect failure correlations. This work highlights the potential of data mining techniques like association rule mining to improve the accuracy of reliability models, particularly in scenarios with complex failed dependencies.

[41] contributes to the reliability analysis in agricultural machinery, explicitly focusing on grain combine harvesters, by proposing an innovative approach based on Failure Mode, Effects, and Criticality Analysis (FMECA) coupled with Data Mining Technology. This study responds to the limitations of traditional FMECA methods, which can be subjective and expensive due to reliance on expert judgment and limited test data. The authors' approach leverages data mining techniques implemented in Python to collect and analyze fault data from the grain harvester header.

Specifically, they use the Apriori algorithm to identify frequently occurring failure modes and the Analytic Hierarchy Process (AHP) to prioritize critical failure causes. Their results indicate that the cutter blade is the most vital component within the header assembly, highlighting the importance of prioritizing inspections and maintenance for this component. This study demonstrates the potential of data mining to improve the objectivity, efficiency, and cost-effectiveness of FMECA for agricultural machinery reliability analysis.

In [42], a novel system identification methodology that integrates data mining techniques to enhance the reliability of identification processes was introduced. Traditionally, system identification involves comparing predicted and observed responses to determine the state of a system and its parameters. However, the reliability of such identification methods has yet to be studied in prior research. The proposed methodology addresses this gap by generating a population of candidate models and assessing their characteristics to gauge identification reliability. By leveraging data mining techniques, the methodology extracts features from the candidate models, providing insights into identification quality and facilitating improvements. This approach extends beyond structural engineering applications, offering potential utility across various domains. The study demonstrates the effectiveness of correlation measurements, principal component analysis (PCA), and decision trees in identifying key variables and separating good and bad models. Overall, this methodology is a valuable tool for engineers involved in monitoring and maintaining engineering systems, showcasing the potential of data mining in enhancing reliability analysis across diverse fields.

While data mining is commonly used to build software fault prediction models, [43] explores its potential for broadly improving software reliability. They argue that traditional development practices rely on poorly documented APIs, leading to misuse and potential security vulnerabilities. Their work highlights the applicability of data mining techniques beyond just fault prediction. The authors propose leveraging data mining to identify usage patterns and relationships within Application Programming Interfaces (APIs). Specifically, they suggest techniques like association rule mining and clustering to uncover patterns that indicate correct API usage. This information can guide development and improve software reliability by reducing API misuse and associated errors.
- Investigations by [44] introduce a novel approach to prognostics in the Aeronautics sector based on classification algorithms. This approach focuses on binary classification tasks rather than the traditional ones to estimate the probability of failure in the upcoming timeframe, using advanced data mining algorithms such as Support Vector Machine (SVM) and Decision Trees (DT).

- In [45], random forest (RF) was employed as a classification algorithm, accompanied by two feature selection methods: a wrapper approach based on the beam search algorithm and a new filter method based on the Kolmogorov-Smirnov test; the results of which are compared to those of a human expert. The work develops a data-driven method for predicting future failures of air compression in commercial vehicles. Furthermore, according to the authors, the techniques developed and tested to handle feature selection with inconsistent data sets, imbalanced and noisy class labels, and multiple examples per vehicle.

- [46] employed a Support Vector Machine (SVM) to develop a predictive maintenance module to predict integral-type faults. The model proposed in this research is explicitly targeting ion-implantation tools. Furthermore, this method utilizes process iteration data to achieve real-time prediction. Although there are no comparisons between SVMs and other data mining techniques, compare the cost. The proposed approach is based on classical preventive maintenance approaches. Hence, the authors state the potential of the recommended module to minimize overall maintenance costs.

- The authors in [47] employed another type of SVM, but in this case, it was used for regression purposes called Support Vector Regression (SVR). The study proposes modifying the SVR kernel to address challenges in prognostic applications, aiming to improve the prediction accuracy of remaining helpful life for industrial systems and equipment. The proposed model was tested on a simplified simulated time-series data set and showed improvement over the traditional SVR formulation.

- [48] employed the K-means clustering method to automatically group the dissolved gas data from the insulating oil of a power transformer. The aim was to characterize each identified cluster, indicating a specific fault or signaling a potential maintenance action. Utilizing the Euclidean distance as a criterion for similarity within the k-means algorithm, the searchers successfully delineated four distinct clusters: the first class revealed the presence of electric arcing with high energy, while the second cluster indicated an abnormal temperature rise of the oil. The third cluster highlighted a period characterized by an accelerated in-crease in the production of all gases, signaling potential operational issues or anomalies and the fourth cluster was associated with post-treatment periods of the oil, indicating maintenance or remedial actions to restore the oil properties and the transformer's performance. Table 2 below summarizes the studied papers, detailing the techniques/algorithms used, contributions, and limitations.

Additionally, the reviewed papers exhibit a variety of data mining techniques employed for improving system reliability, ranging from classification algorithms like random forests and support vector machines (SVMs) for fault prediction to association rule mining that uncovers relationships between variables to understand failure correlations. These studies, which draw on disciplines like data science, highlight the multidisciplinary character of reliability engineering. This fusion of knowledge allows researchers to identify potential failures and understand the underlying causes, leading to more targeted and effective preventative measures. Despite the wide range of application domains—from software fault prediction and maintenance management to system identification—expected contributions emerge, highlighting the integration of data-driven methodologies to improve reliability, spot system flaws, and develop new analytical strategies. Furthermore, the focus on specific application domains like software engineering, semiconductor manufacturing, and power transformers showcases the versatility of data mining techniques. This adaptability demonstrates the potential for broader application across various industries where system reliability is paramount.

These developments are accompanied, nonetheless, by a recognition of persistent limitations and challenges. Notably, the struggle to capture non-linear relationships between variables, data quality issues, and the integration hurdles of predictive maintenance systems highlight areas ripe for future exploration. Researchers are actively exploring solutions, with advanced techniques showing promise for complex data. Integrating domain expertise with data mining can also lead to more robust models. Addressing these challenges offers exciting avenues for advancement, necessitating the development of more robust algorithms, improved data pre-processing, and seamless integration strategies. Therefore, this synthesis not only demonstrates the breadth of contemporary reliability research but also offers insight into the ongoing push to expand the boundaries of this field.
- Investigations by [44] introduce a novel approach to prognostics in the Aeronautics sector based on classification algorithms. This approach focuses on binary classification tasks rather than the traditional ones to estimate the probability of failure in the upcoming timeframe, using advanced data mining algorithms such as Support Vector Machine (SVM) and Decision Trees (DT).

- In [45], random forest (RF) was employed as a classification algorithm, accompanied by two feature selection methods: a wrapper approach based on the beam search algorithm and a new filter method based on the Kolmogorov-Smirnov test; the results of which are compared to those of a human expert. The work develops a data-driven method for predicting future failures of air compression in commercial vehicles. Furthermore, according to the authors, the techniques developed and tested to handle feature selection with inconsistent data sets, imbalanced and noisy class labels, and multiple examples per vehicle.

- [46] employed a Support Vector Machine (SVM) to develop a predictive maintenance module to predict integral-type faults. The model proposed in this research is explicitly targeting ion-implantation tools. Furthermore, this method utilizes process iteration data to achieve real-time prediction. Although there are no comparisons between SVMs and other data mining techniques, compare the cost. The proposed approach is based on classical preventive maintenance approaches. Hence, the authors state the potential of the recommended module to minimize overall maintenance costs.

- The authors in [47] employed another type of SVM, but in this case, it was used for regression purposes called Support Vector Regression (SVR). The study proposes modifying the SVR kernel to address challenges in prognostic applications, aiming to improve the prediction accuracy of remaining helpful life for industrial systems and equipment. The proposed model was tested on a simplified simulated time-series data set and showed improvement over the traditional SVR formulation.

- [48] employed the K-means clustering method to automatically group the dissolved gas data from the insulating oil of a power transformer. The aim was to characterize each identified cluster, indicating a specific fault or signaling a potential maintenance action. Utilizing the Euclidean distance as a criterion for similarity within the k-means algorithm, the searchers successfully delineated four distinct clusters: the first class revealed the presence of electric arcing with high energy, while the second cluster indicated an abnormal temperature rise of the oil. The third cluster highlighted a period characterized by an accelerated in-crease in the production of all gases, signaling potential operational issues or anomalies and the fourth cluster was associated with post-treatment periods of the oil, indicating maintenance or remedial actions to restore the oil properties and the transformer's performance. Table 2 below summarizes the studied papers, detailing the techniques/algorithms used, contributions, and limitations.

Additionally, the reviewed papers exhibit a variety of data mining techniques employed for improving system reliability, ranging from classification algorithms like random forests and support vector machines (SVMs) for fault prediction to association rule mining that uncovers relationships between variables to understand failure correlations. These studies, which draw on disciplines like data science, highlight the multidisciplinary character of reliability engineering. This fusion of knowledge allows researchers to identify potential failures and understand the underlying causes, leading to more targeted and effective preventative measures. Despite the wide range of application domains—from software fault prediction and maintenance management to system identification—expected contributions emerge, highlighting the integration of data-driven methodologies to improve reliability, spot system flaws, and develop new analytical strategies. Furthermore, the focus on specific application domains like software engineering, semiconductor manufacturing, and power transformers showcases the versatility of data mining techniques. This adaptability demonstrates the potential for broader application across various industries where system reliability is paramount.

These developments are accompanied, nonetheless, by a recognition of persistent limitations and challenges. Notably, the struggle to capture non-linear relationships between variables, data quality issues, and the integration hurdles of predictive maintenance systems highlight areas ripe for future exploration. Researchers are actively exploring solutions, with advanced techniques showing promise for complex data. Integrating domain expertise with data mining can also lead to more robust models. Addressing these challenges offers exciting avenues for advancement, necessitating the development of more robust algorithms, improved data pre-processing, and seamless integration strategies. Therefore, this synthesis not only demonstrates the breadth of contemporary reliability research but also offers insight into the ongoing push to expand the boundaries of this field.
## 4. Discussion

Incorporating data mining tools into reliability analysis significantly transforms industrial maintenance methods. Engineers can use data mining to enhance conventional reliability assessment techniques, providing more precise insights and facilitating proactive maintenance plans.

One of the primary challenges in reliability analysis lies in the quality of data [4] available for assessment. The reliability of equipment safety and the determination of inspection periods heavily rely on the quality of data extracted from Computerized Maintenance Management Systems (CMMS). However, it's widely acknowledged that CMMS maintenance records often need more quality, which can undermine reliability assessments. The causes of low data quality can be divided into two issues: one related to the system itself and the other related to operation and management [4]. This limitation has made engineers hesitant to use such data for decision-making processes, as discussed in [4]. Because of this, engineers should always look for methods and tools to make the most out of the data they use for their research.

Data mining offers a solution to the challenge of low-quality maintenance data, which in turn helps improve reliability by enabling engineers to extract valuable insights from large datasets, as mentioned in [19], even in the presence of noise and inconsistencies. Techniques such as K-means clustering, association rule analysis, classification methods, and others have proven invaluable in uncovering hidden patterns and relationships within the data, thereby facilitating the identification of root causes behind data quality issues. This may take the reliability assessment to another level and help gain time, effort, and precious information.

Although progress has been achieved in addressing problems related to data quality, several areas in the current research environment still offer exciting opportunities for further study because data mining for reliability has yet to be extensively addressed in prior research. While recent research has concentrated on creating methodologies for evaluating and enhancing data quality, more nuanced approaches tailored to particular industry contexts are still required. These approaches could develop tools and methods for assessing and enhancing data quality and other maintenance systems. Improving the accuracy and dependability of maintenance records involves investigating methods for data validation, anomaly detection, and error repair.

<table><thead><tr><td>Ref</td><td>Techniques/algorithms used</td><td>Contribution</td><td>Limitations</td></tr></thead><tbody><tr><td rowspan="2">[4]</td><td>Data mining technology, quality metrics, association rule, and clustering.</td><td>Integrates techniques to investigate low-quality maintenance records, showing promising results.</td><td rowspan="2">Additionally, the study identified deficiencies in the CMMS system, such as policy, software, and operation dimensions, leading to conflicts, low-quality records, and erroneous data accumulation. Data cleaning and conversion challenges can compromise the reliability and usefulness of the resulting data for failure analysis. The article also highlights decision trees' limitations in handling combinations of variables that determine the classes of data points.</td></tr><tr><td>Review of RAGAGEP, failure analysis standards, and data quality metrics.</td><td>Identifies deficiencies in software design, data quality trends, and policy shortcomings. Offers a systematic analysis of maintenance record quality that is more objective than expert-based methods. It helps improve maintenance record quality, reducing resistance to equipment diagnosis.</td></tr></tbody></table>
## 4. Discussion

Incorporating data mining tools into reliability analysis significantly transforms industrial maintenance methods. Engineers can use data mining to enhance conventional reliability assessment techniques, providing more precise insights and facilitating proactive maintenance plans.

One of the primary challenges in reliability analysis lies in the quality of data [4] available for assessment. The reliability of equipment safety and the determination of inspection periods heavily rely on the quality of data extracted from Computerized Maintenance Management Systems (CMMS). However, it's widely acknowledged that CMMS maintenance records often need more quality, which can undermine reliability assessments. The causes of low data quality can be divided into two issues: one related to the system itself and the other related to operation and management [4]. This limitation has made engineers hesitant to use such data for decision-making processes, as discussed in [4]. Because of this, engineers should always look for methods and tools to make the most out of the data they use for their research.

Data mining offers a solution to the challenge of low-quality maintenance data, which in turn helps improve reliability by enabling engineers to extract valuable insights from large datasets, as mentioned in [19], even in the presence of noise and inconsistencies. Techniques such as K-means clustering, association rule analysis, classification methods, and others have proven invaluable in uncovering hidden patterns and relationships within the data, thereby facilitating the identification of root causes behind data quality issues. This may take the reliability assessment to another level and help gain time, effort, and precious information.

Although progress has been achieved in addressing problems related to data quality, several areas in the current research environment still offer exciting opportunities for further study because data mining for reliability has yet to be extensively addressed in prior research. While recent research has concentrated on creating methodologies for evaluating and enhancing data quality, more nuanced approaches tailored to particular industry contexts are still required. These approaches could develop tools and methods for assessing and enhancing data quality and other maintenance systems. Improving the accuracy and dependability of maintenance records involves investigating methods for data validation, anomaly detection, and error repair.

<table><thead><tr><td>Ref</td><td>Techniques/algorithms used</td><td>Contribution</td><td>Limitations</td></tr></thead><tbody><tr><td>[4]</td><td>Data mining technology, quality metrics, association rule, and clustering.<br/>Review of RAGAGEP, failure analysis standards, and data quality metrics.</td><td>Integrates techniques to investigate low-quality maintenance records, showing promising results.<br/>Identifies deficiencies in software design, data quality trends, and policy shortcomings.<br/>Offers a systematic analysis of maintenance record quality that is more objective than expert-based methods.<br/>It helps improve maintenance record quality, reducing resistance to equipment diagnosis.</td><td>Additionally, the study identified deficiencies in the CMMS system, such as policy, software, and operation dimensions, leading to conflicts, low-quality records, and erroneous data accumulation.<br/>Data cleaning and conversion challenges can compromise the reliability and usefulness of the resulting data for failure analysis.<br/>The article also highlights decision trees' limitations in handling combinations of variables that determine the classes of data points.</td></tr></tbody></table>
<table><tr><td>[40]</td><td>Association rule mining technique was used for reliability modeling.<br>Apriori algorithm was employed for association rule mining.<br>The maximum likelihood estimation method was used for parameter estimation.</td><td>The proposed reliability modeling is based on the degree of correlation between failures.<br>Introduced failure correlation factor into parameter estimation for reliability modeling.<br>I used association rule mining to study failure correlation in a CNCsystem.<br>A model with a failure correlation factor is suitable for multiple failuremodes.</td><td>Lack of objective basis for deterring--<br>mining failure correlation factor.<br>Reliability modeling mainly focused on failure time data processing.</td></tr><tr><td>[41]</td><td>FMECA analysis method-based on Data Mining Technology.<br>Data visualization using Pythonfor reliability research.<br>Data collection with Python Programming Language.</td><td>Proposes FMECA analysis method based on Data Mining Technology.<br>Identifies blade part as the most hazardous in grain harvester.<br>Enhances reliability analysis with Python for fault data processing.<br>Integrates intelligent monitoring for equipment reliability and safety analysis.</td><td>Subjectivity, ambiguity, high test cost, and complex data acquisition.<br>Problems in failure mode, influence, and hazard analysis.<br>Existing issues in reliability testing of agricultural machinery.</td></tr><tr><td>[42]</td><td>PGSL algorithm for stochastic global search in system identification.<br>PCA is used as a weightingmethod for model characteristics.</td><td>Data mining techniques improve system identification reliability--.<br>Identifying candidate models and reliability indications.</td><td>Cannot obtain relationships between more than two parameters simultaneously.<br>Linear data mining methods like PCA cannot reveal non-linear relationships.<br>Decision trees struggle with combinations of linear or non-linear relationships.<br>Techniques used cannot identify non-linear relationships between model variables.</td></tr><tr><td>[43]</td><td>Classification trees, association discovery, clustering, artificial neural networks, etc.<br>Quinlan proposed the ID3 algorithm for classification tree induction.</td><td>Data mining techniques for softwarefault prediction andquality enhancement.<br>Exploration of datamining algorithms forsoftware reliability improvement.</td><td>Incomplete specifications can lead to perceived unreliability in software systems.<br>Few works on clustering techniques applied to software engineering data.</td></tr></table>
<table><tr><td>[40]</td><td>Association rule mining technique was used for reliability modeling.<br>Apriori algorithm was employed for association rule mining.<br>The maximum likelihood estimation method was used for parameter estimation.</td><td>The proposed reliability modeling is based on the degree of correlation between failures.<br>Introduced failure correlation factor into parameter estimation for reliability modeling.<br>I used association rule mining to study failure correlation in a CNCsystem.<br>A model with a failure correlation factor is suitable for multiple failuremodes.</td><td>Lack of objective basis for deterring--<br>mining failure correlation factor.<br>Reliability modeling mainly focused on failure time data processing.</td></tr><tr><td>[41]</td><td>FMECA analysis method-based on Data Mining Technology.<br>Data visualization using Pythonfor reliability research.<br>Data collection with Python Programming Language.</td><td>Proposes FMECA analysis method based on Data Mining Technology.<br>Identifies blade part as the most hazardous in grain harvester.<br>Enhances reliability analysis with Python for fault data processing.<br>Integrates intelligent monitoring for equipment reliability and safety analysis.</td><td>Subjectivity, ambiguity, high test cost, and complex data acquisition.<br>Problems in failure mode, influence, and hazard analysis.<br>Existing issues in reliability testing of agricultural machinery.</td></tr><tr><td>[42]</td><td>PGSL algorithm for stochastic global search in system identification.<br>PCA is used as a weightingmethod for model characteristics.</td><td>Data mining techniques improve system identification reliability--.<br>Identifying candidate models and reliability indications.</td><td>Cannot obtain relationships between more than two parameters simultaneously.<br>Linear data mining methods like PCA cannot reveal non-linear relationships.<br>Decision trees struggle with combinations of linear or non-linear relationships.<br>Techniques used cannot identify non-linear relationships between model variables.</td></tr><tr><td>[43]</td><td>Classification trees, association discovery, clustering, artificial neural networks, etc.<br>Quinlan proposed the ID3 algorithm for classification tree induction.</td><td>Data mining techniques for softwarefault prediction andquality enhancement.<br>Exploration of datamining algorithms forsoftware reliability improvement.</td><td>Incomplete specifications can lead to perceived unreliability in software systems.<br>Few works on clustering techniques applied to software engineering data.</td></tr></table>
<table><tr><td>[44]</td><td>Random forests, support vector machines, nearest neighbors, and deep learning techniques.<br/>Grid search, evolutionary search, signal processing, Principle Component Analysis (PCA).<br/>K-Nearest Neighbors (KNN), Gaussian Support Vector Machines (GSVM), Random Forests(RF).</td><td>Proposed novel classification models for prognostics without RUL estimates.<br/>Evaluated machine learning classifiers on real-world aeronautics case studies.</td><td>Traditional classifiers have low performance compared to deep learning methods.<br/>Dataset quality affects pre-predictability, especially for challenging datasets.</td></tr><tr><td>[45]</td><td>A random forest classifier algorithm was used for prediction models.<br/>Feature selection methods: Wear, Usage, Wear with age normalization, Usage with age normalization.<br/>The LVD database was analyzed for vehicle usage patterns and critical parameters.</td><td>Predictive maintenance solutions for the automotive industry using machine learning.<br/>Comparison of machine learning features with human expertfeatures.<br/>Challenges in predictive maintenance for heavy-duty vehicles were discussed.<br/>Use of logged on-board data for predicting air compressor failures.</td><td>Data sources are not designed for data mining.<br/>Loss of data over time due to lack of collection.</td></tr><tr><td>[46]</td><td>Classification methods for pre-the dictation of integral type faults.<br/>Support Vector Machines (SVMs) in dealing with classification problems.<br/>Linear SVMs and Radial Basis Function (RBF) SVMs for comparison.</td><td>Testing the PdM system on an actual production dataset.<br/>Prediction of integral type failures in semiconductor manufacturing-ing processes.<br/>Development of a PdM module for ion-sourcetungsten filament breaks.<br/>Application of Classification methods for pre-dictating equipment failures.</td><td>Historical data-based PdM approach lacks current machine state utilization.<br/>Limited to predicting ion-source tungsten filament breaks in semiconductor manufacturing.</td></tr></table>
<table><tr><td>[44]</td><td>Random forests, support vector machines, nearest neighbors, and deep learning techniques.<br/>Grid search, evolutionary search, signal processing, Principle Component Analysis (PCA).<br/>K-Nearest Neighbors (KNN), Gaussian Support Vector Machines (GSVM), Random Forests(RF).</td><td>Proposed novel classification models for prognostics without RUL estimates.<br/>Evaluated machine learning classifiers on real-world aeronautics case studies.</td><td>Traditional classifiers have low performance compared to deep learning methods.<br/>Dataset quality affects pre-predictability, especially for challenging datasets.</td></tr><tr><td>[45]</td><td>A random forest classifier algorithm was used for prediction models.<br/>Feature selection methods: Wear, Usage, Wear with age normalization, Usage with age normalization.<br/>The LVD database was analyzed for vehicle usage patterns and critical parameters.</td><td>Predictive maintenance solutions for the automotive industry using machine learning.<br/>Comparison of machine learning features with human expertfeatures.<br/>Challenges in predictive maintenance for heavy-duty vehicles were discussed.<br/>Use of logged on-board data for predicting air compressor failures.</td><td>Data sources are not designed for data mining.<br/>Loss of data over time due to lack of collection.</td></tr><tr><td>[46]</td><td>Classification methods for pre-the dictation of integral type faults.<br/>Support Vector Machines (SVMs) in dealing with classification problems.<br/>Linear SVMs and Radial Basis Function (RBF) SVMs for comparison.</td><td>Testing the PdM system on an actual production dataset.<br/>Prediction of integral type failures in semiconductor manufacturing-ing processes.<br/>Development of a PdM module for ion-sourcetungsten filament breaks.<br/>Application of Classification methods for pre-dictating equipment failures.</td><td>Historical data-based PdM approach lacks current machine state utilization.<br/>Limited to predicting ion-source tungsten filament breaks in semiconductor manufacturing.</td></tr></table>
<table><caption>Table 2: Summary of Studied Papers</caption><tr><td>[47]</td><td>Support Vector Regression (SVR) with a modified regression kernel.<br/>Kernel-based approaches for time-series data sets.</td><td>Proposed regression kernel for support vector regression in prognostics.<br/>Highlighted challenges in developing prognostic algorithms using machine learning.<br/>Reviewed traditional and modern data-driven approaches for predictive applications.<br/>I suggested a regression kernel modification for support vector regression.</td><td>Data sets are unstructured with unique characteristics from multiple sources.<br/>Time-series data poses challenges for conventional machine learning algorithms.<br/>Non-stationary characteristics of data are challenging to model.<br/>The precise definition of remaining useful life is unclear.</td></tr><tr><td>[48]</td><td>Principal Component Analysis (PCA) for variable identification.<br/>Clustering by k-means method for classification of dissolved gas data.</td><td>Analysis of power transformer operating periods through dissolved gas concentrations.<br/>Unsupervised classification of gas data to identify significant events.</td><td>The challenges in data cleaning and conversion.<br/>The proposed methodologies, such as correlation measurement, PCA, and decision trees, cannot capture nonlinear relationships between model variables.</td></tr></table>

The stages of the data mining process—data gathering, data preparation, mining, and analyzing the collected data—provide a systematic framework for extracting meaningful insights from raw data. Each stage contributes to the overall reliability analysis by ensuring the derived insights' relevance, accuracy, and reliability.

Furthermore, data mining facilitates predictive analytics, which improves decision-making skills. Engineers can anticipate maintenance requirements and equipment breakdowns using machine learning techniques. This enables proactive interventions to prevent costly downtime and safety hazards. This predictive capacity turns maintenance procedures from reactive to proactive by eliminating operational risks and maximizing resource allocation.

Integrating data mining techniques into reliability assessment offers a transformative approach to maintenance practices. By overcoming the limitations of traditional methods and harnessing the power of big data, engineers can make more informed decisions, optimize maintenance schedules, and ensure the continued reliability and safety of industrial systems and equipment.

## 5. Conclusions

This research explores integrating data mining techniques into reliability assessment for industrial systems. The main finding highlights the transformative potential of data mining in enhancing maintenance practices, enabling engineers to make informed decisions, optimize schedules, and ensure system reliability and safety. The study delves into various data mining techniques, showcasing their effectiveness in uncovering hidden patterns and relationships within large datasets. These methods, such as K-means clustering, association rule analysis, and classification methods, can identify root causes behind data quality issues and elevate reliability assessment to a new level. The predictive analytics enabled by data mining allows for proactive interventions, minimizing downtime and maximizing resource allocation.

This research demonstrates how data mining can revolutionize traditional methods, particularly in Industry 4.0, by overcoming the limitations of conventional techniques and leveraging the power of big data. Engineers can optimize maintenance procedures and ensure the continued reliability of industrial systems and equipment.
<table><caption>Table 2: Summary of Studied Papers</caption><tr><td>[47]</td><td>Support Vector Regression (SVR) with a modified regression kernel.<br/>Kernel-based approaches for time-series data sets.</td><td>Proposed regression kernel for support vector regression in prognostics.<br/>Highlighted challenges in developing prognostic algorithms using machine learning.<br/>Reviewed traditional and modern data-driven approaches for predictive applications.<br/>I suggested a regression kernel modification for support vector regression.</td><td>Data sets are unstructured with unique characteristics from multiple sources.<br/>Time-series data poses challenges for conventional machine learning algorithms.<br/>Non-stationary characteristics of data are challenging to model.<br/>The precise definition of remaining useful life is unclear.</td></tr><tr><td>[48]</td><td>Principal Component Analysis (PCA) for variable identification.<br/>Clustering by k-means method for classification of dissolved gas data.</td><td>Analysis of power transformer operating periods through dissolved gas concentrations.<br/>Unsupervised classification of gas data to identify significant events.</td><td>The challenges in data cleaning and conversion.<br/>The proposed methodologies, such as correlation measurement, PCA, and decision trees, cannot capture nonlinear relationships between model variables.</td></tr></table>

The stages of the data mining process—data gathering, data preparation, mining, and analyzing the collected data—provide a systematic framework for extracting meaningful insights from raw data. Each stage contributes to the overall reliability analysis by ensuring the derived insights' relevance, accuracy, and reliability.

Furthermore, data mining facilitates predictive analytics, which improves decision-making skills. Engineers can anticipate maintenance requirements and equipment breakdowns using machine learning techniques. This enables proactive interventions to prevent costly downtime and safety hazards. This predictive capacity turns maintenance procedures from reactive to proactive by eliminating operational risks and maximizing resource allocation.

Integrating data mining techniques into reliability assessment offers a transformative approach to maintenance practices. By overcoming the limitations of traditional methods and harnessing the power of big data, engineers can make more informed decisions, optimize maintenance schedules, and ensure the continued reliability and safety of industrial systems and equipment.

## 5. Conclusions

This research explores integrating data mining techniques into reliability assessment for industrial systems. The main finding highlights the transformative potential of data mining in enhancing maintenance practices, enabling engineers to make informed decisions, optimize schedules, and ensure system reliability and safety. The study delves into various data mining techniques, showcasing their effectiveness in uncovering hidden patterns and relationships within large datasets. These methods, such as K-means clustering, association rule analysis, and classification methods, can identify root causes behind data quality issues and elevate reliability assessment to a new level. The predictive analytics enabled by data mining allows for proactive interventions, minimizing downtime and maximizing resource allocation.

This research demonstrates how data mining can revolutionize traditional methods, particularly in Industry 4.0, by overcoming the limitations of conventional techniques and leveraging the power of big data. Engineers can optimize maintenance procedures and ensure the continued reliability of industrial systems and equipment.
This paper's implications are significant for industries reliant on complex machinery and systems. Adopting data mining techniques can improve efficiency, lower operational risks, and improve maintenance practices. In the long run, switching from reactive to proactive maintenance techniques can result in lower costs and more output.

However, it is essential to acknowledge the limitations of this study. While data mining shows promise in enhancing reliability assessment, there are still challenges related to data quality and implementation. Future research should focus on developing more nuanced approaches tailored to specific industry contexts. Additionally, exploring the integration of data mining with other emerging technologies could further enhance the reliability and safety of industrial systems.

## References

[1] Jonas Friederich and Sanja Lazarova-Molnar. Reliability assessment of manufacturing systems: A comprehensive overview, challenges, and opportunities. *Journal of Manufacturing Systems*, 72:38–58, 2024.

[2] Mingjian Zuo. System reliability and system re-silence. *Frontiers of Engineering Management*, 8(4):615–619, 2021.

[3] Amitava Bandyopadhyay and Dr. A.C Man-Dal. Improvement of software reliability using data mining techniques. *International Journal of Scientific and Research Publications (IJSRP)*, 12(06):183–187, June 2022.

[4] Yen-Ju Lu, Wei-Chen Lee, and Chen-Hua Wang. Using data mining technology to explore causes of inaccurate reliability data and suggestions for maintenance management. *Journal of Loss Prevention in the Process Industries*, 83:105063, 2023.

[5] Aurora Esteban, Amelia Zafra, and Sebastian Ventura. Data mining in predictive maintenance systems: A taxonomy and systematic review. *Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery*, 12(5):e1471, 2022.

[6] Andrew KS Jardine, Daming Lin, and Dragan Banjevic. A review on machinery diagnostics and prognostics implementing condition-based maintenance. *Mechanical systems and signal processing*, 20(7):1483–1510, 2006.

[7] Tran Van Ta, Doan Minh Thien, and Vo Trong Cang. Marine propulsion system reliability assessment by fault tree analysis. *International Journal of Mechanical Engineering and Applications*, 8(1):1–7, 2016.

[8] Adam Coates and Andrew Y. Ng. *Learning Feature Representations with K-Means*, pages 561–580. Springer Berlin Heidelberg, Berlin, Heidel-berg, 2012.

[9] Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, and Lukasz A. Kurgan. *Unsupervised Learning: Association Rules*, pages 289–306. Springer US, Boston, MA, 2007.

[10] Jovanne C Alejandrino, Jovito Bolacoy Jr, John Vianne B Murcia, et al. Supervised and unsupervised data mining approaches in loan default prediction. *International Journal of Electrical & Computer Engineering (2088-8708)*, 13(2), 2023.

[11] RA Evans. Information vs data, 1981.

[12] Jeanne Harris. Data is useless without the skills to analyze it. *Harvard Business Review*, 13, 2012.

[13] Varun Grover, Roger HL Chiang, Ting-Peng Liang, and Dongsong Zhang. Creating strategic business value from big data analytics: A research framework. *Journal of Management Information Systems*, 35(2):388–423, 2018.

[14] Benjamin CM Fung, Ke Wang, Rui Chen, and Philip S Yu. Privacy-preserving data publishing: A survey of recent developments. *ACM Computing Surveys (Csur)*, 42(4):1–53, 2010.

[15] Camilo Ernesto López Guarín, Elizabeth León Guzmán, and Fabio A González. A model to predict low academic performance at a specific enrollment using data mining. *IEEE Revista Iberoamericana de tecnologias del Aprendizaje*, 10(3):119–125, 2015.

[16] Toon Calders and Bart Custers. What is data mining, and how does it work? In *Discrimination and privacy in the information society: Data mining and profiling in large databases*, pages 27–42. Springer, 2013.
This paper's implications are significant for industries reliant on complex machinery and systems. Adopting data mining techniques can improve efficiency, lower operational risks, and improve maintenance practices. In the long run, switching from reactive to proactive maintenance techniques can result in lower costs and more output.

However, it is essential to acknowledge the limitations of this study. While data mining shows promise in enhancing reliability assessment, there are still challenges related to data quality and implementation. Future research should focus on developing more nuanced approaches tailored to specific industry contexts. Additionally, exploring the integration of data mining with other emerging technologies could further enhance the reliability and safety of industrial systems.

## References

[1] Jonas Friederich and Sanja Lazarova-Molnar. Reliability assessment of manufacturing systems: A comprehensive overview, challenges, and opportunities. *Journal of Manufacturing Systems*, 72:38–58, 2024.

[2] Mingjian Zuo. System reliability and system re-silence. *Frontiers of Engineering Management*, 8(4):615–619, 2021.

[3] Amitava Bandyopadhyay and Dr. A.C Man-Dal. Improvement of software reliability using data mining techniques. *International Journal of Scientific and Research Publications (IJSRP)*, 12(06):183–187, June 2022.

[4] Yen-Ju Lu, Wei-Chen Lee, and Chen-Hua Wang. Using data mining technology to explore causes of inaccurate reliability data and suggestions for maintenance management. *Journal of Loss Prevention in the Process Industries*, 83:105063, 2023.

[5] Aurora Esteban, Amelia Zafra, and Sebastian Ventura. Data mining in predictive maintenance systems: A taxonomy and systematic review. *Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery*, 12(5):e1471, 2022.

[6] Andrew KS Jardine, Daming Lin, and Dragan Banjevic. A review on machinery diagnostics and prognostics implementing condition-based maintenance. *Mechanical systems and signal processing*, 20(7):1483–1510, 2006.

[7] Tran Van Ta, Doan Minh Thien, and Vo Trong Cang. Marine propulsion system reliability assessment by fault tree analysis. *International Journal of Mechanical Engineering and Applications*, 8(1):1–7, 2016.

[8] Adam Coates and Andrew Y. Ng. *Learning Feature Representations with K-Means*, pages 561–580. Springer Berlin Heidelberg, Berlin, Heidel-berg, 2012.

[9] Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, and Lukasz A. Kurgan. *Unsupervised Learning: Association Rules*, pages 289–306. Springer US, Boston, MA, 2007.

[10] Jovanne C Alejandrino, Jovito Bolacoy Jr, John Vianne B Murcia, et al. Supervised and unsupervised data mining approaches in loan default prediction. *International Journal of Electrical & Computer Engineering (2088-8708)*, 13(2), 2023.

[11] RA Evans. Information vs data, 1981.

[12] Jeanne Harris. Data is useless without the skills to analyze it. *Harvard Business Review*, 13, 2012.

[13] Varun Grover, Roger HL Chiang, Ting-Peng Liang, and Dongsong Zhang. Creating strategic business value from big data analytics: A research framework. *Journal of Management Information Systems*, 35(2):388–423, 2018.

[14] Benjamin CM Fung, Ke Wang, Rui Chen, and Philip S Yu. Privacy-preserving data publishing: A survey of recent developments. *ACM Computing Surveys (Csur)*, 42(4):1–53, 2010.

[15] Camilo Ernesto López Guarín, Elizabeth León Guzmán, and Fabio A González. A model to predict low academic performance at a specific enrollment using data mining. *IEEE Revista Iberoamericana de tecnologias del Aprendizaje*, 10(3):119–125, 2015.

[16] Toon Calders and Bart Custers. What is data mining, and how does it work? In *Discrimination and privacy in the information society: Data mining and profiling in large databases*, pages 27–42. Springer, 2013.
[17] Wei Chen, Haoyuan Hong, Mahdi Panahi, Hi- man Shahabi, Yi Wang, Ataollah Shirzadi, Saied Pirasteh, Ali Asghar Alesheikh, Khabat Khos- ravi, Somayeh Panahi, Fatemeh Rezaie, Shaojun Li, Abolfazl Jaafari, Dieu Tien Bui, and Baharin Bin Ahmad. Spatial prediction of landslide susceptibility using GIS-based data mining techniques of anfis with whale optimization algorithm (woa) and grey wolf optimizer (two). *Applied Sciences*, 9(18), 2019.

[18] S Iwin Thanakumar Joseph and Iwin Thanaku- mar. Survey of data mining algorithms for in-intelligent computing systems. *Journal of trends in Computer Science and Smart technology (TC- SST)*, 1(01):14–24, 2019.

[19] Manish Sharma and Richa Gupta. The significance of using data extraction methods for an effective extensive data mining process. In *2023, 2nd Inter-national Conference for Innovation in Technology (INOCON)*, pages 1–4. IEEE, 2023.

[20] Pooya Tabesh, Elham Mousavidin, and Sona Hasani. Implementing big data strategies: A managerial perspective. *Business Horizons*, 62:347–358, 03 2019.

[21] T Senthil Kumar. Data mining-based marketing decision support system using a hybrid machine learning algorithm. *Journal of Artificial Intelligence*, 2(03):185–193, 2020.

[22] Anurag Kumar Verma, Saurabh Pal, and Surjeet Kumar. Classification of skin disease using ensemble data mining techniques. *Asian Pacific journal of cancer prevention: APJCP*, 20(6):1887, 2019.

[23] Özerk Yavuz. A classification and clustering approach uses data mining techniques to analyze the gastrointestinal tract: *International Scientific and Vocational Studies Journal*, 5(2):254–265, 2022.

[24] Manish Sharma, Bhasker Pant, and Vijay Singh. Demographic profile building for a cold start in recommender system: A social media fusion approach. *Materials Today: Proceedings*, 46:11208–11212, 2021.

[25] Zhiqiang Ge, Zhihuan Song, Steven X Ding, and Biao Huang. Data mining and analytics in the process industry: The role of machine learning. *Ieee Access*, 5:20590–20616, 2017.

[26] Anil K Jain. Data clustering: 50 years beyond k- means–pattern recognition letters, 31(8):651–666, 2010.

[27] David R Musicant, Janara M Christensen, and Jamie F Olson. Supervised learning by training on aggregate outputs. In *Seventh IEEE International Conference on Data Mining (ICDM 2007)*, pages 252–261. IEEE, 2007.

[28] Gopalan Kesavaraj and Sreekumar Sukumaran. A study on classification techniques in data mining. In *2013, Fourth International Conference on Computing, communications, and Networking Technologies (ICT)*, pages 1–7. IEEE, 2013.

[29] Mustafa Cakir, Mehmet Ali Guvenc, and Selcuk Mistikoglu. The experimental application of popular machine learning algorithms on predictive maintenance and IoT-based condition monitoring system design. *Computers & Industrial Engineering*, 151:106948, 2021.

[30] Pablo Aqueveque, Luciano Radrigan, Francisco Pastene, Anibal S Morales, and Ernesto Guerra. Data-driven condition monitoring of mining mobile machinery in non-stationary operations using wireless accelerometer sensor modules. *IEEE Access*, 9:17365–17381, 2021.

[31] Michael D Twa, Srinivasan Parthasarathy, Cyn- thia Roberts, Ashraf M Mahmoud, Thomas W Raasch, and Mark A Bullimore. Automated decision tree classification of corneal shape. *Optometry and Vision Science*, 82(12):1038–1046, 2005.

[32] Yongheng Zhao and Yanxia Zhang. Comparison of decision tree methods for finding active objects. *Advances in Space Research*, 41(12):1955–1959, 2008.

[33] Jair Cervantes, Farid Garcia-Lamont, Lisbeth Rodríguez-Mazahua, and Asdrubal Lopez. A comprehensive survey on support vector machine classification: Applications, challenges, and trends. *Neurocomputing*, 408:189–215, 2020.

[34] Prajak Chertchom. A comparison study between data mining tools over regression methods: Recommendation for SMEs. In *2018 5th International Conference on Business and Industrial Research (ICBIR)*, pages 46–50. IEEE, 2018.

[35] Salim Jibrin Danbatta and Asaf Varol. Predicting student's final graduation GPA using data mining and regression methods: a Kano Informatics Institute case study. In *2020 8th International Symposium on*
[17] Wei Chen, Haoyuan Hong, Mahdi Panahi, Hi- man Shahabi, Yi Wang, Ataollah Shirzadi, Saied Pirasteh, Ali Asghar Alesheikh, Khabat Khos- ravi, Somayeh Panahi, Fatemeh Rezaie, Shaojun Li, Abolfazl Jaafari, Dieu Tien Bui, and Baharin Bin Ahmad. Spatial prediction of landslide susceptibility using GIS-based data mining techniques of anfis with whale optimization algorithm (woa) and grey wolf optimizer (two). *Applied Sciences*, 9(18), 2019.

[18] S Iwin Thanakumar Joseph and Iwin Thanaku- mar. Survey of data mining algorithms for in-intelligent computing systems. *Journal of trends in Computer Science and Smart technology (TC- SST)*, 1(01):14–24, 2019.

[19] Manish Sharma and Richa Gupta. The significance of using data extraction methods for an effective extensive data mining process. In *2023, 2nd Inter-national Conference for Innovation in Technology (INOCON)*, pages 1–4. IEEE, 2023.

[20] Pooya Tabesh, Elham Mousavidin, and Sona Hasani. Implementing big data strategies: A managerial perspective. *Business Horizons*, 62:347–358, 03 2019.

[21] T Senthil Kumar. Data mining-based marketing decision support system using a hybrid machine learning algorithm. *Journal of Artificial Intelligence*, 2(03):185–193, 2020.

[22] Anurag Kumar Verma, Saurabh Pal, and Surjeet Kumar. Classification of skin disease using ensemble data mining techniques. *Asian Pacific journal of cancer prevention: APJCP*, 20(6):1887, 2019.

[23] Özerk Yavuz. A classification and clustering approach uses data mining techniques to analyze the gastrointestinal tract: *International Scientific and Vocational Studies Journal*, 5(2):254–265, 2022.

[24] Manish Sharma, Bhasker Pant, and Vijay Singh. Demographic profile building for a cold start in recommender system: A social media fusion approach. *Materials Today: Proceedings*, 46:11208–11212, 2021.

[25] Zhiqiang Ge, Zhihuan Song, Steven X Ding, and Biao Huang. Data mining and analytics in the process industry: The role of machine learning. *Ieee Access*, 5:20590–20616, 2017.

[26] Anil K Jain. Data clustering: 50 years beyond k- means–pattern recognition letters, 31(8):651–666, 2010.

[27] David R Musicant, Janara M Christensen, and Jamie F Olson. Supervised learning by training on aggregate outputs. In *Seventh IEEE International Conference on Data Mining (ICDM 2007)*, pages 252–261. IEEE, 2007.

[28] Gopalan Kesavaraj and Sreekumar Sukumaran. A study on classification techniques in data mining. In *2013, Fourth International Conference on Computing, communications, and Networking Technologies (ICT)*, pages 1–7. IEEE, 2013.

[29] Mustafa Cakir, Mehmet Ali Guvenc, and Selcuk Mistikoglu. The experimental application of popular machine learning algorithms on predictive maintenance and IoT-based condition monitoring system design. *Computers & Industrial Engineering*, 151:106948, 2021.

[30] Pablo Aqueveque, Luciano Radrigan, Francisco Pastene, Anibal S Morales, and Ernesto Guerra. Data-driven condition monitoring of mining mobile machinery in non-stationary operations using wireless accelerometer sensor modules. *IEEE Access*, 9:17365–17381, 2021.

[31] Michael D Twa, Srinivasan Parthasarathy, Cyn- thia Roberts, Ashraf M Mahmoud, Thomas W Raasch, and Mark A Bullimore. Automated decision tree classification of corneal shape. *Optometry and Vision Science*, 82(12):1038–1046, 2005.

[32] Yongheng Zhao and Yanxia Zhang. Comparison of decision tree methods for finding active objects. *Advances in Space Research*, 41(12):1955–1959, 2008.

[33] Jair Cervantes, Farid Garcia-Lamont, Lisbeth Rodríguez-Mazahua, and Asdrubal Lopez. A comprehensive survey on support vector machine classification: Applications, challenges, and trends. *Neurocomputing*, 408:189–215, 2020.

[34] Prajak Chertchom. A comparison study between data mining tools over regression methods: Recommendation for SMEs. In *2018 5th International Conference on Business and Industrial Research (ICBIR)*, pages 46–50. IEEE, 2018.

[35] Salim Jibrin Danbatta and Asaf Varol. Predicting student's final graduation GPA using data mining and regression methods: a Kano Informatics Institute case study. In *2020 8th International Symposium on*
Digital Forensics and Security (IS-DFS), pages 1-7. IEEE, 2020.

[36] Jan Zenisek, Florian Holzinger, and Michael Af- fenzeller. Machine learning-based concept drift detection for predictive maintenance. *Computers & Industrial Engineering*, 137:106031, 2019.

[37] Aized Soofi and Arshad Awan. Classification techniques in machine learning: Applications and issues. *Journal of Basic Applied Sciences*, 13:459-465, 08 2017.

[38] Michael W Berry, Azlinah Mohamed, and Bee Wah Yap. *Supervised and unsupervised learning for data science*. Springer, 2019.

[39] P. Govender and V. Sivakumar. Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980-2019). *Atmospheric Pollution Research*, 11(1):40-56, 2020.

[40] Guangpeng Liu and Chong Peng. Research on reliability modeling of CNC systems based on association rule mining. *Procedia Manufacturing*, 11:1162-1169, 2017.

[41] YANG Xiaohui, Guohai ZHANG, Yao Jia, LIAN Jitan, WANG Xin, LV Danyang, DENG Yujie, and Aoqi ZHANG. Reliability analysis of grain combines harvesters based on data mining technology. *INMATEH-Agricultural Engineering*, 67(2), 2022.

[42] Sandro Saitta, Benny Raphael, and Ian FC Smith. Data mining techniques for improving the reliability of system identification. *Advanced Engineering Informatics*, 19(4):289-298, 2005.

[43] Nadhem Sultan Ali and VP Pawar. Data mining techniques are used to improve software reliability. *International Journal of Advanced Research in Computer Science*, 4(2), 2013.

[44] Marcia L Baptista, Elsa MP Henriques, and Helmut Prendinger. Classification prognostics approach in aviation. *Measurement*, 182:109756, 2021.

[45] Rune Prytz, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, and Stefan Byttner. Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. *Engineering applications of artificial intelligence*, 41:139-150, 2015.

[46] Gian Antonio Susto, Sean McLoone, Daniele Pagano, Andrea Schirru, Simone Pampuri, and Alessandro Beghi. Prediction of integral typefailures in semiconductor manufacturing through classification methods. In *2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA)*, pages 1-4. IEEE, 2013.

[47] Josey Mathew, Ming Luo, and Chee Khiang Pang. Regression kernel for prognostics with support vector machines. In *2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)*, pages 1-5. IEEE, 2017.

[48] Samuel Eke, Thomas Aka-Ngnui, Guy Clerc, and Issouf Fofana. Characterization of the operating periods of a power transformer by clustering the dissolved gas data. In *2017 IEEE 11th International Symposium on diagnostics for electrical machines, power electronics and drives (SDEMPED)*, pages 298-303. IEEE, 2017.
Digital Forensics and Security (IS-DFS), pages 1-7. IEEE, 2020.

[36] Jan Zenisek, Florian Holzinger, and Michael Af- fenzeller. Machine learning-based concept drift detection for predictive maintenance. *Computers & Industrial Engineering*, 137:106031, 2019.

[37] Aized Soofi and Arshad Awan. Classification techniques in machine learning: Applications and issues. *Journal of Basic Applied Sciences*, 13:459-465, 08 2017.

[38] Michael W Berry, Azlinah Mohamed, and Bee Wah Yap. *Supervised and unsupervised learning for data science*. Springer, 2019.

[39] P. Govender and V. Sivakumar. Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980-2019). *Atmospheric Pollution Research*, 11(1):40-56, 2020.

[40] Guangpeng Liu and Chong Peng. Research on reliability modeling of CNC systems based on association rule mining. *Procedia Manufacturing*, 11:1162-1169, 2017.

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