bluuebunny/crossref_metadata_2025 huggingface dataset:
Data quality and reliability: a cornerstone for PHM processes
Authors: Leger Jean-Baptiste , Krauth Pierre-Jean , Groussier Guillaume , Monnin Maxime , Mouchette Alain , Lawayeb Fayçal
Published: March 2024

In most of industrial processes, the measurement are central to the process control and quality management. This become even truer when measurement data are used to develop and support PHM strategies. In this context, many software are installed in order to collect data for providing quality assessment at each step of the manufacturing process. However, measurement error or drift are not considered leading to downgrading / rejected products / suboptimal running conditions that comes from measurement drift not detected on time. In concrete, these lead to bigger penalty than losses of production due to stopping time for repairing sensors. Indeed, generally speaking, process data is the “raw material” for many business processes, starting from process control strategy, PHM strategies to Business Intelligence. Thus being able to ensure data quality and reliability is of first importance. Towards this end, methods and tools are required to support online measurement monitoring, predictive diagnosis and reliability enhancement.In this paper, a dedicated approach developed in collaboration with ArcelorMittal Research is presented. It consists in the development of intelligent software that would enable sensor measurement validation taking into account process parameters and operational conditions. An illustrative case study is extracted from an ongoing application developed for the finishing line in ArcelorMittal plant at Florange in France. Results regarding measurement reliability assessment as well as sensor failure anticipation will be described.
Leveraging Next Generation Reasoning for Prognostics and Health Management of the Smart Grid
Authors: Cassar Gilbert , Walker Mark , Yang Yueting
Published: March 2024

With the increasing complexity from an evolving Smart Grid, the significance of providing real-time situational awareness and the ability to leverage advanced reasoning and prediction for control and automation will become key differentiators for service providers. Similar techniques are being applied within prognostics and health management (PHM) applications and are providing value by predicting and assuring system reliability, performing real-time detection and diagnosis of failure, and presenting current and predicted system states to users to aid in decision making. With the overlap in application and requirements for advanced software techniques, the smart grid industry is compelled to investigate products and processes applied to PHM across other domains. However, the complexity of grid management, the speed of technology development, the dynamic nature of electric power supply and demand – each of these contribute to the necessity for applying advanced reasoning capabilities that provide more flexibility to developers and users. Such advanced capabilities allow for leveraging all available information, enabling accurate predictions of future conditions and availability, and incorporating the necessary knowledge for making high level decisions. Object oriented, model-based reasoning systems have demonstrated value within the PHM community for handling such complexity, and in this paper the authors discuss a pragmatic approach for applying these next generation PHM techniques to the smart grid.
Life characterization of power distribution transformers using clustering techniques
Authors: Mulliez Edouard , Sanz-Bobi Miguel A. , Sanchez Alvaro , Mazidi Peyman , Gonzalez Ana , Bachiller Raúl
Published: March 2024

The current development of the smart grids has considerably increased the amount of research studies around new exploitation paradigms focused on the electrical distribution systems. One of the key elements of electrical  distribution networks is the distribution transformer that supports the load that has to feed the consumer needs. This paper aims at characterizing the life of the distribution  transformers using clustering techniques. This will make it possible to focus the attention of the Distribution System Operator on particular groups of distribution transformers reducing the amount of information to be analyzed. Also this classification combined with the study of stress indicators for each distribution transformer can be used to complement the criteria used for the network planning and operation.
A Functional Architecture of Prognostics and Health Management using a Systems Engineering Approach
Authors: Li Rui , Verhagen Wim J.C. , Curran Richard
Published: March 2024

Prognostic and Health Management (PHM) describes a set of capabilities that enable effective and efficient approaches towards data analysis for fault diagnostics and failure prognostics. This can support decision making related to health management, sustainment and operation of critical systems, such as aviation systems. As a result of the rapidly growing interest in PHM, a substantial amount of research proposes and discusses PHM frameworks and system architectures. Many research efforts regarding PHM frameworks focus on technical aspects, such as techniques associated with big data, cloud-based distribution or open facilities. Several papers propose specific PHM system architectures based on different frameworks. In addition, several authors address the derivation of PHM system architectures from system requirements (related to functions, behaviours, structures) using a systems engineering perspective. However, further interpretations of PHM system architecture derived from requirements in functional view are lacking. Moreover, the PHM system operates as a system of systems as it results from the combination of aviation systems, airline operational systems, and other ground health management hosting systems. In both research and industry, these systems are discussed and designed independently, whereas they need to be integrated to offer monitoring functions and services. Research of PHM architecture allowing communication and integration with the various contributing systems is lacking. To address these gaps, this research outlines an architecture design methodology incorporating a functional view from a systems engineering perspective. In addition, it proposes a functional architecture for PHM system as the application of the methodology. This proposed architecture is derived from associated functions and requirements to identify the functional elements, system boundary and the external/internal interactions of PHM system, to ensure the traceability and efficiency of architecture design. It also has compatibility and interoperability to integrate with the various systems, due to its compliance with the standard of Open System Architecture for Condition-Based Maintenance (OSA-CBM). A Case Study is conducted to verify the functional hierarchy and interactions and to demonstrate the compatibility and applicability through a compliance matrix. Furthermore, architecture models are established using System Modeling Language (SysML) through structural diagrams to represent the static structure and interactions. These models also sustain the integration with a variety of other information or management systems that together enable an organization to operate efficiently and competitively using health monitoring services through OSA-CBM.
RailWear Estimation for Predictive Maintenance: a strategic approach
Authors: Meghoe Annemieke , Loendersloot Richard , Bosman Rob , Tinga Tiedo
Published: March 2024

Since the very beginning of rail transport, wear has been identified as one of the dominant damage mechanisms that influence the Remaining Useful Life (RUL) of rail tracks. Whereas maintenance of the track is now predominantly executed at fixed intervals or based on yearly inspections, the accurate prediction of rail wear could considerably improve the maintenance process. The present work proposes a method for long-term rail wear prediction using measurements of actual rail and wheel profiles as starting point. By doing so, the computational expensive step of updating the rail profile in a wear calculation, as is done in presently used methods, can be omitted. The proposed method is used to study a number of generic trends, varying curve radius and rail or wheel profile. Further, the method is validated against measured wear on actual track sections for moderate curves. Finally, it can easily be extended to include variations in operational usage of the track (type / weight of trains, geometric details, slip conditions) in the future. The method presented in this paper can therefore assist in improving the track maintenance process by maximizing the utilization of the track service life, and minimizing maintenance costs.
Denoising autoencoder anomaly detection for correlated data
Authors: Goldthorpe Peter James , Desmet Antoine
Published: March 2024

In the mining industry, primary digging units such as wheel loaders are critical components due to their position at the start of the process chain. Consequently, the cost of unexpected downtime is high: this motivates efforts to provide an early warning of faults using remote diagnostics.
Machines are equipped with sensors that measure machine health. Some sensors are highly correlated and a model based on machine learning techniques can leverage such relationships across sensors to detect within-group abnormalities.
Autoencoders are auto-associative artificial neural networks which are trained to compress and rebuild the original input with minimal loss. The information is stored in the lower dimensional hidden layers as an internal coding. This is susceptible to a phenomenon called spillover, where the error in a single input can propagate through the network, corrupting the coding and biasing the entire reconstructed data. A denoising autoencoder is a more robust variation on the traditional autoencoder, trained to remove noise and build an error-free reconstruction.
We created a denoising autoencoder to utilize the noise removal on corrupted inputs, and rebuild from working inputs. While this technique is novel to this problem it remained susceptible to spillover. We show our findings and discuss future anomaly detection techniques in correlated data.
Power Consumption Forecast using Nonlinear Regression Methodologies
Authors: Sturm Benedikt , Jaroszewski Daniel
Published: March 2024

In this paper, we present an innovating method to forecast time series. We focus on a long-term forecasting model dealing with main periodicities in addition to short term effects. The consolidation of Fourier Transformations, which covers the basic oscillation of a time series, with machine learning algorithms approximating the error term, is at the heart of our forecasting model. Later on, we compare different regressions such as kernel and logistic regression, a combinatorial technique on sparse grids and finally a special representative of neural networks. Thereby we are able to forecast power consumption in certain locations of a given network and we show the results of those forecasts as functions of various inputs. The results presented are used for power demand planning of cities and are consequently prognostic in nature. In the context of Health Management, however, one usually works with anomaly detection and supervised learning methods. Nevertheless, a time series forecast in neighboring applications, e.g. the power consumption of a traction system in railway vehicles, could substantially benefit from these prognosis functionalities. This also means that deviations of physical quantities measured under real-time conditions from their expected behavior indicate a likely prevailing malfunction.
Assessment of Maintenance Policies for Smart Buildings
Authors: Abate Alessandro , Budde Carlos Esteban , Cauchi Nathalie , Hoque Khaza Anuarul , Stoelinga Mariëlle
Published: March 2024

Cyber-physical systems must meet high RAMS—reliability, availability, maintainability, and safety—standards. It is of essence to implement robust maintenance policies that decrease system downtime in a cost-effective way. Power plants and smart buildings are prominent examples where the cost of periodic inspections is high, and should be mitigated without compromising system reliability and availability. Fault Maintenance Trees (FMTs), a novel extension in fault tree analysis, can be used to assess system resilience: FMTs allow reasoning about failures in the presence of maintenance strategies, by encoding fault modes in a comprehensible and "maintenance-friendly" manner. A main concern is how to build a concrete model from the FMT, in order to compute the relevant RAMS metrics via (ideally automatic) analyses. Formal methods offer automated and trustworthy techniques to tackle with such task. In this work, we apply quantitative model checking—a well established formal verification technique—to analyse the FMT of a Heating, Ventilation and Air-Conditioning unit from a smart building. More specifically, we model the FMT in terms of continuous-time Markovchains and priced time automata, which we respectively analyse using probabilistic and statistical model checking. In this way we are capable of automatically estimating the reliability, availability, expected number of failures, and differentiated costs of the FMT model for various time horizons and maintenance policies. We further contrast the two approaches we use, and identify their advantages and drawbacks.
Near real-time monitor of rail way track adhesion conditions
Authors: Locht Inka Laura Marie , Oosterhof Nikolaas Nuttert
Published: March 2024

In this paper we examine the possibilities of using sensor data from trains in service to develop a real-time monitor of the adhesion conditions of the rail. In everyday railway operations, low adhesion conditions of the track are an important challenge for railway operators, since these may result in a loss of punctuality, an increase in wear of both wheel and rail, and in an increase of the risk for red signal passage in situations where trains are unable to stop in time. At the same time, prudent driving behavior while the adhesion conditions returned to normal, results in unnecessary train delays. A central issue here is that rail adhesion conditions vary across space and time. To date it is a challenge to give accurate real-time information to both train drivers and infrastructure managers. With real-time monitoring of the adhesion conditions, the drivers can adjust their (de)acceleration control to local adhesion conditions and thereby minimize wear, and the infrastructure managers could improve the track conditions by taking friction enhancing measures.
 
To monitor the adhesion conditions of the track, we used real-time sensor data from ~20 trains. We designed an algorithm that can diagnose track sections as having either slippery or normal adhesion conditions. Specifically, we trained a logistic classifier on a data set that contained reported rail adhesion conditions as well as sensor data from trains in service, such as information about traction, velocity, excessive wheel slip-/sliding detection, and weight. We then assessed the performance of this classifier using an independent test data set.
 
This first assessment shows a classification accuracy of approximately 77%, with a ~23% false positive rate and a ~23% false negative rate, when compared to the drivers reporting. Several improvements are proposed to increase the sensitivity, which outline the directions of our future research towards the implementation of a real-time monitor of railway track adhesion conditions.
Lifetime prediction of wind turbine blades using degradation model considering variable load profiles
Authors: Jihin Rosmawati , Do Manh-Hung , S¨offker Dirk
Published: March 2024

Assessment of fatigue damage, classification of system state, prediction of a remaining life as well as the extension of maintenance intervals become a challenge in structural health monitoring of wind turbines. Almost all wind turbine parts are subjected to different load combinations due to the variation of wind profiles. The dynamic complexity of wind profiles causes limited ability to define failure thresholds, as well as estimating current health status of the system. Due to numerous factors affecting system operation it is hardly possibly to define solutions calculating degradation and related remaining useful lifetime. Various possible degradation scenarios could be arisen due to a variety of circumstances. Instead of using analytical models, in this contribution numerical (data driven) models with the capability to handle such scenarios and providing more effective degradation prediction are used. This allows an increased accuracy for  estimating lifetime. A newly introduced state machine-based prognostic model is used to enable flexibility in deterioration modeling while concerningthe relation between load and system lifetime. In addition to the previous development, here a suitable collection of different loads and wear-dependent basic degradation processes are defined to identify the load-lifetime relation. Using core wear units, the real load time series is composed of this units allowing to learn about the effective load-lifetime relation, which is used for training of the state machine lifetime model. To observe model applicability, wind turbine blademoments time series data are used. Then, damage degradation data for various power-dynamic relations generated using a reference software to train this model and work as reference datasets. Results demonstrate the strong potential of the proposed approach for wind turbine lifetime prediction.
Prognosis of Lithium-Ion Batteries Considering Cycle and Storage Conditions
Authors: Lee Soyoung , Doh Jaehyeok , Lee Jongsoo
Published: March 2024

Lithium-ion batteries are used as a major power source for electric vehicles (EV) due to their high energy density. When the battery is repeatedly charged and discharged during the operation of the vehicle, the capacity is gradually deteriorated, which is divided into cycle degradation and storage degradation. In order to predict the accurate life of the battery, combination of the two degradations must be considered reflecting the actual usage conditions. However, composite (i.e., both cycle and storage conditions) tests take relatively longer than the tests for each conditions. In this study, we proposed a method for predicting the remaining useful life (RUL) of a complex situation using the results of respective tests on cycle and storage conditions, which require relatively short test times. Based on the results of respective tests, the model-based approach using particle filter is applied to predict RUL of a lithium-ion battery, which was derived as a probability distribution. By modeling the joint probability distribution using copula, RUL of composite condition considering both cycle and storage degradations. It was verified by comparison with the distribution of the RUL derived using the composite tests results.
Sensor positioning and thermal model for condition monitoring of pressure gas reservoirs in vehicles
Authors: Bücking Henrik , Pfaff Raphael , Dirksmeier Roger
Published: March 2024

Passively acting safety relevant systems, e.g. automatic fire extinguishing systems or brakes, rely on the availability of compressed gases as acting medium and energy storage. Due to the safety relevance of these systems, it is necessary to monitor the condition of these elements continuously. However this task is difficult due to the partly abrupt changes in environmental temperature due to tunnel crossings and the interrelation of temperature and pressure in gases. As further the direct measurement of the gas temperature is difficult and costly, it is desirable to use external sensors to estimate the average gas temperature while at the same time avoiding false positives as not to reduce availability of the subsystems.
For this reason, the present paper analyses the behavior of a cylindrical pressure reservoir during changes in the environment temperature. The gas under consideration is Nitrogen under a pressure of approximately $200\,\mathrm{bar}$ at $15\,^{\circ}\mathrm{C}$. Aiming to identify a dynamical model of the gas temperature, different temperature profiles were simulated while measuring gas-pressure and temperatures in two locations within the gas reservoir as well as on the cylinder wall. From the recorded data, a dynamical model is identified which expresses the relation between environmental and mean gas temperature. The estimated gas temperature from this system model is used to determine a reference pressure which can be compared to the observed pressure. In case of any mass flow from the reservoir, the error grows and an error can be triggered.
The model was developed using temperature curves resembling true operational curves simulated in a climatic chamber. The detection and classification behaviour was tested using computer simulations.
Characterizing the Degradation Process of Lithium-ion Batteries Using a Similarity-Based-Modeling Approach
Authors: Perez Aramis , Jaramillo Francisco , Quintero Vanessa , Orchard Marcos
Published: March 2024

This article proposes a Similarity-Based-Modeling (SBM) approach capable of characterizing the degradation process of a lithium-ion (Li-ion) battery when discharged under different current rates and different State-of-Charge (SOC) ranges. The degradation process can be represented through a biexponential model. In this regard, it is possible to determine the equivalent cycle-by-cycle efﬁciency which has low values at the beginning of the degradation process until it reaches a higher and steady value. The lifespan of the batteries is analyzed through the use of Monte Carlo simulations which intends to represent a more realistic way of how the batteries are used.
Investigating Novel Approaches to Overcome Data Deficiency in Prognostics and Health Management
Authors: Kim Seokgoo , An Dawn , Kim Nam Ho , Choi Joo-Ho
Published: March 2024

Recently, the prognostics and health management (PHM) is becoming more important in many industries since it enhances reliability and efficiency during the operation. Among the several issues in the current PHM research, it may be of no doubt that the data deficiency of field data may be one of the most challenges, making the PHM applications difficult in the real industry. Performance of prognostics requires large amount of run-to-fail data, which is however not easy due to the high cost or inability to run until failure under real operation. This is overcome by exploiting two resources in terms of data: 1) accelerated life data at laboratory 2) real data with suspension at field. In this study, a new algorithm is developed that combines these two data sources for better prognostics.
A multi-task deep learning model for rolling element-bearing diagnostics
Authors: Hinchi Ahmed Zakariae
Published: March 2024

The rolling element bearing is the predominant source of failures in rotating machinery. Therefore, detecting the corresponding faults, predicting their locations and measuring their severity is of immense importance. Classical intelligent diagnostics approaches rely on feature extraction methods followed by a classification model. Recently, deep learning models improved the fault classification accuracy by learning a suitable representation directly from the raw sensor data. In this work, we present a novel multi-task deep convolutional neural network trained end-to-end on raw vibration data to learn a shared representation for fault isolation and fault size evaluation. The proposed model architecture is constructed by stacking blocks of convolution layers, pooling layers, and batch normalization layers followed by a regression head and a classification head. Extensive experiments show that the proposed approach produces a superior performance to other existing methods and generalizes well to fault sizes not present in the training set.
Determining the Causes of Train Delays - An Automatic Fuzzy Matching Approach
Authors: Lee Wan-Jui
Published: March 2024

In order to manage the health of assets, knowledge of conditions on the system level is often required. One of the most common approaches to determine the system conditions is to check the frequency of unplanned or corrective maintenance of considered systems. However unplanned or  corrective maintenance usually means the issue has reached an intolerable degree and therefore leaves very limited room to improve the health condition of assets. To depart from the conventional approach of asset health management based on unplanned maintenance, Dutch Railways has decided to focus on operational disturbances, i.e., train delays, due to technical issues of trains in order to determine which system deterioration to investigate at the early stage.
This work introduces the framework of the cause determination system for train delays that has been implemented within Dutch Railways. The cause determination is composed of two parts. One is an automatic fuzzy matching system that will match a delay to the most probable service request assigned by the support center. The causes of 40% of delays can be automatically identified by this method with an accuracy of 95%. This significantly reduces the human hours spent in identifying delay causes. The rest of cause determination is manually carried out by the Delay Analysis by Calling project.
In this project, the calling team will first call train drivers to ask and record the encountered issues and handling procedures, and then the reliability engineers will determine the causes of delays based on these feedbacks. The calling team therefore serves as a feedback channel for drivers and support center and enables us to analyze on the delay handling and advice given by the support center for continuous improvement and development. Another advantage of integrating a highly accurate and robust fuzzy matching system into an interactive cause identification framework that requires input from various business units is that it intrigues interests and builds up trust in data science technology within the organization. This helps us to smoothly introduce this culture change which often is a critical point in transforming into a data-driven organization, especially for the maintenance industry.
Based on the identified delay causes, Dutch Railways has built a Delay Analysis Dashboard which can provide a good overview of system conditions for various fleets, and also provide more possibilities to avoid operational disturbances.
Data-driven condition now- and forecasting of railway switches for
Authors: Narezo Guzman Daniela , Hadzic Edin , Schuil Robert , Baars Eric , Groos Jörn Christoffer
Published: March 2024

This contribution presents a data-based model that exploitsthe power consumed by point engines during bladesmovement of railway switches to detect relevant anomaliesin switch behavior. The model incorporates local airtemperature at the time of the measurement to account forthe significant influence of the environmental conditions onnormal switch behavior. Anomaly detection by the model isvalidated against alerts triggered by the state-of-the-artmonitoring system POSS®, which is based on switchspecific and manually selected reference curves. The databased model leads to less in number and more reliable alertsin comparison to the current version of POSS®. Especiallyfalse alerts caused by temperature effects are significantlyreduced. Furthermore, the high sensitivity of the modelproves to be capable of detecting emerging switch failures atan early stage of development. The detection capabilities ofswitch condition (nowcast) and identification of emergingfailures at an early stage (required for failure forecast)proves that the model is useful for traffic interferenceprevention, condition-based predictive maintenance andswitch health enhancement.
Effects of operating condition variations on vibration-based bearing condition monitoring features: Experimental investigation
Authors: Ompusunggu Agusmian Partogi , Devos Steven
Published: March 2024

The envelope spectrum analysis on vibration signals has been demonstrated for many years as a powerful technique to extract effective indicators (features) for condition monitoring of rolling element bearings under stationary operating conditions. As will be shown in this paper, applying these diagnostic features without a care to rotating machines subject to varying operating conditions may lead to unreliable bearing fault diagnostics. Hence, the applicability of the diagnostic features is rather limited. To extend the applicability of the diagnostic features, number of researchers/research institutes have developed new features/methods to deal with variation of operating conditions. However, the methods proposed in the literature mainly focuses on shaft speed variations. In practice, both shaft speed and load can vary in time simultaneously so there might be an interaction effect on the diagnostic features responses. Moreover, the bearing temperature, being an operational parameter, may also vary significantly in some applications. Because the temperature affects the film thickness formed between the rolling elements and the raceways of bearings, which in turn contributes to the damping and contact stiffness of bearings, it is believed that the bearing temperature might also play an important role on the diagnostic features responses. To improve our understanding on how the three operational parameters variations, namely speed, load and temperature, affect the diagnostic feature responses, a set of experiments has been designed to investigate the effects using an in-house developed test setup. The main effects of the operational parameters on the diagnostic features are presented and discussed in this paper. Qualitative models describing the relationships between the diagnostic features and the operational parameters are deduced and discussed in the paper. It is believed that these qualitative models can be useful to inspire us in the future to develop methods/strategies for bearing fault diagnostics under varying rotational speed, load and temperature.
Investigation of Intermittent Failure in Electrical Interconnections of Avionics System
Authors: Han Changwoon
Published: March 2024

If a failure of a function is reported for a limited period of time and subsequent recovery of the function in a product, it’s called intermittent failure. The intermittent failure is one of the causes of no-fault-found (NFF). It was reported that electrical interconnections are one of the most detrimental sources for the intermittent failure of electronic devices in vibration environment. In this study, intermittent failure in electrical interconnections is investigated. Printed circuit board (PCB) and flexible PCB (FPCB) are designed with several types of electrical interconnections. Electronic devices are also developed to measure the intermittent failure of the interconnections. Vibration and thermal cycling tests are conducted for the interconnections and intermittent failures are monitored in real-time. The failure analysis for the site of intermittent failure occurrences reveals the cause of the failure
Anomaly Detection Indicators of a Wind Turbine Gearbox Based on Feature Extraction from its Vibration Performance
Authors: Martinez Montaña Miguel , Sanz-Bobi Miguel A.
Published: March 2024

This paper proposes a method for obtaining several health condition indicators for wind turbines based on vibration data driven from two similar experimental turbines (damaged and healthy). These indicators are able to capture the bearing and gear condition of the gearbox in the wind turbines. Signal processing and feature extraction were carried out –on both the time and frequency domains– from raw data in order to generate datasets for each shaft of power of the wind turbines. Based on good health condition data, a data mining approach was used to build two reference models for the indicators, one using Self-Organizing Maps (SOM) and another one using Gaussian Mixture Models (GMM). These reference patterns for the indicators were tested with a dataset coming from a damaged wind turbine and the results obtained confirmed the adequacy of these indicators to detect anomalies in the health condition of a wind turbine.
Unsupervised Domain Adaptation based Remaining Useful Life Prediction of Rolling Element Bearings
Authors: Liu Chenyu , Gryllias Konstantinos
Published: March 2024

With the rise of Artificial Intelligence (AI), machine learning techniques are now conquering the research field of Prognostics and Health Management (PHM). Classic deployable prognostic models manipulate large amount of machinery historical data to map the degradation process based on inherent features. Nowadays one of the major challenges in prognostics research is the data deficit problem when historical data is not available or accessible, in enough quantity and variety. In the frames of Transfer Learning, the domain adaptation technique aims to build a model with strong generalization ability which can be transferred to datasets with different distributions. In this paper, a Domain Adversarial Neural Network (DANN) model is combined with a Bidirectional Long Short- Term Memory (Bi-LSTM) neural network for the estimation of the Remaining Useful Life (RUL) of rolling element bearings. The unsupervised domain adaptation is fulfilled using a labelled bearing degradation dataset as the source domain data and an unlabelled dataset captured under different operation conditions as the target domain data for the Bi-LSTM DANN. The proposed method achieves promising results, applied on real bearing vibration data captured on run-to-failure tests, with high prediction accuracy of the bearing RUL compared to un-adapted methods.
Contribution to Online System-level Prognostics based on Adaptive Degradation Models
Authors: Tamssaouet Ferhat , Nguyen Khanh T. P. , Medjaher Kamal , Orchard Marcos
Published: March 2024

Considering traditional model-based prognostics approaches, a previously defined model is required to estimate the system’s health state and then propagate it to predict the system remaining useful life (SRUL). Following a Bayesian framework, the result of this prior estimation is updated by in-field measurements without changing the model parameters. Nevertheless, in the case of prognostics at system-level, solely updating prior health state, based on the pre-determined model, is no longer sufficient because numerous mutual interactions between components cause multiple uncertainties in system degradation modeling, and then can lead to inaccurate SRUL prediction. Therefore, this paper proposes a new methodology for online joint uncertainty quantification and model estimation based on particle filtering (PF) and gradient descent (GD). In detail, the inoperability input-output model (IIM) is used to characterize system degradations considering interactions between components and effects of the mission profile; and then the inoperability of system components is estimated in a probabilistic manner using PF. In the case of consecutive discrepancy between the prior and posterior estimates of the system health state, GD is used to correct and to adapt the IIM parameters. To illustrate the effectiveness of the proposed methodology and its suitability for an online implementation, the Tennessee Eastman Process is investigated as a case study.
Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data
Authors: Aimiyekagbon Osarenren K. , Bender Amelie , Sextro Walter
Published: March 2024

In all fields, the significance of a reliable and accurate predictive model is almost unquantifiable. With deep domain knowledge, models derived from first principles typically outperforms other models in terms of reliability and accuracy. When it may become a cumbersome or an unachievable task to build or validate such models of complex (non-linear) systems, machine learning techniques are employed to build predictive models. However, the accuracy of such techniques is not only dependent on the hyper-parameters of the chosen algorithm, but also on the amount and quality of data. This paper investigates the application of classical time series forecasting approaches for the reliable prognostics of technical systems, where black box machine learning techniques might not successfully be employed given insufficient amount of data and where first principles models are infeasible due to lack of domain specific data. Forecasting by analogy, forecasting by analytical function fitting, an exponential smoothing forecasting method and the long short-term memory (LSTM) are evaluated and compared against the ground truth data. As a case study, the methods are applied to predict future crack lengths of riveted aluminium plates under cyclic loading. The performance of the predictive models is evaluated based on error metrics leading to a proposal of when to apply which forecasting approach.
A Dual-Stream architecture based on Neural Turing Machine and Attention for the Remaining Useful Life Estimation problem
Authors: Falcon Alex , D'Agostino Giovanni , Serra Giuseppe , Brajnik Giorgio , Tasso Carlo
Published: March 2024

Estimating in a reliable way the Remaining Useful Life (RUL) of a mechanical component is a fundamental task in the field of Prognostics and Health Management (PHM). In recent years a greater availability of high quality sensors and easiness of data gathering gave rise to data-driven models based on deep learning for this task, which has recently seen the introduction of "dual-stream" architectures. In this paper we propose a dual-stream architecture to address the RUL estimation problem through the exploitation of a Neural Turing Machine (NTM) and a Multi-Head Attention (MHA) mechanism. The NTM is a content-based memory addressing system which gives each of the streams the ability to access to and interact with the memory and acts as a fusion technique. The MHA is an attention mechanism added as a mean for our architecture to identify the existing relations between different sensor data in order to reveal hidden patterns among them. To evaluate the performance of our model, we considered the C-MAPSS dataset, a benchmark dataset published by NASA consisting of several time series related to the life of turbofan engines. We show that our approach achieves the best prediction score (which measures the safety of the predictions) in the available literature on two of the C-MAPSS subdatasets.
Proposal of a Technique for Correlating Defect Dimensions to Vibration Amplitude in Bearing Monitoring
Authors: Brusa Eugenio , Bruzzone Fabio , Delprete Cristiana , Di Maggio Luigi , Rosso Carlo
Published: March 2024

The capability of early stage detection of a defect is gaining more and more importance because it can help the maintenance process, the cost reduction and the reliability of the systems.

The increment of vibration amplitude is a well-known method for evaluating the damage of a component, but it is sometimes difficult to understand the exact level of damage. In other words, the amplitude of vibration cannot be directly connected to the dimension of the defect.

In the present paper, based on a non-Hertzian contact algorithm, the spectrum of the pressure distribution in the contact surface between the race and the rolling element is evaluated. Such spectrum is then compared with the acquired spectrum of a vibration response of a defected bearing. The bearing vibration pattern was previously analyzed with monitoring techniques to extract all the damage information.

The correlation between the spectrum of the pressure distribution in the defected contact surface and the analyzed spectrum of the damaged bearing highlights a strict relationship. By using that analysis, a precise correlation between defect aspect and dimension and vibration level can be addressed to estimate the level of damaging.
On the use of PCA for Diagnostics via Novelty Detection: interpretation, practical application notes and recommendation for use
Authors: Daga Alessandro Paolo , Fasana Alessandro , Garibaldi Luigi , Marchesiello Stefano
Published: March 2024

The Principal Component Analysis (PCA) is the simplest eigenvector-based multivariate data analysis tool and dates back to 1901 when Karl Pearson proposed it as a way for finding the best fitting d-1 hyperplane of a system of points in a d-dimensional (Euclidean) space.

Over the time, the PCA evolved in different fields with several different names and with different scopes, but, in its essence, it is always an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

Generalizing Pearson’s purpose, the knowledge derived by such an analysis is mostly used to find a subspace which effectively and efficiently summarizes the original system of points by losing a minimum amount of information.

In the field of Diagnostics, the fundamental task of detecting damage is basically a binary classification problem which is in many cases tackled via Novelty Detection: an observation is classified as novel if it differs significantly from other observations. Novelty can, in principle, be assessed directly in the original space, but the effectiveness of the estimated novelty can be improved by taking advantage of the PCA.

In this work, the traditional PCA will be compared to a robust modification that is commonly used in the field of diagnostics to face the issue of confounding influences which could affect the novelty-damage correspondence. Comparisons will be made to shed light on the main misleading aspects of PCA, and finally, define a unique, theoretically justified procedure for Diagnostics via Novelty Detection.
Fault Diagnosis and Prognosis of a Brushless DC motor using a Model-based Approach
Authors: Blesa Joaquim , Quevedo Joseba , Puig Vicenç , Nejjari Fatiha , Zaragoza Raul , Rolán Alejandro
Published: March 2024

This paper proposes a model-based fault diagnosis and prognosis approach applied to brushless DC motors (BLDC). The objective is an early detection of mechanical and electrical faults in BLDC motors operating under a variety of operating conditions. The proposed model-based method is based on the evaluation of a set of residuals that are computed taking into account analytical redundancy relations. Fault diagnosis consist of two steps: First, checking if at least one of the residuals is inconsistent with the normal operation of the system. And, second, evaluating the set of the residuals that are inconsistent to determine which fault is present in the system. Fault prognosis consists of the same two steps but instead of considering current inconsistencies evaluates drift deviations from nominal operation to predict futures residual inconsistencies and therefore predict future fault detections and diagnosis. A description of various kinds of mechanical and electrical faults that can occur in a BLDC motor is presented. The performance of the proposed method is illustrated through simulation experiments.
Model-based Prognosis Approach using a Zonotopic Kalman Filter with Application to a Wind Turbine
Authors: Khoury Boutros , Puig Vicenç , Nejjari Fatiha
Published: March 2024

Wind turbines generally operate under adverse conditions making them prone to relatively high failure rates. Due to the direct exposure of the blades to dynamic and cyclic loads of wind, the rotor and the blades unsurprisingly represent the most common major component damages of a wind turbine system, which is especially enhanced when located offshore.

This paper presents a new model-based prognosis procedure based on a zonotopic Kalman filter (ZKF), which combines a physical model with observed data to assess the system degradation. Using this information and the model of the system, the end of life (EOL) and the remaining useful life (RUL) with its uncertainty can be predicted. The proposed prognostic method is applied to monitor the state of health of a wind turbine system specifically, its blades. The remaining useful life prediction will help in scheduling optimal maintenance and reducing the cost caused by wind turbine damage and unplanned shutdown.
Optimized Maintenance Decision-Making – A Simulation-supported Prescriptive Analytics Approach based on Probabilistic Cost-Benefit Analysis
Authors: Koops Lily Geraldine
Published: March 2024

Prescriptive Maintenance strategies are emerging as potential next level of reliability and maintenance best practice. Likely outcomes of maintenance alternatives and their effects on e.g. cost and safety are comparatively evaluated by exploiting various sources of data, knowledge and models. By this means, optimized courses of actions are recommended to quickly resolve problems and to automate Maintenance, Repair and Overhaul (MRO) decisions. In this work, the key question is pursued as to how their dependability and potential business advantage can be assessed and improved in the presence of uncertainty and variability of various decision-influencing factors such as degradation and maintenance model parameters and cost sources. For this purpose, a step-by-step procedure to optimal solution prescription and potential / risk assessment is developed based on a probabilistic approach to cost-benefit analysis and on the definition of relevant metrics. By the help of a Wiener process degradation model capable of implementing random effects of imperfect repairs and a Monte Carlo simulation, its value is illustrated by a use case example – repair / replacement decision support in the aeronautical context. The probabilistic approach not only allows to determine, which decision option promises the higher profit and is thus preferred, but also with which risk and potential cost disadvantage it is associated. Furthermore, it uncovers, where higher-quality data or information, can gainfully reduce result uncertainty and hence be assigned a monetary value. It is argued that the presented approach could give industry practitioners directions for identifying and optimizing business cases for Prescriptive Maintenance, by pointing at which sources of data or information are particularly valuable and hence justify dedicated investments for acquiring it. The relevance of the results is discussed specifically with reference to emerging digitized and automated repair processes as well as more generally in the context of future data-trading schemes.
Supervised Learning Using Quantum Technology
Authors: Jaroszewski Daniel , Sturm Benedikt , Mergenthaler Wolfgang , Steffen Kay , Linke Patrick , Regel Michael , Haack Björn
Published: March 2024

In this paper, we present classical machine learning algorithms enhanced by quantum technology to classify a data set. The data set contains binary input variables and binary output variables. The goal is to extend classical approaches such as neural networks by using quantum machine learning principles. Classical algorithms struggle as the dimensionality of the feature space increases. We examine the usage of quantum technologies to speed up these classical algorithms and to introduce the new quantum paradigm into machine diagnostic domain. Most of the prognosis models based on binary or multi-valued classification have become increasingly complex throughout the recent past. Starting with a short introduction into quantum computing, we will present an approach of combining quantum computing and classical machine learning to the reader. Further, we show different implementations of quantum classification algorithms. Finally, the algorithms presented are applied to a data set. The results of the implementations are shown and discussed in the last section.