# State Diagnosis and Monitoring Method of Robot Electric Power Equipment Based on Data Mining

Authors: Haifeng Guo; Zhaojun Meng; Huimin Yu; Rui Chen; Ling Li; Li Cheng
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

In recent years, with the continuous improvement of system networking, the use of robot electric power equipment is becoming more and more extensive, so it is very necessary to diagnose and monitor its status. Therefore, a diagnosis and monitoring system based on the data mining algorithm is constructed. This study mainly uses the gray prediction algorithm and discrete prediction algorithm; the results showed that after the combination of gray prediction has a certain degree of increase, through the analysis, we can draw the reason for this is that gray prediction algorithm to check the failure data is no longer as input data in the detection of outliers, thereby reducing the noise of the data set, so that the perfomance of oulier detection algorithm can ahieve a great progress. In terms of the running time of outlier detection, the running time of outlier detection using K-Mean algorithm and DBSCAN algorithm increases to a certain extent because the algorithm combines gray prediction and increases the algorithm process.


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## Body

## 1. Introduction

In recent years, with the continuous improvement of the system network degree and the rapid development of the field real-time data storage and communication technology, the data volume in the system database is increasing. However, at this stage, one can only have a very limited use and analysis of these data, such as basic data query or device working state statistics. This phenomenon, in which a large portion of the system’s data are underutilized, is often described as data-rich, but information-poor. In fact, behind a large number of historical data, a lot of important information is often hidden. It is not until system decision does the comprehensive analysis of the information behind the data that historical data can be converted to directly used knowledge, which makes people urgently need an approach to dig out huge amounts of hidden knowledge of historical data. In this context, data mining (DataMining, DM) technology gradually developed, which is a new tool generated from information science and technology, whose development and the development of a new generation of information systems such as data warehouse are inseparable [1].Data mining is the discovery of unknown and potential application patterns or information from its own and large number of data (data warehouse or database). See Figure1.Figure 1
Process of finding the basic knowledge.In general, the basic process of human discovery of knowledge can be divided into the following steps:(1)
Observe and understand the objective world through various means such as people’s own feelings, and sensors, and collect the original data from the objective world, sort out, archive them, and input them into the database.(2)
Change the collected raw data, remove noise and a series of data preprocessing work.(3)
Further refine the preprocessed data and screen out the useful information for people.(4)
After in-depth research and analysis of string information, summarize and extract more useful information, which can help human beings to understand the mode of the operation of objective things, namely, knowledge(5)
From the accumulated large amount of knowledge, summarize the principles and laws that can guide people’s action and form wisdom.(6)
Using wisdom, engaged in productive labor, human beings can change the objective world according to their own will.In the process of the above knowledge discovery, data mining plays an important role in steps (2) to (4), mining useful knowledge that can be understood from a large amount of incomplete data. Need is the mother of invention. In recent years, data mining has been boosting at home and abroad and has been widely used in scientific research, government project management, enterprise financial decision-making, industrial production line production, Chinese and western medicine, and other fields. In terms of machine learning and artificial intelligence, the data mining technology that comprehensively uses statistics, pattern recognition, visualization technology, database, numerical analysis, and other subject knowledge has also become a research hot spot [2].With the development of fault diagnosis technology, various fault diagnosis methods can be roughly divided into large categories of mathematical model-based methods, artificial intelligence-based methods, and data-driven methods.The diagnostic idea of the mathematical model-based method is diagnosed through the analysis of the residual sequence, but the precise mathematical model of the diagnosed system must be established first. According to the different modes of residual production, it can be divided into state estimation-based diagnostic method, parameter estimation-based diagnostic method, and equivalent space method [3]. Among them, the method based on parameter estimation needs to find a one-to-one correspondence between model parameters and physical parameters to facilitate the separation of faults. Methods for linear parameter estimation can be implemented using well-established parameter identification techniques such as least squares and a Kalman filter. However, the most studied parameter estimation methods for nonlinear systems are the strong tracking filter method [4]. Its biggest feature is that using a strong tracking filter to estimate the model parameters is robust to the model uncertainty. See Figure 2.Figure 2
Schematic diagram of the fault diagnosis based on the mathematical model.Artificial intelligence-based method is a common fault diagnosis method based on nonanalytical model which includes expert system-based diagnostic method, neural network-based diagnostic method, fuzzy logic-based diagnostic method, pattern recognition-based diagnostic method, and graph theory-based diagnostic method. Expert application in the medical diagnostic system has been extended to electric power equipment maintenance and diagnosis, industry, agriculture, business, and other industries. The diagnostic method based on the neural network is divided into two categories. One is the neural network as the output estimator and replaces the traditional observer; the other is the neural network for classification and pattern recognition [5].


## 2. Literature Review

In fault diagnosis with a modern, networking, integrated, and electric power equipment complicated system, the system of real-time running state data is very large and inevitably contains noise, incomplete and isolated points, etc. The traditional fault diagnosis method is difficult to meet the requirements of the system fault diagnosis speed, and effectiveness, where electric power equipment monitoring and knowledge acquisition has bottleneck problems. Generally, the composition structure of data mining-based fault diagnostic systems is shown in Figure3.Figure 3
Schematic diagram of the fault diagnosis system based on data mining.The application of data mining technology in fault diagnosis belongs to a knowledge-based intelligent fault diagnosis method developed in the late 1980s. By using the powerful knowledge acquisition ability unique to data mining technology, it can effectively solve the problems such as the difficulty to establish the traditional expert diagnosis system model, the effective use of failure history data, the failure of the system, and the failure detection system. At the same time, to a certain extent, the use of data mining technology can improve the lack of self-learning ability in the fault diagnosis method and that the knowledge base cannot be updated and so on [6].Cheng et al. CASSIOPEE mass quantity control system is based on data mining technology. The main application of this system is the fault diagnosis and fault prediction of the Boeing 737 aircraft, which achieves satisfactory fault diagnosis results in some practical projects and brings a considerable economic benefit [7]. According to the intelligent fault diagnosis problem of steam turbine, a TIGE civilian diagnosis system based on data mining technology is developed by Zhang et al. and the actual application effect meets the requirements of steam turbine system diagnosis [8]. ROSETTA, a data mining toolkit based on the coarse set theory, was developed to summarize and extract specific discriminant rules for each fault from many vibration signal characteristic parameters by Lv et al [9]. The KATE data mining software by Zhang et al. was developed to mine implicit knowledge from large amounts of data through inductive methods and automatically generate decision trees to provide strong support for electric power equipment fault diagnosis decisions. The system has now been used in a Boeing aircraft manufacturing [10]. After Afrash et al. applied data mining technology to loans and other business monitoring, the company’s credit card utilization rate increased by 10%–15% [11].In China, due to the late start of data mining technology research, the research mainly focuses on theoretical and method discussion and simulation verification; thus, there are practical applications in only a few fields. In recent years, many scientific research institutions and high research institutes attach great importance to the research of data mining methods. Relevant studies in China are as follows:Li et al. has studied the improvement direction of decision tree algorithm, analyzed the shortcomings of feature discretization, improved the Fayyad boundary point decision theorem, and studied the sample incremental learning problem of decision tree [12]. Zhou et al. designed a comprehensive diagnosis method based on simulation which is designed to form the fault detection and diagnosis process, which can improve the speed and accuracy and effectively improve the automation and intelligence of system fault diagnosis [13]. Zhou et al. introduced time series data mining technology into spacecraft telemetry data analysis and processing, system state feature extraction, fault diagnosis and identification, to promote the development of spacecraft fault diagnosis technology, improve the reliability and safety of satellite orbit operation, and extend the service life of satellite, which has great significance, which also proves that data mining in the field of spacecraft fault diagnosis has broad application prospects [14]. For association rule algorithm, Jiang et al. combined with the fuzzy clustering method and made it possible that association rule algorithm can not only mine Boolean attribute rules but can also be extended to the field of mining numerical properties [15]. For the decision tree algorithm, an improved algorithm combined with the ant colony algorithm is proposed to fundamentally improve the efficiency of the decision tree. Zhang taking full-attitude combined top TQZ-1A as the research object and using Clementine data mining tool and CRISP-DM industry standard, constructed, and improved C5.0 classification model based on two-stage clustering. It verifies the good predictability of the model evaluation index [16].


## 3. Methods

### 3.1. Gray Prediction Algorithm

The first-order linear gray model Gm (1, 1) is the basic prediction model of gray system theory. It takes differential fitting as the kernel and finds out the change law between the data according to the data characteristics of the system. The Gm (l, l) gray prediction model modeling process is as follows:Raw data preprocessing is as shown in formula (1):(1)x1k=∑i=1kx0i,k=1,2,…,n.The defined form predicted by Gm (1, 1) gray is shown in formula (2):(2)x0k+az1k=b,k=1,2,…,n,where z (k) is the adjacent generation of x (k), namely, z (k) = 0.5x (1) (k)+0.5x (1) (k-1), a is the development coefficient, and b is the gray action amount. The whitening differential equation for this system is obtained: as shown in formula (3):(3)dX1dt+aX1=b.Using least squares, solve a, b as shown in formula (4):(4)a,bT=BT,B−1BTY.Taking together formulas (2) and (3), it is as shown in formula (5):(5)x^1k+1=x^01−bae−ak+ba,x^0k+1=x^1k+1−x^1k.Furthermore, the predicted value of time pointK + 1 can be found as shown in formula (6):(6)x^0k+1=x01−ba1−eae−ak.The running state of the electric power robot is affected by the surrounding environment and changes all the time. If only the Gm (1, 1) static prediction model is used, the prediction accuracy of the real-time data of the electric power robot system will gradually decrease with the change of the electric power robot state. Therefore, the gray prediction algorithm applied to the robot system should be able to actively discard the old data to the historical moment, constantly introduce the new data from the current moment, and establish a real-time dynamic prediction model. The method of dynamic prediction is to establish the Gm (1, 1) model, introduce the continuous data ofL group in the time window, that is, from the current moment to the past L group data, which constantly updates the sample data of Gm (1, 1) model [17].As shown in formula (7):(7)X0new=x0k−L+1,x0k−L+2,…,x0k.With the complex underwater working environment of AUV, as well as many external interference, the nonlinearity and uncertainty of its own system is strong, which requires that the fault diagnosis method of the AUV system should have high accuracy and stability and can meet the real-time requirements of independent control of the system [18]. To this end, this paper first adopts the gray dynamic prediction method, to select real-time and continuous K data X0=x01,x02,…,x0k obtained by AUV sensor, and then get the gray prediction X1=x11,x12,…,x1k after pretreating these data by first-order accumulated generating operation. Eventually, x (k + 1) is obtained as the output expected value of k + 1, and then the residual difference of the output value is calculated as shown in formula (8):(8)εk+1=yk+1−xk+1,k=1,2,3….According to the abovementioned principle, using the dynamic gray prediction algorithm, the system fault can be quickly determined and the fault judgment rules are as shown in formula (9):(9)εk+12>εθ,NO,≤εθ,YES,Whereθ is the fault threshold set based on the system history failure data.


### 3.2. Outliers Detection Algorithm

In general, the fault diagnosis method based on outlier detection is based on the historical data of the system. Without the need of obtaining the system accurate model, it analyzes the hidden information implied in these data through data mining and obtains the fault rules to achieve the fault diagnosis. There are many methods for outlier detection, such as statistical-based, proximity-based, cluster-based, and classification-based, and so on [19]. Among them, the cluster-based outlier detection method determines the outlier points by examining the relationship between objects and clusters. Each outlier is an object, which belongs to a small remote cluster, or does not belong to any cluster. The fault diagnosis method is based on outlier detection has the following advantages. First, it is an unsupervised fault diagnosis method, effective for many types of data; second, after comparing other objects with the obtained cluster, the object can be decided whether it is outlier from in a rapid speed, and then fault diagnosis will be made [20]. The advantages of this kind of detection method are very suitable for the fault diagnosis requirements of the electric power robot system. Therefore, a cluster-based outlier detection method was used.The effectiveness of clustering-based outlier detection is highly dependent on the clustering method. Improper selection of the clustering method will directly lead to poor clustering effect and even clustering failure. Therefore, it is necessary to find a clustering method with high clustering accuracy and good timeliness for the fault diagnosis based on outlier detection [21]. An improved clustering algorithm with DBSCAN kernel and kernel idea, iterative density-based clustering algorithm IKD, and iterative density-based spatial clustering algorithm with noise application combining K-mean) are proposed [22].By analyzing the various steps of the IKD algorithm, it is seen that the IKD algorithm is an unsupervised clustering party rebellion without any prior knowledge. In order to evaluate the effect of the IKD algorithm clustering method, clustering evaluation parameter H is used. Assuming that the data objects are clustered into K clusters, H is defined as shown in formula (10):(10)H=AB=∑s=IKr∑p⊂Csp−ms∑s=IKrms−m,s=1,2,3,…,Kr.Compared with the original clustering algorithm, the IKD algorithm has the following advantages;(1)
First of all, after the first DBSCAN clustering, the outlier is temporarily deleted, which is ready for the next K-mean clustering, and completes the data pretreatment, so as to greatly overcome the disadvantage of using K-mean clustering algorithm in the process of clustering, but also the advantage of that DBSCAN algorithm can handle noise points has been fully utilized;(2)
Second, on the basis of the DBSCAN algorithm, the IKD algorithm combines with K-mean clustering algorithm, which through the change in the algorithm process, fundamentally and effectively solves two problems: one is that K-mean algorithm needs to give the number of clusters K of clusters to be generated in advance,; the other is that W and clustering effect is excessively dependent on the private random selection in the initial;(3)
Third, the final step of the IKD algorithm is to use the improved DBSCAN algorithm combined with the ISODATA algorithm for clustering, so the algorithm completely inherits the advantages of density-based clustering method, and can find clusters of any shape or clusters with great differences in the number of data objects, which is difficult for other clustering algorithms to do this;(4)
Finally, the IKD algorithm can automatically merge the clusters obtained in the clustering process, and through repeated iterative operation it can independently decide whether to terminate the cluster according to the cluster evaluation parameter H, so as to obtain the objective and correct clustering.By using this AUV fault diagnosis method based on gray prediction and IKD algorithm, the efficiency of fault diagnosis of robot system is significantly improved, and the accuracy of diagnosis is basically unchanged when using DCD based onD C D algorithm alone [23].


## 3.1. Gray Prediction Algorithm

The first-order linear gray model Gm (1, 1) is the basic prediction model of gray system theory. It takes differential fitting as the kernel and finds out the change law between the data according to the data characteristics of the system. The Gm (l, l) gray prediction model modeling process is as follows:Raw data preprocessing is as shown in formula (1):(1)x1k=∑i=1kx0i,k=1,2,…,n.The defined form predicted by Gm (1, 1) gray is shown in formula (2):(2)x0k+az1k=b,k=1,2,…,n,where z (k) is the adjacent generation of x (k), namely, z (k) = 0.5x (1) (k)+0.5x (1) (k-1), a is the development coefficient, and b is the gray action amount. The whitening differential equation for this system is obtained: as shown in formula (3):(3)dX1dt+aX1=b.Using least squares, solve a, b as shown in formula (4):(4)a,bT=BT,B−1BTY.Taking together formulas (2) and (3), it is as shown in formula (5):(5)x^1k+1=x^01−bae−ak+ba,x^0k+1=x^1k+1−x^1k.Furthermore, the predicted value of time pointK + 1 can be found as shown in formula (6):(6)x^0k+1=x01−ba1−eae−ak.The running state of the electric power robot is affected by the surrounding environment and changes all the time. If only the Gm (1, 1) static prediction model is used, the prediction accuracy of the real-time data of the electric power robot system will gradually decrease with the change of the electric power robot state. Therefore, the gray prediction algorithm applied to the robot system should be able to actively discard the old data to the historical moment, constantly introduce the new data from the current moment, and establish a real-time dynamic prediction model. The method of dynamic prediction is to establish the Gm (1, 1) model, introduce the continuous data ofL group in the time window, that is, from the current moment to the past L group data, which constantly updates the sample data of Gm (1, 1) model [17].As shown in formula (7):(7)X0new=x0k−L+1,x0k−L+2,…,x0k.With the complex underwater working environment of AUV, as well as many external interference, the nonlinearity and uncertainty of its own system is strong, which requires that the fault diagnosis method of the AUV system should have high accuracy and stability and can meet the real-time requirements of independent control of the system [18]. To this end, this paper first adopts the gray dynamic prediction method, to select real-time and continuous K data X0=x01,x02,…,x0k obtained by AUV sensor, and then get the gray prediction X1=x11,x12,…,x1k after pretreating these data by first-order accumulated generating operation. Eventually, x (k + 1) is obtained as the output expected value of k + 1, and then the residual difference of the output value is calculated as shown in formula (8):(8)εk+1=yk+1−xk+1,k=1,2,3….According to the abovementioned principle, using the dynamic gray prediction algorithm, the system fault can be quickly determined and the fault judgment rules are as shown in formula (9):(9)εk+12>εθ,NO,≤εθ,YES,Whereθ is the fault threshold set based on the system history failure data.


## 3.2. Outliers Detection Algorithm

In general, the fault diagnosis method based on outlier detection is based on the historical data of the system. Without the need of obtaining the system accurate model, it analyzes the hidden information implied in these data through data mining and obtains the fault rules to achieve the fault diagnosis. There are many methods for outlier detection, such as statistical-based, proximity-based, cluster-based, and classification-based, and so on [19]. Among them, the cluster-based outlier detection method determines the outlier points by examining the relationship between objects and clusters. Each outlier is an object, which belongs to a small remote cluster, or does not belong to any cluster. The fault diagnosis method is based on outlier detection has the following advantages. First, it is an unsupervised fault diagnosis method, effective for many types of data; second, after comparing other objects with the obtained cluster, the object can be decided whether it is outlier from in a rapid speed, and then fault diagnosis will be made [20]. The advantages of this kind of detection method are very suitable for the fault diagnosis requirements of the electric power robot system. Therefore, a cluster-based outlier detection method was used.The effectiveness of clustering-based outlier detection is highly dependent on the clustering method. Improper selection of the clustering method will directly lead to poor clustering effect and even clustering failure. Therefore, it is necessary to find a clustering method with high clustering accuracy and good timeliness for the fault diagnosis based on outlier detection [21]. An improved clustering algorithm with DBSCAN kernel and kernel idea, iterative density-based clustering algorithm IKD, and iterative density-based spatial clustering algorithm with noise application combining K-mean) are proposed [22].By analyzing the various steps of the IKD algorithm, it is seen that the IKD algorithm is an unsupervised clustering party rebellion without any prior knowledge. In order to evaluate the effect of the IKD algorithm clustering method, clustering evaluation parameter H is used. Assuming that the data objects are clustered into K clusters, H is defined as shown in formula (10):(10)H=AB=∑s=IKr∑p⊂Csp−ms∑s=IKrms−m,s=1,2,3,…,Kr.Compared with the original clustering algorithm, the IKD algorithm has the following advantages;(1)
First of all, after the first DBSCAN clustering, the outlier is temporarily deleted, which is ready for the next K-mean clustering, and completes the data pretreatment, so as to greatly overcome the disadvantage of using K-mean clustering algorithm in the process of clustering, but also the advantage of that DBSCAN algorithm can handle noise points has been fully utilized;(2)
Second, on the basis of the DBSCAN algorithm, the IKD algorithm combines with K-mean clustering algorithm, which through the change in the algorithm process, fundamentally and effectively solves two problems: one is that K-mean algorithm needs to give the number of clusters K of clusters to be generated in advance,; the other is that W and clustering effect is excessively dependent on the private random selection in the initial;(3)
Third, the final step of the IKD algorithm is to use the improved DBSCAN algorithm combined with the ISODATA algorithm for clustering, so the algorithm completely inherits the advantages of density-based clustering method, and can find clusters of any shape or clusters with great differences in the number of data objects, which is difficult for other clustering algorithms to do this;(4)
Finally, the IKD algorithm can automatically merge the clusters obtained in the clustering process, and through repeated iterative operation it can independently decide whether to terminate the cluster according to the cluster evaluation parameter H, so as to obtain the objective and correct clustering.By using this AUV fault diagnosis method based on gray prediction and IKD algorithm, the efficiency of fault diagnosis of robot system is significantly improved, and the accuracy of diagnosis is basically unchanged when using DCD based onD C D algorithm alone [23].


## 4. Experimental Results and Discussion

The 500 sets of data in the electric power robot propeller system are test data. See Figures4–7.Figure 4
Raw data.Figure 5
Comparison of clever evidence and predicted knot harvest after pretreatment.Figure 6
Results of predicting the model running.Figure 7
Operation curve diagram.In the test, the real-time operation data collected by the sensor are preprocessed. The specific method is to get the average from 10 sets of data to represent the sensor value.It can be seen from the test results that the Gm (1, 1) predicted the model accurately, and the residual squareεk+12 did not exceed 0.03. If the fault width set in the fault diagnosis system based on gray prediction is 0.05, it can be diagnosed that the electric power robot system has not failed [24].The reason why the first four predictions are 0 is that the sensor data of the first four gray predictions are used as the observation value of the initial moment in the algorithm, so the predicted value is only available from the fifth time on. From the comparison of preprocessed data and prediction data, the prediction curve well fits the actual measurements of the sensor, which verifies that the electric power robot fault diagnosis based on gray prediction is feasible [25].Finally, the test and analysis based on gray prediction and improved outlier detection are summarized. In addition to detecting hard faults in the system, another main purpose of introducing gray prediction methods is to improve the timeliness of outlier detection based on the IKD algorithm. As with the test set above, in the experiment, the number of iterations of ISODATA algorithm and IKD algorithm were still set at 5 times, and the same data pre-processing and gray prediction operations were performed before running the four cluster-based outlier tests.(1)
In the use of four algorithms outlier detection, combined with the gray prediction, the accuracy of result has a certain degree of improvement. Through analysis, it can be concluded that the fault data checked out by gray prediction algorithm is no longer used, thus reducing the noise in the data set, which in turn makes the outlier detection algorithm performance give full play;(2)
The running time of outlier detection using K-mean algorithm and DBSCAN algorithm increases to a certain extent, because the algorithm combines gray prediction and increases the algorithm process;(3)
It is worth noting that the running time of outlier detection using ISODATA algorithm and IKD algorithm is reduced to a certain extent. Among them, the time of diagnosis using IKD algorithm is reduced by 29.79%, which verifies that the fault diagnosis method based on gray prediction and outlier detection is feasible and improves the timeliness of fault diagnosis based on outlier detection.


## 5. Conclusion

This paper presents a diagnosis and monitoring method based on data mining. It analyzes the K-mean algorithm, the DBSCAN algorithm, and the ISODATA algorithm, respectively, and tests the improved IKD clustering algorithm in outlier detection. The verification results show that the outlier detection method based on data mining IKD algorithm is feasible. By comparison, it was found that the detection accuracy obtained by using the IKD algorithm was significantly higher than that obtained with other clustering algorithms, but with slightly less timeliness. To solve this problem, a gray prediction model is introduced in the fault detection of the electric power robot, and the test data show that this method works well and improve the speed of fault detection to some extent. Finally, the detected fault data completes the fault diagnosis of the underwater electric power robot propeller system.In the future data mining, the visualization means have been greatly enriched and improved. This advanced technology is very suitable for being applied to the fault diagnosis of complex systems. If this difficulty is overcome, the function of the fault diagnosis system in human-computer interaction will be greatly improved.



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