dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper proposes a three-phase metaheuristic-based approach for induction machine bearing failure detection and diagnosis. It consists of extracting and processing different failure types features to set up a knowledge base, which contains different failure types. The first phase consists in pre-processing the measured signals by aggregating them and preparing the data in exploitable formats for the clustering. The second phase ensures the induction machine operating mode diagnosis. A measured signals clustering is performed to build classes where each one represents a health state. A variable neighborhood search (VNS) metaheuristic is designed for data clustering. Moreover, VNS is hybridized with a classical mechanics-inspired optimization (CMO) metaheuristic to balance global exploration and local exploitation during the evolutionary process.
The third phase consists of two-step failure detection, setting up a knowledge base containing different failure types, and defining a representative model for each failure type. In the learning step, different class features are extracted and inserted in the knowledge base to be used during the decision step. The proposed metaheuristic-based failure detection diagnosis approach is evaluated using PRONOSTIA and CWR bearing data experimental platforms vibration and temperature measurements. The achieved results clearly demonstrate the failure detection and diagnosis, efficiency, and effectiveness of the proposed metaheuristic approach.
Most of the carried-out researches in this field are based on GA. However, GAs have two main drawbacks: local search ability lack and premature convergence. In this context, trajectory methods, such as VNS, have shown potential to exploit promising regions in the search space with higher quality solutions. Nevertheless, it is still prone to premature convergence traps due to the limited exploration ability. Thus, it is a natural choice to consider the hybridization of metaheuristics  [29]  to solve optimization problems  [30]. In this paper, we propose a three-phase approach, each designed to meet a specific objective to solve the bearings failure detection and diagnosis problem in induction machine. In the first phase, measured experimental platforms signals aggregation and data preparation is carried out.
This graphical representation describes bearings degradation evolution over time. The last phase ensures bearing failures detection by making failure type decision. It is composed of two steps: learning and decision. The proposed learning algorithm uses the resulting VNS clustering graphical representation as image input to extract and process different class features. The extracted learning data are stored in a knowledge base that will be used for decision making. In the second step, the extracted features are used to calculate failure type probabilities. Figure  1  describes the proposed approach workflow. Energies 2020, 13, x FOR PEER REVIEW 4 of 30 algorithm to solve such an NP-Hard combinatory problem, this is why there is a crucial need to apply artificial intelligence techniques to find a good solution in a bounded time.
This graphical representation describes bearings degradation evolution over time. The last phase ensures bearing failures detection by making failure type decision. It is composed of two steps: learning and decision. The proposed learning algorithm uses the resulting VNS clustering graphical representation as image input to extract and process different class features. The extracted learning data are stored in a knowledge base that will be used for decision making. In the second step, the extracted features are used to calculate failure type probabilities. Figure  1  describes the proposed approach workflow. The following subsections discuss the details of each phase for solving the bearing failure detection and diagnosis problem in induction machines.
First, the solution representation and the objective function, then the initial solution generation methods, neighborhood structures, local search procedures, and the shaking technique are discussed. Algorithm 1. VNS Main Algorithm Input InputDataset, Exit_conditions Begin E = Pre-processing (InputDataset) X 0 = Generate_Initial_Solution(E) X best = X 0 X current = X 0 i = 0 Repeat k = 1 Stagnation = True X prev = X current While k ≤ 3 Do// Neighborhood Search Switch (k) Case 1: X current = Expand_Local_Search (X prev ) Case 2: X current = Reduce_Local_Search (X prev ) Case 3: X current = Ascend_Local_Search (X prev ) End Switch If f(X current )>f(X prev ) Then k=1 Stagnation = False Else k = k + 1 End If End While If (Stagnation) Then X current = Shake (X prev ) // Shaking i=i+1 Else i = 0 End If If f(X current ) > f(X best ) Then X best = X current Until Exit_conditions Return X best End.
• Randomly This process consists of assigning objects to classes. The number of classes is increased in each iteration and varies in an interval from two to the predefined maximum number that defines the exit condition. In each iteration, classes centers are randomly selected according to the current classes number, then each data is assigned to its nearest class center. If the current iteration solution has the best objective function value, it becomes the best solution. The output is the data configuration that has the best objective function (Algorithm 2)  [28]. This procedure allows defining a dynamic number of classes because the number of resulting classes varies according to the best-found solution. Algorithm 2. Random initial solution generation. Input Dataset B // measurement data Begin Initialize Max_Classes_Number Repeat k = 2 // Initialize k the classes number While k ≤ Max_Classes_Number Do Classes_center = Select_Class_Center(B,k) X k =Affect_Data(B,Classes_center) Calculate f(X k ) If f(X k ) > f(X best ) Then X bes t = X k End If k = k + 1 End While Until Exit_conditions Return X best End The main problem of the random generation is to define the classes number because it is a random technique for choosing the classes number and also for choosing the class centers.
In this example, CMO metaheuristic is applied in both learning and test steps on the input VNS-clustering graphical representation image (Figure  9 ) to make clustering containing new bearings health state configuration (Figure  10 ). Each class representing a health state is processed at pixel level to extract its defining features for a given failure type: color patterns which represent a dominant health states model, color changes which represent a bearing transition model from an operating mode to another, the class  In this example, CMO metaheuristic is applied in both learning and test steps on the input VNS-clustering graphical representation image (Figure  9 ) to make clustering containing new bearings health state configuration (Figure  10 ). vibrations, etc. ) when faults occur  [11]  while frequency-domain feature extraction is mainly based on measured signals analysis  [12] , and time-frequency-domain feature extraction is based on empirical methods such as the empirical mode decomposition (EMD)  [13]  that uses a dataset containing captured signals without any assumption about data to extract useful information.
, s m ). After transforming the input data into a space body system, each body represents one class, which contains its included satellites. Each class center is its corresponding planet. Each class has two gravitation fields calculated according to its mass, which correspond to an attraction force applied. The first field is applied on the Earth-Moon system to keep the satellites in rotation around planets using the input data; the second one is applied on the Earth-Apple system, causing some bodies to fall on others applying the greatest attraction force. In the earth-moon system equilibrium step, each class c 1 exerts an attraction force F c1s on a satellite, which is rotating around another planet p 2 with an applied force F c2s. If F c1s > F c2s , then the satellites leave its path around p 2 and follows a new path around p 1.
Decision Step The decision algorithm (Algorithm 9) is used to detect the failure type from an input image of a given test dataset VNS-clustering graphical representation, it also shares the same first two data processing steps with the learning algorithm: the CMO metaheuristic data clustering step and the extraction step of the color patterns, color changes, the class edges and pattern shapes, sizes, and locations features. After the features are extracted, the algorithm uses them to calculate matching probabilities with the knowledge base, where during each matching the test image colors are normalized according to a learning images colors degradation over time, a failure type decision is then taken according to the highest probability. Finally, the algorithm performs cognitive feedback by comparing the detected failure type original features and coefficients in the knowledge base with the ones extracted from the input image to enhance the knowledge data and optimize the coefficients.
Validation on Experimental Data The aim of this section is testing how effective is the proposed three-phase approach for solving induction machines bearing failures detection and diagnosis problem. The algorithms have been implemented on MATLAB 2015 and C#. NET 2015 and run experiments on 64 bits operating system PC with 8 Gb of installed memory and i7 processor @ 2. 00GHz. To ensure results, stability algorithms have been independently run 10 times on each dataset. The experiments are classified into two sets of tests of which each is dedicated to each phase of the proposed approach. In what follows, we will first describe the used datasets and data pre-processing. Then, we evaluate the different features of our newly proposed VNS algorithm. Finally, experimental results are analyzed and discussed.
Datasets PRONOSTIA experimental platform vibration and temperature data have been used for validation of the proposed failures diagnosis approach  [43, 44]  (Figure  11 ). PRONOSTIA is an experimentation platform devoted to test and validate bearings failure diagnosis and prognosis approaches  [10, 17, 26]. It provides datasets obtained by run-to-failure experiments that stop at total bearing failure, resulting in measuring a vibration maximum value (20 g). In this platform, two accelerometers measure the bearing horizontal and vertical vibrations and a thermocouple measures its temperature. The vibration and temperature signals were sampled at 25. 6 and 10Hz, respectively. Files containing the signals measured during 10 min intervals are generated inside each experiment folder. In terms of data management, they have been stored in a database using an MS-SQL-Server system where pre-processing and data storage are developed with VB.
Net 2015. Details on data management and used sets for training and testing can be found in  [43, 44]. PRONOSTIA data sets are summarized in Table  1. CWR vibration data were provided by the Case Western Reserve University Bearing Data Center  [45]. It is an experimentation platform devoted to test and validate bearings failure detection approaches (Figure  12 ). Practical details on the experimental platform and bearing specific failure data can be found in  [45, 46]. For the evaluation of the failure detection performances, the CWR measured vibration signals are used. Two different sets of experimentations were performed on all failure types. The first set collected all the failure types data at a sampling time of 12,000 samples/second and the second one at 48,000 samples/second. In this work, all the dataset collected at 12,000 samples/second are used as learning datasets and the ones collected at 48,000 samples/second are used as test datasets.
Dataset Operating Conditions Condition1 Condition2 Condition3 Radial Load (N) 0. 007" 0 1797 IR007_0 B007_0 OR007@6_0 OR007@3_0 OR007@12_0 1 1772 IR007_1 B007_1 OR007@6_1 OR007@3_1 OR007@12_1 2 1750 IR007_2 B007_2 OR007@6_2 OR007@3_2 OR007@12_2 3 1730 IR007_3 B007_3 OR007@6_3 OR007@3_3 OR007@12_3 0. 014" 0 1797 IR014_0 B014_0 OR014@6_0 * * 1 1772 IR014_1 B014_1 OR014@6_1 * * 2 1750 IR014_2 B014_2 OR014@6_2 * * 3 1730 IR014_3 B014_3 OR014@6_3 * * 0. 021" 0 1797 IR021_0 B021_0 OR021@6_0 OR021@3_0 OR021@12_0 1 1772 IR021_1 B021_1 OR021@6_1 OR021@3_1 OR021@12_1 2 1750 IR021_2 B021_2 OR021@6_2 OR021@3_2 OR021@12_2 3 1730 IR021_3 B021_3 OR021@6_3 OR021@3_3 OR021@12_3 0. 0. 007" 0 1797 IR007_0 B007_0 OR007@6_0 OR007@3_0 OR007@12_0 1 1772 IR007_1 B007_1 OR007@6_1 OR007@3_1 OR007@12_1 2 1750 IR007_2 B007_2 OR007@6_2 OR007@3_2 OR007@12_2 3 1730 IR007_3 B007_3 OR007@6_3 OR007@3_3 OR007@12_3 0.
Pre-Processing In PRONOSTIA platform, each data file contains the measured signals for 10 min with a sampling frequency of 25. 6Hz for temperature signals and 10Hz for vibration signals. To optimize the algorithm progressing time, measurements have been aggregated per file considering the aggregated file as a single signal measurement using a standard deviation function. In this context, each signal measurement file becomes a single datum. After the data aggregation step, fair data normalization is applied to vibration, temperature signals, and time. An example of a vibration raw signal from a PRONOSTIA platform experiment is shown in Figure  13. Tnormalized = T. Vaverage. Kaverage. Coefftime where Coeffvibration, Coefftemperature, and Coefftime represent the relative impact coefficients. Example: giving two vectors x={0,2,4} and y={100,200,300}, the normalization here gives x'= {0,400,800} and y'={200,400,600}, i.
Pre-Processing In PRONOSTIA platform, each data file contains the measured signals for 10 minutes with a sampling frequency of 25. 6Hz for temperature signals and 10Hz for vibration signals. To optimize the algorithm progressing time, measurements have been aggregated per file considering the aggregated file as a single signal measurement using a standard deviation function. In this context, each signal measurement file becomes a single datum. After the data aggregation step, fair data normalization is applied to vibration, temperature signals, and time. An example of a vibration raw signal from a PRONOSTIA platform experiment is shown in Figure  13.
Diagnosis Phase Using VNS Clustering The two proposed VNS versions (RAN_VNS and CMO_VNS) are applied and tested with each neighborhood structure Expand and Reduce separately before combining them, to evaluate the performance of the proposed approaches. After the pre-processing phase, newly aggregated and normalized data is obtained. The first way to define an initial solution is a random generation where the selected solution has the best objective function and the class number is auto-selected according to the best solution. The obtained random initial solution for the learning set B1_1,using the variance aggregation function and by applying fair relative impact coefficients for normalization, is shown in Figure  14. The obtained number of random generation classes is big, and the classes are not well-separated which will influence VNS convergence.
The second way to define an initial solution is by using CMO metaheuristic. The obtained result for the learning set B1_1 is shown in Figure  15. The classes number of the obtained CMO results is not big and the classes are well-separated. This will be helpful to the VNS convergence. Using CMO initial solution generation, we will first separately test the performance of each neighborhood structure expand and reduce before combining them. The third local search consists of inverting the last used structure between the first neighborhood structure and the second one, so it is not possible to test it separately. At each iteration in the expand neighborhood, the strongest class radius is expanded. However, it will not lead to the single class configuration that contains all data, because the assigned data make the strongest class weaker when running the next iterations.
However, it will not lead to a configuration when each class contains only one data, because the loosed distant data make the weakest class stronger when running the next iterations. The obtained results are shown in Figure  17. The obtained result for learning set B1_1 by using only the reduce neighborhood shows three classes, two classes for the healthy mode and one for the faulty one. The results obtained by separately applying Expand and Reduce neighborhood structures show a data clustering with separated classes. We can now use them as different local search procedures for the proposed VNS. In the following, we will present tests of VNS-clustering application on PRONOSTIA datasets where CMO is used to generate the initial solution with the three proposed local search procedures Expand_Local_Search, Reduce_Local_Search, and Ascend_Local_Search for the solution space exploitation, and the shaking procedure for the solution space exploration.
The results are shown in Figure  18. shown in Figure  17. The obtained result for learning set B1_1 by using only the reduce neighborhood shows three classes, two classes for the healthy mode and one for the faulty one. shown in Figure  17. The obtained result for learning set B1_1 by using only the reduce neighborhood shows three classes, two classes for the healthy mode and one for the faulty one. The results obtained by separately applying Expand and Reduce neighborhood structures show a data clustering with separated classes. We can now use them as different local search procedures for the proposed VNS. In the following, we will present tests of VNS-clustering application on PRONOSTIA datasets where CMO is used to generate the initial solution with the three proposed local search procedures Expand_Local_Search, Reduce_Local_Search, and Ascend_Local_Search for the solution space exploitation, and the shaking procedure for the solution space exploration.
Detection Phase Performance Analysis on CWR Bearing Data Center Since PRONOSTIA platform does not provide detection datasets, to assess the performance of the failures detection phase we used CWR Bearing Data Center datasets for all bearing failure types  [45] , as detailed in (Table  2 ) and (Table  3 ), fifteen datasets are used for the learning step and thirteen are used for the testing step. After the pre-processing phase, VNS-clustering is applied to CWR datasets. The resulting clustering contains different classes representing the system health state as Each class in the resulting clustering represents an induction machine (bearing) operating state. The achieved results clearly show the proposed VNS-clustering high performance, where bearing degradation is highlighted over time. Indeed, for each experiment, several classes are obtained.
Detection Phase Performance Analysis on CWR Bearing Data Center Since PRONOSTIA platform does not provide detection datasets, to assess the performance of the failures detection phase we used CWR Bearing Data Center datasets for all bearing failure types  [45] , as detailed in (Tables  2  and 3 ), fifteen datasets are used for the learning step and thirteen are used for the testing step. After the pre-processing phase, VNS-clustering is applied to CWR datasets. The resulting clustering contains different classes representing the system health state as shown in Figure  19 , using the variance aggregation function and by applying fair relative impact coefficients for normalization. The proposed learning algorithm processes these classes to extract the learning dataset features and insert them in the knowledge base (Table  4 ).
During the decision step, the VNS algorithm processes the test sets to construct data clustering and then the decision algorithm ensures failure detection by processing the resulting clustering to extract features and matching them with the knowledge base. The obtained results for bearing failure detection using the learning algorithm are summarized in Table  5. Prob_1, Prob_2, and Prob_3 represent failure type probabilities, the results show the three probable failures in descending order for each test set detection result where the final decision taken by the algorithm is the most probable failure detection and having a probability ofProb_1. Only one learning session was performed for every failure type, according to the platform provided learning datasets. Among 13 tests, the failure detection decision probability Prob_1 for 10 tests exceeded 50%, and despite the fact that the decision probabilities of the remaining three tests were below 50%, they were correct and had a decision probability that is way higher than the next two probable failure types Prob_2 and Prob_3, meaning that the algorithm decisions were conclusive for all the 13 cases.
Xu et al. [26]  applied k-means, k-medoids, FCM, GK, GG, and AP clustering on PRONOSTIA datasets  [44]  to diagnoses bearing failures. After empirical mode decomposition of signals, a singular value decomposition is applied to obtain SV1 (Singular Value 1) and SV2 (Singular Value 2), the cluster center points of the three states (normal, slight, and severe) are found according to SV1 and SV2. Xu et al. achieved results are given in  Figure 10 [26]. Compared to the proposed VNS-clustering results (Figure  18 ) on the same dataset, we observe that for k-means, k-medoids, FCM, and AP clustering results (Figure  10 )  [26]  give well-separated classes, but they have almost the same results because they are based on the same initial solution and are sensitive to the latter. Moreover, the slight class center is not well centered which can give a very advanced deterioration alert, so they could not effectively track the early stages of severe class.
For GK clustering (Figure  10 )  [26]  the resulting slight and severe classes are in the wrong order, making the clustering inaccurate. A single class representing the severe health state resulted from the GG clustering (Figure  10 )  [26]  for all the datasets, meaning that the different health states cannot be diagnosed  [26]. The advantage of the proposed VNS is the use of an objective function that simultaneously respects both parts of the clustering definition, the first part consists of minimizing the intra-class distance and the second one consists of well separating classes by maximizing the inter-class distance, using data similarity measure (distance). Using CMO, a clustering-oriented metaheuristic, to generate an initial solution helps in effectively VNS. As a consequence, the obtained classes do not only represent the induction machine healthy or faulty modes, but they represent the machine (bearings) state evolution (bearings degradation) over time.
The third phase ensures failure detection using the resulting VNS-clustering to extract class features and update the knowledge base. The proposed metaheuristic-based failure detection and diagnosis approach was evaluated using PRONOSTIA and CWR Bearing Data experimental platforms vibration and temperature measurements. The achieved results clearly demonstrate the failure detection and diagnosis efficiency and effectiveness of the VNS/CMO-based metaheuristic approach. On one hand, high performance clustering is clearly highlighted, leading to increased failure diagnosis accuracy. On the other hand, the metaheuristic ability has been shown not only for bearing failure diagnosis but also for tracking its evolution over time. Despite running only one learning session, the obtained results were encouraging and can be improved by performing more learning sessions.