RESEARCH PAPER
Fast bearing fault diagnosis of rolling element using Lévy Moth-Flame optimization algorithm and Naive Bayes
1
School of Computer Science, Hubei University of Technology, Wuhan, Hubei, 430068, PR China
2
Lublin University of Technology, ul. Nadbystrzycka 36, 20-618, Lublin, Poland
3
Wuhan Fiberhome Technical Services Co., Ltd., Wuhan FiberHome Telecommunication Technologies Co., Ltd., Wuhan, Hubei, 430074, PR China
4
Lviv Polytechnic National University, Karpinskoho str. 1, Lviv, Ukraine
Publication date: 2020-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(4):730-740
HIGHLIGHTS
- A fault diagnosis method based on LMFO, ensures high classification accuracy and better efficiency.
- EEMD-based feature extraction method effectively removes signal noise.
- The feature selection method based on LMFO effectively removes feature redundancy.
- The NB-based fault diagnosis method ensures accuracy with high efficiency.
KEYWORDS
malfunction diagnosticsnaive Bayesmoth-flame optimization algorithmensemble empirical mode decomposition
ABSTRACT
Fault diagnosis is part of the maintenance system, which can reduce maintenance costs,
increase productivity, and ensure the reliability of the machine system. In the fault diagnosis
system, the analysis and extraction of fault signal characteristics are very important, which
directly affects the accuracy of fault diagnosis. In the paper, a fast bearing fault diagnosis
method based on the ensemble empirical mode decomposition (EEMD), the moth-flame
optimization algorithm based on Lévy flight (LMFO) and the naive Bayes (NB) is proposed,
which combines traditional pattern recognition methods meta-heuristic search can overcome
the difficulty of selecting classifier parameters while solving small sample classification
under reasonable time cost. The article uses a typical rolling bearing system to test the actual performance of the method. Meanwhile, in comparison with the known algorithms and
methods was also displayed in detail. The results manifest the efficiency and accuracy of
signal sparse representation and fault type classification has been enhanced.
REFERENCES (50)
1.
Antoniadou I, Manson G, Staszewski W, Barszcz T, Worden K. A time-frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions. Mechanical Systems and Signal Processing 2015; 64-65: 188-216, https://doi.org/10.1016/j.ymss....
2.
Awadallah M, Morcos M. Application of AI tools in fault diagnosis of electrical machines and drives-an overview. IEEE Transactions on Energy Conversion 2003; 18: 245-251, https://doi.org/10.1109/TEC.20....
3.
Chudzik A, Warda B. Fatigue life prediction of a radial cylindrical roller bearing subjected to a combined load using FEM. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(2): 212-220, https://doi.org/10.17531/ein.2....
4.
Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 1996; 26: 29-41, https://doi.org/10.1109/3477.4....
5.
Eberhart R, Kennedy J. A new optimizer using particle swarm theory. MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science; IEEE: Nagoya, Japan 1995; 39-43, https://doi.org/10.1109/MHS.19....
6.
Feng Z, Liang M, Zhang Y, Hou S. Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation. Renewable Energy 2012; 47: 112-126, https://doi.org/10.1016/j.rene....
7.
Glowacz A. Recognition of acoustic signals of induction motor using fft, smofs-10 and lsvm. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2015; 17(4): 569-574, https://doi.org/10.17531/ein.2....
8.
Goldberg D E, Holland J H. Genetic Algorithms and Machine Learning. Machine Learning 1988; 3: 95-99, https://doi.org/10.1023/A:1022....
9.
Guo W, Tse P W, Djordjevich A. Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition. Measurement 2012; 45: 1308-1322, https://doi.org/10.1016/j.meas....
10.
Holland J H. Adaptation in Natural and Artificial Systems; MIT press 1992, https://doi.org/10.7551/mitpre....
11.
Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q, Yen N C, Tung C C, Liu H H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 1998; 454: 903-995, https://doi.org/10.1098/rspa.1....
12.
Jiang F, Zhu Z, Li W, Chen G., Zhou G. Robust condition monitoring and fault diagnosis of rolling element bearings using improved EEMD and statistical features. Measurement Science and Technology 2014; 25: 025003, https://doi.org/10.1088/0957-0....
13.
Jiang F, Zhu Z, Li W, Zhou G, Chen G. Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis. Journal of Sound and Vibration 2014; 333: 3321-3331, https://doi.org/10.1016/j.jsv.....
14.
Jun S, Kochan O, Kochan R. Thermocouples with Built-In Self-testing. International Journal of Thermophysics 2016; 37: 37, https://doi.org/10.1007/s10765....
15.
Jun S, Kochan O. Common mode noise rejection in measuring channels. Instruments and Experimental Techniques 2015; 58: 86-89, https://doi.org/10.1134/S00204....
16.
Kennedy J. Swarm Intelligence. In Handbook of Nature-Inspired and Innovative Computing; Zomaya, A.Y., Ed.; Kluwer Academic Publishers: Boston 2006; 187-219, https://doi.org/10.1007/0-387-....
17.
Kennedy J. The particle swarm: social adaptation of knowledge. Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) 1997; 303-308, https://doi.org/10.1109/ICEC.1....
18.
Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks 1995; vol. 4: 1942-1948, https://doi.org/10.1109/ICNN.1....
19.
Kosicka E, Kozłowski E, Mazurkiewicz D. Intelligent Systems of Forecasting the Failure of Machinery Park and Supporting Fulfilment of Orders of Spare Parts. In International Conference on Intelligent Systems in Production Engineering and Maintenance. Springer, Cham. 2017; 54-63, https://doi.org/10.1007/978-3-....
20.
Kotary D K, Nanda S J. (2020). Distributed robust data clustering in wireless sensor networks using diffusion moth flame optimization. Engineering Applications of Artificial Intelligence 2020; 87: 103342, https://doi.org/10.1016/j.enga....
21.
Kozłowski E, Mazurkiewicz D, Żabiński T, Prucnal S, Sęp J. Assessment model of cutting tool condition for real-time supervision system. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(4): 679-685, https://doi.org/10.17531/ein.2....
22.
Lei Y, He Z, Zi Y, Hu Q. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mechanical Systems and Signal Processing 2007; 21: 2280-2294, https://doi.org/10.1016/j.ymss....
23.
Lei Y, He Z, Zi Y. Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing 2009; 23: 1327-1338, https://doi.org/10.1016/j.ymss....
24.
Leturiondo U, Salgado O, Galar D. Multi-body modelling of rolling element bearings and performance evaluation with localised damage. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2016; 18(4): 638-648, https://doi.org/10.17531/ein.2....
25.
Li C, Li S, Liu Y. A least squares support vector machine model optimized by moth-flame optimization algorithm for annual power load forecasting. Applied Intelligence 2016; 45: 1166-1178, https://doi.org/10.1007/s10489....
26.
Li Q, Chen H, Huang H, Zhao X, Cai Z, Tong C, Liu W, Tian X. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis. Computational and Mathematical Methods in Medicine 2017; 1-15, https://doi.org/10.1155/2017/9....
27.
Liu Y, Wang G, Chen H, Dong H, Zhu X, Wang S. An improved particle swarm optimization for feature selection. Journal of Bionic Engineering 2011; 8: 191-200, https://doi.org/10.1016/S1672-....
28.
Luo M, Li C, Zhang X, Li R, An X. Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings. ISA Transactions 2016; 65: 556-566, https://doi.org/10.1016/j.isat....
29.
Mafarja M M, Mirjalili S. Hybrid Whale Optimization Algorithm with simulated annealing for feature selection. Neurocomputing 2017; 260: 302-312, https://doi.org/10.1016/j.neuc....
30.
Maior C B S, Chagas Moura M, Lins I D. Particle swarm-optimized support vector machines and pre-processing techniques for remaining useful life estimation of bearings. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(4): 610-619, https://doi.org/10.17531/ein.2....
31.
Mei R N S, Sulaiman M H, Mustaffa Z, Daniyal H. Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique. Applied Soft Computing 2017; 59: 210-222, https://doi.org/10.1016/j.asoc....
32.
Mirjalili S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems 2015; 89: 228-249, https://doi.org/10.1016/j.knos....
33.
Nematollahi A F, Rahiminejad A, Vahidi B. A novel physical based meta-heuristic optimization method known as Lightning Attachment Procedure Optimization. Applied Soft Computing 2017; 59: 596-621, https://doi.org/10.1016/j.asoc....
34.
Przystupa K, Ambrożkiewicz B, Litak G. Diagnostics of Transient States in Hydraulic Pump System with Short Time Fourier Transform. Advances in Science and Technology. Research Journal, 2020; 14(1): 178-183, https://doi.org/10.12913/22998....
35.
Rashedi E, Nezamabadi-pour H, Saryazdi S. GSA: A Gravitational Search Algorithm. Information Sciences 2009; 179: 2232-2248, https://doi.org/10.1016/j.ins.....
36.
Rodrigues D, Pereira L A, Nakamura R Y, Costa K A, Yang X S, Souza A N, Papa J P. A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest. Expert Systems with Applications 2014; 41: 2250-2258, https://doi.org/10.1016/j.eswa....
37.
Rubini R, Meneghetti U. Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mechanical Systems and Signal Processing 2001; 15: 287-302, https://doi.org/10.1006/mssp.2....
38.
Sharawi M, Zawbaa H M, Emary E, Zawbaa H M, Emary E. Feature selection approach based on whale optimization algorithm. 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI); IEEE: Doha, Qatar 2017; 163-168, https://doi.org/10.1109/ICACI.....
39.
Stahczyk U. Feature Evaluation by Filter, Wrapper, and Embedded Approaches. In Feature Selection for Data and Pattern Recognition;Stahczyk, U.; Jain, L.C., Eds.; Springer Berlin Heidelberg: Berlin, Heidelberg 2015; 584: 29-44, https://doi.org/10.1007/978-3-....
40.
Storn R, Price K. Minimizing the real functions of the ICEC'96 contest by differential evolution. Proceedings of IEEE International Conference on Evolutionary Computation; IEEE: Nagoya, Japan 1996; 842-844, https://doi.org/10.1109/ICEC.1....
41.
Sulaiman M H, Mustaffa Z, Aliman O, Daniyal H, Mohamed M R. Application of moth-flame optimization algorithm for solving optimal reactive power dispatch problem. 4th IET Clean Energy and Technology Conference (CEAT 2016) 2016, https://doi.org/10.1049/cp.201....
42.
Wan Y, Wang M, Ye Z, Lai X. A feature selection method based on modified binary coded ant colony optimization algorithm. Applied Soft Computing 2016; 49: 248-258, https://doi.org/10.1016/j.asoc....
43.
Wang X, Yang J, Teng X, Xia W, Jensen R. Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters 2007; 28: 459-471, https://doi.org/10.1016/j.patr....
44.
Wolpert D H. The lack of a priori distinctions between learning algorithms. Neural Computation 1996; 8: 1341-1390, https://doi.org/10.1162/neco.1....
45.
Zawbaa H M, Emary E, Grosan C, Feature Selection via Chaotic Antlion Optimization. PLOS ONE 2016; 11: e0150652, https://doi.org/10.1371/journa....
46.
Zhang L, Xiong G, Liu H, Zou H, Guo W. Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Systems with Applications 2010; 37: 6077-6085, https://doi.org/10.1016/j.eswa....
47.
Zhang W, Jia M P, Zhu L, Yan X A. Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis. Chinese Journal of Mechanical Engineering 2017; 30: 782-795, https://doi.org/10.1007/s10033....
48.
Zhang X, Zhou J. Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mechanical Systems and Signal Processing 2013; 41: 127-140, https://doi.org/10.1016/j.ymss....
49.
Zhang Y, Zuo H, Bai F. Classification of fault location and performance degradation of a roller bearing. Measurement 2013; 46: 1178-1189, https://doi.org/10.1016/j.meas....
50.
Zio E. Reliability engineering: Old problems and new challenges. Reliability Engineering & System Safety 2009; 94: 125-141, https://doi.org/10.1016/j.ress....
CITATIONS (44):
1.
An Analysis Method for Time-, Frequency-, and Energy-Domain Characteristics of Downhole Microseismic Signals and Its Application
Xiao-xu Gao, Xiang-xu Pan, Guang-an Zhu, Xiaowei Feng
Shock and Vibration
Xiao-xu Gao, Xiang-xu Pan, Guang-an Zhu, Xiaowei Feng
Shock and Vibration
2.
Bending Behaviour of Polymeric Materials Used on Biomechanics Orthodontic Appliances
Ivo Domagała, Krzysztof Przystupa, Marcel Firlej, Daniel Pieniak, Agata Niewczas, Barbara Biedziak
Materials
Ivo Domagała, Krzysztof Przystupa, Marcel Firlej, Daniel Pieniak, Agata Niewczas, Barbara Biedziak
Materials
3.
Application of Vibration Signals for the Quantitative Analysis of the Optimal Threshold of Bearing Failure
YaoChi Tang, Kuohao Li, Chengwei Fei
Shock and Vibration
YaoChi Tang, Kuohao Li, Chengwei Fei
Shock and Vibration
4.
A secondary selection-based orthogonal matching pursuit method for rolling element bearing diagnosis
Yongjian Li, Feng Zheng, Qing Xiong, Weihua Zhang
Measurement
Yongjian Li, Feng Zheng, Qing Xiong, Weihua Zhang
Measurement
5.
Mechanical Properties and Strength Reliability of Impregnated Wood after High Temperature Conditions
Krzysztof Przystupa, Daniel Pieniak, Waldemar Samociuk, Agata Walczak, Grzegorz Bartnik, Renata Kamocka-Bronisz, Monika Sutuła
Materials
Krzysztof Przystupa, Daniel Pieniak, Waldemar Samociuk, Agata Walczak, Grzegorz Bartnik, Renata Kamocka-Bronisz, Monika Sutuła
Materials
6.
Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning
Karolina Kudelina, Toomas Vaimann, Bilal Asad, Anton Rassõlkin, Ants Kallaste, Galina Demidova
Applied Sciences
Karolina Kudelina, Toomas Vaimann, Bilal Asad, Anton Rassõlkin, Ants Kallaste, Galina Demidova
Applied Sciences
7.
A Hybrid Feature Selection Framework Using Improved Sine Cosine Algorithm with Metaheuristic Techniques
Lichao Sun, Hang Qin, Krzysztof Przystupa, Yanrong Cui, Orest Kochan, Mikołaj Skowron, Jun Su
Energies
Lichao Sun, Hang Qin, Krzysztof Przystupa, Yanrong Cui, Orest Kochan, Mikołaj Skowron, Jun Su
Energies
8.
Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge Collaboration
Xianghong Tang, Lei Xu, Gongsheng Chen
Entropy
Xianghong Tang, Lei Xu, Gongsheng Chen
Entropy
9.
Online Measurement Error Detection for the ElectronicTransformer in a Smart Grid
Gu Xiong, Krzysztof Przystupa, Yao Teng, Wang Xue, Wang Huan, Zhou Feng, Xiang Qiong, Chunzhi Wang, Mikołaj Skowron, Orest Kochan, Mykola Beshley
Energies
Gu Xiong, Krzysztof Przystupa, Yao Teng, Wang Xue, Wang Huan, Zhou Feng, Xiang Qiong, Chunzhi Wang, Mikołaj Skowron, Orest Kochan, Mykola Beshley
Energies
10.
Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization
Huaitao Shi, Yangyang Li, Peng Zhou, Shenghao Tong, Liang Guo, Baicheng Li, Paola Forte
Shock and Vibration
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Shock and Vibration
11.
An adaptive damage detection method based on differential evolutionary algorithm for beam structures
Naige Wang, Yongying Jiang, Yongteng Zhong, Liang Shao
Measurement
Naige Wang, Yongying Jiang, Yongteng Zhong, Liang Shao
Measurement
12.
Identification of geometric errors of circular profiles at WEDM caused by the wire tool electrode vibrations and their reduction with support of acoustic emission method
Ľuboslav Straka, Ivan Čorný
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Engineering Failure Analysis
13.
Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM
Maoyou Ye, Xiaoan Yan, Minping Jia
Entropy
Maoyou Ye, Xiaoan Yan, Minping Jia
Entropy
14.
Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier
Josef Prost, Ulrike Cihak-Bayr, Ioana Neacșu, Reinhard Grundtner, Franz Pirker, Georg Vorlaufer
Lubricants
Josef Prost, Ulrike Cihak-Bayr, Ioana Neacșu, Reinhard Grundtner, Franz Pirker, Georg Vorlaufer
Lubricants
15.
Research on gear fault diagnosis based on feature fusion optimization and improved two hidden layer extreme learning machine
Lizheng Pan, Lu Zhao, Aiguo Song, Shigang She, Shunchao Wang
Measurement
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Measurement
16.
A novel performance degradation prognostics approach and its application on ball screw
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Measurement
Xiaochen Zhang, Tianjian Luo, Te Han, Hongli Gao
Measurement
17.
Ensemble empirical mode decomposition energy moment entropy and enhanced long short-term memory for early fault prediction of bearing
Zehai Gao, Yang Liu, Quanjiu Wang, Jiali Wang, Yige Luo
Measurement
Zehai Gao, Yang Liu, Quanjiu Wang, Jiali Wang, Yige Luo
Measurement
18.
Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction
Tarek Berghout, Mohamed Benbouzid, Leïla-Hayet Mouss
Energies
Tarek Berghout, Mohamed Benbouzid, Leïla-Hayet Mouss
Energies
19.
Examination of Abnormal Behavior Detection Based on Improved YOLOv3
Meng-ting Fang, Zhong-ju Chen, Krzysztof Przystupa, Tao Li, Michal Majka, Orest Kochan
Electronics
Meng-ting Fang, Zhong-ju Chen, Krzysztof Przystupa, Tao Li, Michal Majka, Orest Kochan
Electronics
20.
Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization
Huibin Shi, Wenlong Fu, Bailin Li, Kaixuan Shao, Duanhao Yang
Entropy
Huibin Shi, Wenlong Fu, Bailin Li, Kaixuan Shao, Duanhao Yang
Entropy
21.
Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
Theresa Loss, Alexander Bergmann
Applied Sciences
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Applied Sciences
22.
Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight
Xuan Chen, Krzysztof Przystupa, Zhiwei Ye, Feng Chen, Chunzhi Wang, Jinhang Liu, Rong Gao, Ming Wei, Orest Kochan
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23.
Magnetic Flux Analysis for the Condition Monitoring of Electric Machines: A Review
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IEEE Transactions on Industrial Informatics
24.
Short-Time/-Angle Spectral Analysis for Vibration Monitoring of Bearing Failures under Variable Speed
Edgar Sierra-Alonso, Julian Caicedo-Acosta, Gutiérrez Orozco, Héctor Quintero, German Castellanos-Dominguez
Applied Sciences
Edgar Sierra-Alonso, Julian Caicedo-Acosta, Gutiérrez Orozco, Héctor Quintero, German Castellanos-Dominguez
Applied Sciences
25.
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Micromachines
Ľuboslav Straka, Miroslav Gombár, Alena Vagaská, Patrik Kuchta
Micromachines
26.
Signal Identification of Gear Vibration in Engine-Gearbox Systems Based on Auto-Regression and Optimized Resonance-Based Signal Sparse Decomposition
Yuanyuan Huang, Shuiguang Tong, Zheming Tong, Feiyun Cong
Sensors
Yuanyuan Huang, Shuiguang Tong, Zheming Tong, Feiyun Cong
Sensors
28.
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Applied Sciences
Gang Yao, Yunce Wang, Mohamed Benbouzid, Mourad Ait-Ahmed
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29.
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Experimental Techniques
30.
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31.
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Krzysztof Przystupa, Mykola Beshley, Olena Hordiichuk-Bublivska, Marian Kyryk, Halyna Beshley, Julia Pyrih, Jarosław Selech
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32.
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Applied Acoustics
34.
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Materials Today: Proceedings
35.
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Dade Wu
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36.
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Bin Wang, Yanbao Guo, Zheng Zhang, Deguo Wang, Junqiang Wang, Yuansheng Zhang
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Measurement
37.
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YONGQIANG DAI, ZHIHUI WANG, HUAN LIU, LEILEI LIU
YONGQIANG DAI, ZHIHUI WANG, HUAN LIU, LEILEI LIU
38.
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Oguzhan Das, Das Bagci, Derya Birant
Heliyon
Oguzhan Das, Das Bagci, Derya Birant
Heliyon
39.
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Xiangqu Xiao, Chaoshun Li, Jie Huang, Tian Yu, Pak Wong
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40.
Vibration-based bearing fault diagnosis of high-speed trains: a literature review
Wanchun Hu, Ge Xin, Jiayi Wu, Guoping An, Yilei Li, Jerome Antoni
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High-speed Railway
43.
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Aditi Gupta, Adeiza J. Onumanyi, Satyadev Ahlawat, Yamuna Prasad, Virendra Singh, Adnan M. Abu-Mahfouz
Pattern Recognition Letters
Aditi Gupta, Adeiza J. Onumanyi, Satyadev Ahlawat, Yamuna Prasad, Virendra Singh, Adnan M. Abu-Mahfouz
Pattern Recognition Letters
44.
A State Monitoring Algorithm for Data Missing Scenarios via Convolutional Neural Network and Random Forest
Yuntao Xu, Kai Sun, Ying Zhang, Fuyang Chen, Yi He
IEEE Access
Yuntao Xu, Kai Sun, Ying Zhang, Fuyang Chen, Yi He
IEEE Access
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| ISSN: | 1507-2711 |
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