Bearing Fault Detection: Feature Selection Algorithm Efficacy Study

被引:1
作者
Nayana, B. R. [1 ]
Geethanjali, P. [2 ]
机构
[1] MS Ramaiah Univ Appl Sci, Dept Comp Sci Engn, Bangalore, India
[2] VIT Univ, Sch Elect Engn, Vellore, India
关键词
Bearing faults; Classification; Feature extraction; Feature selection; Nature inspired algorithms; DIAGNOSIS; IDENTIFICATION;
D O I
10.1080/03772063.2023.2248944
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Early detection of bearing failures is crucial, requiring a comprehensive diagnostic scheme with relevant features and an effective classifier. Computationally intelligent algorithms (CIA) contribute to enhancing the feature selection process. In this study, 99 classification datasets with classes ranging from 4 to 48 were derived from vibration recordings of Case Western Reserve University. 18 features are extracted in the time domain, of which 12 are statistical features and 6 are time-based frequency features (TBFF). Extracted features are ranked by mutual information, multi-cluster feature selection, and Laplacian scores. The best feature subsets are explored by four CIAs: ant colony optimization, simulated annealing, particle swarm optimization, and wheel-based differential evolution(WBDE) algorithms. The top 4 features of every approach are evaluated with 99 datasets. It was discovered that the feature subset recognized by most of the methodologies is the energy of the 4th derivative, waveform length ratio, sparseness, and mean. This combination results in 92.4% (with 4 features) and 88.94%(with 3 features) accuracy for classifying 48 class data, whereas the deep learning model resulted in 85.6% (with 18 features). Remarkably, it is noted that the feature subset identified by WBDE is identical to the feature subset arrived by voting. Finally, the results are compared to similar research in the literature, and improvements in classification accuracy are revealed.
引用
收藏
页码:5228 / 5237
页数:10
相关论文
共 50 条
[41]   A Feature Selection Method for High Impedance Fault Detection [J].
Cui, Qiushi ;
El-Arroudi, Khalil ;
Weng, Yang .
IEEE TRANSACTIONS ON POWER DELIVERY, 2019, 34 (03) :1203-1215
[42]   Feature Selection for Aero-Engine Fault Detection [J].
Udu, Amadi Gabriel ;
Lecchini-Visintini, Andrea ;
Dong, Hongbiao .
DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2023, PT I, 2023, 14146 :522-527
[43]   Feature selection for fault level diagnosis of planetary gearboxes [J].
Liu, Zhiliang ;
Zhao, Xiaomin ;
Zuo, Ming J. ;
Xu, Hongbing .
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 2014, 8 (04) :377-401
[44]   New criteria for wrapper feature selection to enhance bearing fault classification [J].
Sahraoui, Mohammed Amine ;
Rahmoune, Chemseddine ;
Meddour, Ikhlas ;
Bettahar, Toufik ;
Zair, Mohamed .
ADVANCES IN MECHANICAL ENGINEERING, 2023, 15 (06)
[45]   Bearing fault diagnosis using multiple feature selection algorithms with SVM [J].
Kumar, Rajeev ;
Anand, R. S. .
PROGRESS IN ARTIFICIAL INTELLIGENCE, 2024, 13 (02) :119-133
[46]   Time-Series Feature Extraction by Return Map Analysis and Its Application to Bearing-Fault Detection [J].
Ponomareva, Veronika ;
Druzhina, Olga ;
Logunov, Oleg ;
Rudnitskaya, Anna ;
Bobrova, Yulia ;
Andreev, Valery ;
Karimov, Timur .
BIG DATA AND COGNITIVE COMPUTING, 2024, 8 (08)
[47]   Study on Fault Feature Extraction of Rolling Bearing Based on Improved WOA-FMD Algorithm [J].
Jia, Guangfei ;
Meng, Yanchao .
SHOCK AND VIBRATION, 2023, 2023
[48]   Fault Detection in Microgrids Using Combined Classification Algorithms and Feature Selection Methods [J].
Ranjbar, S. ;
Jamali, S. .
2019 INTERNATIONAL CONFERENCE ON PROTECTION AND AUTOMATION OF POWER SYSTEM (IPAPS), 2019, :17-21
[49]   A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection [J].
Mohammadzadeh, Hekmat ;
Gharehchopogh, Farhad Soleimanian .
COMPUTATIONAL INTELLIGENCE, 2021, 37 (01) :176-209
[50]   Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings [J].
Luo, Meng ;
Li, Chaoshun ;
Zhang, Xiaoyuan ;
Li, Ruhai ;
An, Xueli .
ISA TRANSACTIONS, 2016, 65 :556-566