Compound fault prediction of rolling bearing using multimedia data

被引:23
作者
Singh, Sandip Kumar [1 ]
Kumar, Sandeep [1 ]
Dwivedi, J. P. [1 ]
机构
[1] Indian Inst Technol BHU, Dept Mech Engn, Varanasi, Uttar Pradesh, India
关键词
Multimedia signals; Ensemble empirical mode distribution; Intrinsic mode functions; Independent component analysis; Convolution neural network; Combined mode functions; Artificial neural networks; Compound faults; EMPIRICAL MODE DECOMPOSITION; WAVELET TRANSFORM; DIAGNOSIS; MACHINE;
D O I
10.1007/s11042-017-4419-1
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Catastrophic failure of mechanical systems due to faults occurring on rolling bearing is still a great challenge. These faults, which are of multiple type, are compounded in nature. Vibration analysis of multimedia signals is one of the most effective techniques for the health monitoring of these bearings. A compound fault signal usually consists of multiple characteristic signals and strong confusion noise, which makes it a tough task to separate weak fault signals from them. To resolve the compound fault diagnosis problem of rolling bearings byseparation of multimedia signals' (obtained from acoustic or acceleration sensors), ensemble empirical mode decomposition (EEMD) method along with some classifier (like independent component analysis (ICA) technique) has been used to some degree of success. But, they are not found capable of detecting difficult faults existing on small balls of the bearing. In order to solve this problem, we are going to propose a new method based on use of Combined Mode Functions (CMF) for selecting the intrinsic mode functions(IMFs) instead of the maximum cross correlation coefficient based EEMD technique, sandwiched with, Convolution Neural Networks (CNN), which are deep neural nets, used as fault classifiers. This composite CNN-CMF-EEMD methodovercomes the deficiencies of other approaches, such as the inability to learn the complex non-linear relationships in fault diagnosis issues and fine compound faults like those occurring on small balls of the bearing. The difficult compound faults can be separated effectively by executing CNN-CMF-EEMD method, which makes the fault features more easily extracted and more clearly identified. Experimental results reinforce the effectiveness of using CNN-CMF--EEMD technique for fine compound faults. A comparison of CNN-CMF-EEMD with Artificial Neural Networks (ANN) based ANN-CMF-EEMD shows the capability of CNN as a powerful classifier in the domain of compound fault features of rolling bearing.
引用
收藏
页码:18771 / 18788
页数:18
相关论文
共 30 条
[1]  
[Anonymous], INT C ICICI BME
[2]  
[Anonymous], ROLLING BEARING VIBR
[3]  
[Anonymous], P IEEE INT S IND EL
[4]  
Arifianto D., 2011, 2011 2nd International Conference on Instrumentation Control and Automation (ICA 2011), P274, DOI 10.1109/ICA.2011.6130171
[5]   Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear faults in induction motor [J].
Ballal, Makarand S. ;
Khan, Zafar J. ;
Suryawanshi, Hiralal M. ;
Sonolikar, Ram L. .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2007, 54 (01) :250-258
[6]   Early fault diagnosis of rotating machinery based on wavelet packets-Empirical mode decomposition feature extraction and neural network [J].
Bin, G. F. ;
Gao, J. J. ;
Li, X. J. ;
Dhillon, B. S. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2012, 27 :696-711
[7]  
Chen WY, 2012, 2012 XXTH INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES (ICEM), P2390, DOI 10.1109/ICElMach.2012.6350218
[8]   Deep, Big, Simple Neural Nets for Handwritten Digit Recognition [J].
Ciresan, Dan Claudiu ;
Meier, Ueli ;
Gambardella, Luca Maria ;
Schmidhuber, Juergen .
NEURAL COMPUTATION, 2010, 22 (12) :3207-3220
[9]   Rotating machine fault diagnosis using empirical mode decomposition [J].
Gao, Q. ;
Duan, C. ;
Fan, H. ;
Meng, Q. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2008, 22 (05) :1072-1081
[10]   A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis [J].
Grasso, M. ;
Chatterton, S. ;
Pennacchi, P. ;
Colosimo, B. M. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2016, 81 :126-147