Deep neural network based classification of rolling element bearings and health degradation through comprehensive vibration signal analysis

doi:10.23919/JSEE.2022.000023

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (1): 233-246.doi: 10.23919/JSEE.2022.000023

• RELIABILITY • Previous Articles

Deep neural network based classification of rolling element bearings and health degradation through comprehensive vibration signal analysis

Kwame Bensah KULEVOME Delanyo^1,², Hong WANG^1,^2,*(), Xuegang WANG¹()

¹ School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
² Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China

Received:2020-11-10 Accepted:2021-11-25 Online:2022-01-18 Published:2022-02-22
Contact: Hong WANG E-mail:hongw@uestc.edu.cn;xgwang@uestc.edu.cn
About author:|KULEVOME Delanyo Kwame Bensah was born in 1983. He received his B.Eng. degree from Accra Institute of Technology and M.Eng. degree in electronic science and technology in 2019 from University of Electronic Science and Technology of China, Chengdu, China, where he is currently pursuing his Ph.D. degree in information and communication engineering. His research interests include prognostics and health management of systems, signal processing, and deep learning. E-mail: kdelanyo@ieee.org||WANG Hong was born in 1974. He received his B.S., M.S., and Ph.D. degrees from Northwestern Polytechnical University, Chongqing University, and University of Electronic Science and Technology of China (UESTC), respectively. He has been a faculty member with UESTC since 2003. From 2007 to 2009 he was engaged in doctoral research with the Second Research Institute of Civil Aviation Administration. From 2009 to 2010 he was a research scholar with Polytechnic Institute of New York University and a research assistant with New Jersey Institute of Technology, USA. His present areas of interest include radar signal processing, avionics, aeronautical telecommunication, and surveillance technologies in ATC. E-mail: hongw@uestc.edu.cn||WANG Xuegang was born in 1962. He received his Ph.D. degree from Xidian University in 1992. He is now a professor and Ph.D. supervisor with University of Electronic Science and Technology of China. His research interests include radar signal processing, and millimeter wave radar. E-mail: xgwang@uestc.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (42027805), National Aeronautical Fund (ASFC-2017 2080005) and National Key R&D Program of China (2017YFC03 07100)

Abstract

Abstract:

Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry. The likelihood of failure has the propensity of increasing under prolonged operation and varying working conditions. Hence, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating machinery. The variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault features. In this paper, the efficacy and the leverage of a pre-trained convolutional neural network (CNN) is harnessed in the implementation of a robust fault classification model. In the absence of sufficient data, this method has a high-performance rate. Initially, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a classifier. The raw vibration data are strategically segmented and transformed into two representations which are trained separately and jointly. The proposed approach is carried out on bearing vibration data and shows high-performance results. In addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator (HI) under varying operating conditions for a given fault condition.

Key words: bearing failure, deep neural network, fault classification, health indicator, prognostics and health management

Kwame Bensah KULEVOME Delanyo, Hong WANG, Xuegang WANG. Deep neural network based classification of rolling element bearings and health degradation through comprehensive vibration signal analysis[J]. Journal of Systems Engineering and Electronics, 2022, 33(1): 233-246.

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Parameter	Value
Type	6205-2RS JEM SKF
Inside diameter/mm	25
Outside diameter/mm	52
Thickness/mm	15
Ball diameter/mm	8
Pitch diameter/mm	34.35
Fatigue load/kN	0.335

Bearing operating condition	Length of data
Bearing operating condition	0 hp	3 hp
Normal	240 000	480 000
Ball, inner, and outer race fault	120 000	120 000

Bearing operating condition	Diameter of faults/ mm	Size of segment				Class label
		Training		Validation and testing
		0 hp	3 hp	0 hp	3 hp
Normal	0	480	960	120	240	1
Ball	0.18	240	240	60	60	2
Ball	0.36	240	240	60	60	3
Ball	0.54	240	240	60	60	4
Inner race	0.18	240	240	60	60	5
Inner race	0.36	240	240	60	60	6
Inner race	0.54	240	240	60	60	7
Outer race	0.18	240	240	60	60	8
Outer race	0.36	240	240	60	60	9
Outer race	0.54	240	240	60	60	10

Parameter	Value
Sampling frequency/kHz	12
Window type	Hamming
Overlap length/%	50
Number of DFT points	256
Filter bank	Triangular
Number of filters	32
Window length	128

Load	Input data	Average accuracy/%		Average time/ (epoch/s)	Epoch for best model
Load	Input data	Train	Test	Average time/ (epoch/s)	Epoch for best model
0 hp	Raw signal	91.26	89.52	35	16
	Spectrogram	99.77	97.88	30	15
	Mel spectrogram	99.98	98.79	27	16
3 hp	Raw signal	98.27	91.03	41	34
	Spectrogram	100	98.97	38	34
	Mel Spectrogram	100	99.23	35	25

Deep neural network based classification of rolling element bearings and health degradation through comprehensive vibration signal analysis

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Deep learning method	Average testing accuracy/%
Ensemble DAE	97.18
FCNN	98.8
Normalized CNN	98.5
MSCNN-LSTM	98.46
The proposed method	99.23

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