GA-OIHF Elman Neural Network Algorithm for Fault Diagnosis of Full Life Cycle of Rolling Bearing

doi:10.16183/j.cnki.jsjtu.2020.157

Abstract

Abstract:

For the fault diagnosis needs of the full life cycle (light degradation, moderate degradation, and severe degradation) of rolling bearing under the environment of high background noise, a genetic algorithm-output input hidden feedback (GA-OIHF ) Elman neural network model is proposed to achieve precise diagnosis of the degradation faults of rolling bearing. Ensemble empirical mode decomposition (EEMD) is selected to effectively reduce the noise and extract fault features of the vibration signal. An OIHF Elman neural network is designed by increasing the feedbacks from the output layer to the hidden layer and the input layer based on the Elman neural network, thus further improves its ability to process full life cycle data of rolling bearing. Then, a novel GA-OIHF Elman neural network model is developed by combining the genetic algorithm (GA) and the OIHF Elman neural network. The novel GA-OIHF Elman neural network model combines the global optimization of GA and the local optimization ability of the OIHF Elman neural network to achieve an accurate fault diagnosis of the entire life cycle of rolling bearing. The experimental results show that the GA-OIHF Elman algorithm model can not only accurately diagnose the fault in the full life cycle of rolling bearing, but also ensure the stability of the diagnosis model for different faults including different fault components and stages.

Key words: rolling bearing, genetic algorithm, Elman neural network, ensemble empirical mode decomposition (EEMD), full life cycle

CLC Number:

TH17

ZHUO Pengcheng, YAN Jin, ZHENG Meimei, XIA Tangbin, XI Lifeng. GA-OIHF Elman Neural Network Algorithm for Fault Diagnosis of Full Life Cycle of Rolling Bearing[J]. Journal of Shanghai Jiao Tong University, 2021, 55(10): 1255-1262.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Tab.1

Average diagnostic results of SVM and four other neural networks in test set

诊断模型	$MSE ¯$	STD
SVM	0.0804	0.0978
Elman	0.0609	0.0868
OHF Elman	0.0204	0.0582
OIF Elman	0.0201	0.0616
OIHF Elman	0.0131	0.0470

Tab.1

Tab.2

Diagnostic results of OIHF Elman neural network of three evolutionary algorithms

诊断模型	$MSE ¯$	STD
OIHF Elman	0.0131	0.0470
GA-OIHF Elman	0.0100	0.0403
PSO-OIHF Elman	0.0114	0.0438
MEA-OIHF Elman	0.0115	0.0423

Tab.2

Tab.3

MSE of full life cycle fault diagnosis of GA-OIHF Elman neural network model

故障状态与故障部件	神经网络类型	$MSE ¯$
正常	OIHF Elman	3.21×10^-14
正常	GA-OIHF Elman	0.0087
轻度退化	OIHF Elman	0.0022
轻度退化	GA-OIHF Elman	0.0043
中度退化	OIHF Elman	0.0316
中度退化	GA-OIHF Elman	0.0186
重度退化	OIHF Elman	0.0069
重度退化	GA-OIHF Elman	0.0036
滚动体	OIHF Elman	0.0061
滚动体	GA-OIHF Elman	0.0079
内圈	OIHF Elman	0.0113
内圈	GA-OIHF Elman	0.0039
外圈	OIHF Elman	0.0270
外圈	GA-OIHF Elman	0.0164

Tab.3

Tab.4

Diagnostic MSE of GA-OIHF Elman in different stages of three components

退化故障阶段	故障部件	$MSE ¯$
轻度退化	内圈	1.36×10^-15
	外圈	2.97×10^-9
	滚动体	0.0101
中度退化	内圈	1.35×10^-178
	外圈	0.0319
	滚动体	0.0111
重度退化	内圈	0.0101
	外圈	2.05×10^-13
	滚动体	1.24×10^-31

Tab.4

References 19

[1]	HUANG W T, SUN H J, LUO J N, et al. Periodic feature oriented adapted dictionary free OMP for rolling element bearing incipient fault diagnosis[J]. Mechanical Systems and Signal Processing, 2019, 126:137-160. doi: 10.1016/j.ymssp.2019.02.023 URL
[2]	XIA T B, DONG Y F, XIAO L, et al. Recent advances in prognostics and health management for advanced manufacturing paradigms[J]. Reliability Engineering & System Safety, 2018, 178:255-268. doi: 10.1016/j.ress.2018.06.021 URL
[3]	XIA T B, SONG Y, ZHENG Y, et al. An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation[J]. Computers in Industry, 2020, 115:103182. doi: 10.1016/j.compind.2019.103182 URL
[4]	SAARI J, STRÖMBERGSSON D, LUNDBERG J, et al. Detection and identification of windmill bearing faults using a one-class support vector machine (SVM)[J]. Measurement, 2019, 137:287-301. doi: 10.1016/j.measurement.2019.01.020 URL
[5]	陈玉昇, 杨燕华, 林萌, 等. 基于深度学习神经网络的核电厂故障诊断技术[J]. 上海交通大学学报, 2018, 52(Sup.1):58-61.
	CHEN Yusheng, YANG Yanhua, LIN Meng, et al. Fault diagnosis technology of nuclear power plant based on deep learning neural network[J]. Journal of Shanghai Jiao Tong University, 2018, 52(Sup.1):58-61.
[6]	BEN ALI J, FNAIECH N, SAIDI L, et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals[J]. Applied Acoustics, 2015, 89:16-27. doi: 10.1016/j.apacoust.2014.08.016 URL
[7]	LI L, HUANG Y X, TAO J F, et al. Featured temporal segmentation method and AdaBoost-BP detector for internal leakage evaluation of a hydraulic cylinder[J]. Measurement, 2018, 130:279-289. doi: 10.1016/j.measurement.2018.08.029 URL
[8]	汤宝平, 习建民, 李锋. 基于Elman神经网络的旋转机械故障诊断[J]. 计算机集成制造系统, 2010, 16(10):2148-2152.
	TANG Baoping, XI Jianmin, LI Feng. Fault diagnosis for rotating machinery based on Elman neural network[J]. Computer Integrated Manufacturing Systems, 2010, 16(10):2148-2152.
[9]	SHI X H, LIANG Y C, LEE H P, et al. Improved Elman networks and applications for controlling ultrasonic motors[J]. Applied Artificial Intelligence, 2004, 18(7):603-629. doi: 10.1080/08839510490483279 URL
[10]	ZHENG K, YANG D W, ZHANG B, et al. A group sparse representation method in frequency domain with adaptive parameters optimization of detecting incipient rolling bearing fault[J]. Journal of Sound and Vibration, 2019, 462:114931. doi: 10.1016/j.jsv.2019.114931 URL
[11]	LEI Y G, QIAO Z J, XU X F, et al. An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2017, 94:148-164. doi: 10.1016/j.ymssp.2017.02.041 URL
[12]	LI H, LIU T, WU X, et al. Application of EEMD and improved frequency band entropy in bearing fault feature extraction[J]. ISA Transactions, 2019, 88:170-185. doi: 10.1016/j.isatra.2018.12.002 URL
[13]	HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971):903-995. doi: 10.1098/rspa.1998.0193 URL
[14]	WANG Z W, ZHANG Q H, XIONG J B, et al. Fault diagnosis of a rolling bearing using wavelet packet denoising and random forests[J]. IEEE Sensors Journal, 2017, 17(17):5581-5588. doi: 10.1109/JSEN.2017.2726011 URL
[15]	AMIRAT Y, BENBOUZID M E H, WANG T, et al. EEMD-based notch filter for induction machine bearing faults detection[J]. Applied Acoustics, 2018, 133:202-209. doi: 10.1016/j.apacoust.2017.12.030 URL
[16]	刘涛, 杜世昌, 黄德林, 等. 基于改进的集合经验模态方法振动信号分解[J]. 上海交通大学学报, 2016, 50(9):1452-1459.
	LIU Tao, DU Shichang, HUANG Delin, et al. Vibration signal decomposition based on an improved ensemble empirical mode decomposition method[J]. Journal of Shanghai Jiao Tong University, 2016, 50(9):1452-1459.
[17]	FU Q, JING B, HE P J, et al. Fault feature selection and diagnosis of rolling bearings based on EEMD and optimized Elman_AdaBoost algorithm[J]. IEEE Sensors Journal, 2018, 18(12):5024-5034. doi: 10.1109/JSEN.2018.2830109 URL
[18]	GUO H X, LI Y J, LI Y N, et al. BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification[J]. Engineering Applications of Artificial Intelligence, 2016, 49:176-193. doi: 10.1016/j.engappai.2015.09.011 URL
[19]	LIU H, TIAN H Q, LIANG X F, et al. New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks[J]. Renewable Energy, 2015, 83:1066-1075. doi: 10.1016/j.renene.2015.06.004 URL