Rolling Bearing Fault Diagnosis Method Based on Enhanced Deep Auto-encoder Network

doi:10.3969/j.issn.1004-132X.2021.21.011

Abstract

Abstract: To improve the feature mining capabilities of deep auto-encoder networks and select the network hyperparameters adaptively， an enhanced deep auto-encoder network was proposed for rolling bearing fault diagnosis. Maximum correlation entropy was used to replace mean square error as the loss function of auto-encoder. Sparse penalty term and contractive penalty term embedded with non-negative constraints were added to further reduce the reconstruction errors. Key parameters of the network were adaptively selected through gray wolf optimization algorithm. After experimental analyses， results show that compared with the existing methods， the proposed method has stronger feature extraction ability and stability. For bearing vibration data under variable operating conditions， the proposed method may also achieve high recognition accuracy.

Key words: rolling bearing, fault diagnosis, auto-encoder network, deep learning

摘要： 为了提高深度自编码网络的特征挖掘能力，自适应地选取网络超参数，提出了一种增强深度自编码网络，并将其应用于滚动轴承故障诊断。采用最大相关熵代替均方误差作为自编码器的损失函数，加入稀疏惩罚项和嵌入非负约束因子的收缩惩罚项，进一步减小重构误差；通过灰狼优化算法自适应地选取网络关键参数。实验分析结果表明，与现有方法相比，该方法具有更强的特征提取能力与稳定性，对变工况下的轴承振动数据也能达到较高的识别精度。

关键词: 滚动轴承, 故障诊断, 自编码网络, 深度学习

CLC Number:

TH165.3

TONG Jinyu, LUO Jin, ZHENG Jinde. Rolling Bearing Fault Diagnosis Method Based on Enhanced Deep Auto-encoder Network[J]. China Mechanical Engineering, 2021, 32(21): 2617-2624.

童靳于, 罗金, 郑近德. 基于增强深度自编码网络的滚动轴承故障诊断方法[J]. 中国机械工程, 2021, 32(21): 2617-2624.

References

［1］王兴龙，郑近德，潘海洋，等. 基于MED与自相关谱峭度图的滚动轴承故障诊断方法［J］. 振动与冲击， 2020， 39（18）：118-124.
WANG Xinglong， ZHENG Jinde， PAN Haiyang， et al. Fault Diagnosis Method for Rolling Bearings Based on MED and Autograms［J］. Journal of Vibration and Shock， 2020， 39（18）：118-124.
［2］王振亚，姚立纲. 广义精细复合多尺度样本熵与流形学习相结合的滚动轴承故障诊断方法［J］. 中国机械工程， 2020， 31（20）：2463-2471.
WANG Zhenya， YAO Ligang. Rolling Bearing Fault Diagnosis Method Based on Generalized Refined Composite Multiscale Sample Entropy Combined and Manifold Learning［J］. China Mechanical Engineering， 2020， 31（20）：2463-2471.
［3］郑近德，潘海洋，戚晓利，等. 复合层次模糊熵及其在滚动轴承故障诊断中的应用［J］. 中国机械工程， 2016， 27（15）：2048-2055.
ZHENG Jinde， PAN Haiyang， QI Xiaoli， et al. Composite Hierarchical Fuzzy Entropy and Its Application to Rolling Bearing Fault Diagnosis［J］. China Mechanical Engineering， 2016， 27（15）：2048-2055.
［4］李从志，郑近德，潘海洋，等. 基于精细复合多尺度散布熵与支持向量机的滚动轴承故障诊断方法［J］. 中国机械工程， 2019， 30（14）：1713-1719.
LI Congzhi， ZHENG Jinde， PAN Haiyang， et al. Fault Diagnosis Method of Rolling Bearing Based on Refined Composite Multiscale Dispersion Entropy and Support Vector Machine［J］. China Mechanical Engineering， 2019， 30（14）：1713-1719.
［5］李恒，张氢，秦仙蓉，等. 基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法［J］. 振动与冲击， 2018， 37（19）：124-131.
LI Heng， ZHANG Qing， QIN Xianrong， et al. Fault Diagnosis Method for Rolling Bearings Based on Short-time Fourier Transform and Convolution Neural Network［J］. Journal of Vibration and Shock， 2018， 37（19）：124-131.
［6］李兵，韩睿，何怡刚，等. 改进随机森林算法在电机轴承故障诊断中的应用［J］. 中国电机工程学报， 2020， 40（4）：1310-1319.
LI Bing， HAN Rui， He Yigang， et al. Applications of the Improved Random Forest Algorithm in Fault Diagnosis of Motor Bearings［J］. Proceedings of the Chinese Society for Electrical Engineering， 2020， 40（4）：1310-1319.
［7］张超，陈建军，郭迅. 基于EMD能量熵和支持向量机的齿轮故障诊断方法［J］. 振动与冲击， 2010， 29（10）：216-220.
ZHANG Chao， CHEN Jianjun， GUO Xun. A Gear Fault Diagnosis Method Based on EMD Energy Entropy and SVM［J］. Journal of Vibration and Shock， 2010， 29（10）：216-220.
［8］BENALI 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.
［9］姜洪开，邵海东，李兴球. 基于深度学习的飞行器智能故障诊断方法［J］. 机械工程学报， 2019， 55（7）：27-34.
JIANG Hongkai， SHAO Haidong， LI Xingqiu. Deep Learning Theory with Application in Intelligent Fault Diagnosis of Aircraft［J］. Journal of Mechanical Engineering， 2019， 55（7）：27-34.
［10］JIA F， LEI Y G， GUO L， et al. A Neural Network Constructed by Deep Learning Technique and Its Application to Intelligent Fault Diagnosis of Machines［J］. Neurocomputing， 2018， 272：619-628.
［11］SHAO H D， JIANG H K， WANG F， et al. An Enhancement Deep Feature Fusion Method for Rotating Machinery Fault Diagnosis［J］. Knowledge-Based Systems， 2017， 119：200-220.
［12］SUN W J， SHAO S Y， ZHAO R， et al. A Sparse Auto-encoder-based Deep Neural Network Approach for Induction Motor Faults Classification［J］. Measurement， 2016， 89：171-178.
［13］SHEN C Q， QI Y M， WANG J， et al. An Automatic and Robust Features Learning Method for Rotating Machinery Fault Diagnosis Based on Contractive Autoencoder［J］. Engineering Applications of Artificial Intelligence， 2018， 76：170-184.
［14］TONG J Y， LUO J， PAN H Y， et al. A Novel Cuckoo Search Optimized Deep Auto-encoder Network Based Fault Diagnosis Method for Rolling Bearing［J］. Shock and Vibration， 2020， 2020：8891905.
［15］袁非牛，章琳，史劲亭，等. 自编码神经网络理论及应用综述［J］. 计算机学报， 2019， 42（1）：203-230.
YUAN Feiniu， ZHANG Lin， SHI Jinting， et al. Theories and Application of Auto-encoder Neural Networks：a Literature Survey［J］. Chinese Journal of Computers， 2019， 42（1）：203-230.
［16］SHAO H D， JIANG H K， ZHAO H W， et al. A Novel Deep Autoencoder Feature Learning Method for Rotating Machinery Fault Diagnosis［J］. Mechanical Systems and Signal Processing， 2017， 95：187-204.
［17］LIU Y Q， SUN C， JIANG S D. A Reduced Gaussian Kernel Least-Mean-Square Algorithm for Nonlinear Adaptive Signal Processing［J］. Circuits， Systems， and Signal Processing， 2019， 38：371-394.
［18］SRIVASTAVA N， HINTON G， KRIZHEVSKY A， et al. Dropout：a Simple Way to Prevent Neural Networks from Overfitting［J］. Journal of Machine Learning Research， 2014， 15（1）：1929-1958.
［19］LIU W F， MA T Z， TAO D P， et al. HSAE：a Hessian Regularized Sparse Auto-encoders［J］. Neurocomputing， 2016， 187：59-65.
［20］LIU Y N， FENG X Q， ZHOU Z G. Multimodal Video Classification with Stacked Contractive Autoencoders［J］. Signal Processing， 2015， 120：761-766.
［21］张焱，冯乔琦，黄庆卿，等. 非负自编码网络基于部分特征表示的变工况滚动轴承状态识别［J］. 仪器仪表学报， 2020， 41（4）：77-85.
ZHANG Yan， FENG Qiaoqi， HUANG Qingqing， et al. Rolling Bearing State Recognition under Variable Condition Using Part-based Representation of Nonnegativity Constrained Autoencoder Networks［J］. Chinese Journal of Scientific Instrument， 2020， 41（4）：77-85.
［22］MIRJALILI S， MIRJALILI S M， LEWIS A， et al. Grey Wolf Optimizer［J］. Advances in Engineering Software， 2014， 69：46-61.
［23］ZHENG J D， PAN H Y， LIU Q Y， et al. Refined Time-shift Multiscale Normalised Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing［J］. Physica A：Statal Mechanics and Its Applications， 2020， 545：123641.

[1]	LUO Ji-Sai, XU De-Ge, SHI Mei-Li. Gearbox Fault Diagnosis with Rotating Speed Fluctuation Based on SVD and Chirplet Path Pursuit Algorithm [J]. J4, 201016, 21(16): 1947-1951.
[2]	HU Chao, LIU Zuo-Min. Research on Rolling Bearing’s Heathcote’s Stick-slip Model Based on Materials Compatibility [J]. J4, 201016, 21(16): 1969-1973.
[3]	ZHAO Zhihong, LI Lehao, YANG Shaopu, LI Qing. A Frequency Domain Feature Extraction Auto-encoder and Its Applications on Fault Diagnosis [J]. China Mechanical Engineering, 2021, 32(20): 2468-2474.
[4]	YU Haoshuai, TANG Baoping, ZHANG Kai, TAN Qian, WEI Jing. Fault Diagnosis Method of Wind Turbine Gearboxes Mixed with Attention Prototype Networks under Small Samples [J]. China Mechanical Engineering, 2021, 32(20): 2475-2481.
[5]	YIN Chen, ZHOU Shichao, HE Jianliang, SUN Yuxin, WANG Yulin. Tool Wear Online Recognition Method Based on Multi-source Synchronous Signals and Deep Learning [J]. China Mechanical Engineering, 2021, 32(20): 2482-2491.
[6]	LIU Xiaofeng, TAN Qi, YE Rongting. Ensemble Variable Predictive Model Based on Optimal Features and Its Applications [J]. China Mechanical Engineering, 2021, 32(18): 2153-2158，2164.
[7]	GUO Junchao, ZHEN Dong, MENG Zhaozong, SHI Zhanqun, GU Fengshou. Feature Extraction of Rolling Bearings Based on WAEEMD and MSB [J]. China Mechanical Engineering, 2021, 32(15): 1793-1800.
[8]	SHI Mingkuan, ZHAO Rongzhen. A Fault Diagnosis Method of Rotating Machinery Based on LBDP [J]. China Mechanical Engineering, 2021, 32(14): 1653-1658,1668.
[9]	HE Zhiyi, SHAO Haidong, CHENG Junsheng, YANG Yu, . A Fault Diagnosis Method of Mechanical Equipment Based on FKCH-TM and Thermal Images#br# [J]. China Mechanical Engineering, 2021, 32(12): 1456-1461，1478.
[10]	CHEN Jian, YANG Bin, HUANG Kaixuan, CAI Kunqi, LIU Yuanyuan, LIU Xingfu, . Applications of a Ridgeline Extraction Method in Bearing Fault Diagnosis [J]. China Mechanical Engineering, 2021, 32(10): 1157-1163.
[11]	HE Qiang, TANG Xianghong, LI Chuanjiang, LU Jianguang, CHEN Jiadui. Bearing Fault Diagnosis Method Based on Small Sample Data under Unbalanced Loads#br# [J]. China Mechanical Engineering, 2021, 32(10): 1164-1171,1180.
[12]	ZHENG Jinde, WANG Xinglong, PAN Haiyang, TONG Jinyu, LIU Qingyun. Rolling Bearing Fault Diagnosis Method Based on Adaptive Autogram [J]. China Mechanical Engineering, 2021, 32(07): 778-785，792.
[13]	CHEN Huanguo, LIU Peijun, YU Hang, XIAO Xue. Identification of Fault Degree of Oil Filter Blockage in Electro-hydraulic Actuator Based on an Improved PCA-SOM [J]. China Mechanical Engineering, 2021, 32(07): 799-805.
[14]	DING Jiaxin ;WANG Zhenya ;YAO Ligang ;CAI Yongwu. Rolling Bearing Fault Diagnosis Based on GCMWPE and Parameter Optimization SVM [J]. China Mechanical Engineering, 2021, 32(02): 147-155.
[15]	YANG Luhang1;LI Baoqing1;WANG Ping2,3;WANG Jian2,3;YANG Yu1. Fault Diagnosis Method of Rolling Bearings Based on Probability Output Flexible Convex Hull [J]. China Mechanical Engineering, 2021, 32(01): 40-46.