Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO

被引：105
作者：
Shao Haidong [1 ,2 ]
Ding Ziyang [1 ]
Cheng Junsheng [1 ]
Jiang Hongkai [3 ]
机构：
[1] Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China
[2] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden
[3] Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Peoples R China
来源：
ISA TRANSACTIONS | 2020年 / 105卷
基金：
中国国家自然科学基金;
关键词：
Intelligent fault diagnosis; Different rotating machines; Novel stacked transfer auto-encoder; Parameter transfer strategy; Particle swarm optimization; PARTICLE SWARM OPTIMIZATION; NEURAL-NETWORK; AUTOENCODER; ALGORITHM; ENTROPY; VOLTAGE; SYSTEM;
D O I：
10.1016/j.isatra.2020.05.041
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Intelligent fault diagnosis techniques cross rotating machines have great significances in theory and engineering For this purpose, this paper presents a novel method using novel stacked transfer auto-encoder (NSTAE) optimized by particle swarm optimization (PSO). First, novel stacked auto-encoder (NSAE) model is designed with scaled exponential linear unit (SELU), correntropy and nonnegative constraint. Then, NSTAE is constructed using NSAE and parameter transfer strategy to enable the pre-trained source-domain NSAE to adapt to the target-domain samples. Finally, PSO is used to flexibly decide the hyperparameters of NSTAE. The effectiveness and superiority of the presented method are investigated through analyzing the collected experimental data of bearings and gears from different rotating machines. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：308 / 319
页数：12
相关论文
共 55 条
[41]   A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings [J].
Sun, Meidi ;
Wang, Hui ;
Liu, Ping ;
Huang, Shoudao ;
Fan, Peng .
MEASUREMENT, 2019, 146 :305-314
[42]   Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis [J].
Tang, Shenghao ;
Shen, Changqing ;
Wang, Dong ;
Li, Shuang ;
Huang, Weiguo ;
Zhu, Zhongkui .
NEUROCOMPUTING, 2018, 305 :1-14
[43]   Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection [J].
Theissler, Andreas .
KNOWLEDGE-BASED SYSTEMS, 2017, 123 :163-173
[44]   Fouling resistance prediction based on GA-Elman neural network for circulating cooling water with electromagnetic anti-fouling treatment [J].
Wang, Jianguo ;
Lv, Zhe ;
Liang, Yandong ;
Deng, Lijuan ;
Li, Zhiwei .
JOURNAL OF THE ENERGY INSTITUTE, 2019, 92 (05) :1519-1526
[45]   Minimum entropy deconvolution based on simulation-determined band pass filter to detect faults in axial piston pump bearings [J].
Wang, Shuhui ;
Xiang, Jiawei ;
Tang, Hesheng ;
Liu, Xiaoyang ;
Zhong, Yongteng .
ISA TRANSACTIONS, 2019, 88 :186-198
[46]   cPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks [J].
Wang, Yulong ;
Zhang, Haoxin ;
Zhang, Guangwei .
SWARM AND EVOLUTIONARY COMPUTATION, 2019, 49 :114-123
[47]   Fault detection and diagnosis based on transfer learning for multimode chemical processes [J].
Wu, Hao ;
Zhao, Jinsong .
COMPUTERS & CHEMICAL ENGINEERING, 2020, 135
[48]   An adaptive deep transfer learning method for bearing fault diagnosis [J].
Wu, Zhenghong ;
Jiang, Hongkai ;
Zhao, Ke ;
Li, Xingqiu .
MEASUREMENT, 2020, 151
[49]   ECG signal enhancement based on improved denoising auto-encoder [J].
Xiong, Peng ;
Wang, Hongrui ;
Liu, Ming ;
Zhou, Suiping ;
Hou, Zengguang ;
Liu, Xiuling .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2016, 52 :194-202
[50]   Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions [J].
Yan, Xiaoan ;
Liu, Ying ;
Jia, Minping .
KNOWLEDGE-BASED SYSTEMS, 2020, 193
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