Research on Rotating Machinery Fault Diagnosis Method Based on Energy Spectrum Matrix and Adaptive Convolutional Neural Network | |
Liu YY(刘意杨)4,5,6; Yang YS(杨友胜)3,5; Feng TY(冯铁)2; Sun Y(孙懿)1,5; Zhang XJ(张雪健)2 | |
刊名 | PROCESSES |
2021 | |
卷号 | 9期号:1页码:1-25 |
关键词 | hierarchical fault diagnosis energy spectrum matrix dynamic adjustment of the learning rate convolutional neural network rotating machinery |
ISSN号 | 2227-9717 |
产权排序 | 1 |
英文摘要 | Traditional intelligent fault diagnosis methods focus on distinguishing different fault modes, but ignore the deterioration of fault severity. This paper proposes a new two-stage hierarchical convolutional neural network for fault diagnosis of rotating machinery bearings. The failure mode and failure severity are modeled as a hierarchical structure. First, the original vibration signal is transformed into an energy spectrum matrix containing fault-related information through wavelet packet decomposition. Secondly, in the model training method, an adaptive learning rate dynamic adjustment strategy is further proposed, which adaptively extracts robust features from the spectrum matrix for fault mode and severity diagnosis. To verify the effectiveness of the method, the bearing fault data was collected using a rotating machine test bench. On this basis, the diagnostic accuracy, convergence performance and robustness of the model under different signal-to-noise ratios and variable load environments are evaluated, and the feature learning ability of the method is verified by visual analysis. Experimental results show that this method has achieved satisfactory results in both fault pattern recognition and fault severity evaluation, and is superior to other machine learning and deep learning methods. |
资助项目 | Revitalizing Liaoning Outstanding Talents Project[XLYC1907057] ; National Nature Science Foundation of China[U1908212] ; Key Project of Natural Science Foundation of China[61533015] ; National Key R&D Program of China[2018YFB1700200] |
WOS研究方向 | Engineering |
语种 | 英语 |
WOS记录号 | WOS:000610731100001 |
资助机构 | Revitalizing Liaoning Outstanding Talents Project [XLYC1907057] ; National Nature Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [U1908212] ; Key Project of Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61533015] ; National Key R&D Program of China [2018YFB1700200] |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/28315] |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | Liu YY(刘意杨) |
作者单位 | 1.Information and Control Engineering Department, Shenyang Jianzhu University, Shenyang 110168, China 2.Industrial Engineering Department, XIOLIFT, Hangzhou 311199, China 3.College of Information Science and Engineering, Northeastern University, Shenyang 110819, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 5.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 6.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China |
推荐引用方式 GB/T 7714 | Liu YY,Yang YS,Feng TY,et al. Research on Rotating Machinery Fault Diagnosis Method Based on Energy Spectrum Matrix and Adaptive Convolutional Neural Network[J]. PROCESSES,2021,9(1):1-25. |
APA | Liu YY,Yang YS,Feng TY,Sun Y,&Zhang XJ.(2021).Research on Rotating Machinery Fault Diagnosis Method Based on Energy Spectrum Matrix and Adaptive Convolutional Neural Network.PROCESSES,9(1),1-25. |
MLA | Liu YY,et al."Research on Rotating Machinery Fault Diagnosis Method Based on Energy Spectrum Matrix and Adaptive Convolutional Neural Network".PROCESSES 9.1(2021):1-25. |
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