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Pei D, Yue J, Jiao J. Fuzzy Entropy-Assisted Deconvolution Method and Its Application for Bearing Fault Diagnosis. ENTROPY (BASEL, SWITZERLAND) 2024; 26:304. [PMID: 38667858 PMCID: PMC11049568 DOI: 10.3390/e26040304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/19/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024]
Abstract
Vibration signal analysis is an important means for bearing fault diagnosis. Affected by the vibration of other machine parts, external noise and the vibration transmission path, the impulses induced by a bearing defect in the measured vibrations are very weak. Blind deconvolution (BD) methods can counteract the effect of the transmission path and enhance the fault impulses. Most BD methods highlight fault features of the filtered signals by impulse-featured objective functions (OFs). However, residual noise in the filtered signals has not been well tackled. To overcome this problem, a fuzzy entropy-assisted deconvolution (FEAD) method is proposed. First, FEAD takes advantage of the high noise sensitivity of fuzzy entropy (FuzzyEn) and constructs a weighted FuzzyEn-kurtosis OF to enhance the fault impulses while suppressing noise interference. Then, the PSO algorithm is used to iteratively solve the optimal inverse deconvolution filter. Finally, envelope spectrum analysis is performed on the filtered signal to realize bearing fault diagnosis. The feasibility of FEAD was first verified by the bearing fault simulation signals at constant and variable speeds. The bearing test signals from Case Western Reserve University (CWRU), the railway wheelset and the test bench validated the good performance of FEAD in fault feature enhancement. A comparison with and quantitative results for the other state-of-the-art BD methods indicated the superiority of the proposed method.
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Affiliation(s)
- Di Pei
- School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China;
| | - Jianhai Yue
- School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China;
| | - Jing Jiao
- Locomotive & Car Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China;
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Wang Q, Sun Z, Zhu Y, Song C, Li D. Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:19963-19982. [PMID: 38052632 DOI: 10.3934/mbe.2023884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
As an essential component of mechanical equipment, the fault diagnosis of rolling bearings may not only guarantee the systematic operation of the equipment, but also minimize any financial losses caused by equipment shutdowns. Fault diagnosis algorithms based on convolutional neural networks (CNN) have been widely used. However, traditional CNNs have limited feature representation capabilities, thereby making it challenging to determine their hyperparameters. This paper proposes a fault diagnosis method that combines a 1D-CNN with an attention mechanism and hyperparameter optimization to overcome the aforementioned limitations; this method improves the search speed for optimal hyperparameters of CNN models, improves the diagnostic accuracy, and enhances the representation of fault feature information in CNNs. First, the 1D-CNN is improved by combining it with an attention mechanism to enhance the fault feature information. Second, a swarm intelligence algorithm based on Differential Evolution (DE) and Grey Wolf Optimization (GWO) is proposed, which not only improves the convergence accuracy, but also increases the search efficiency. Finally, the improved 1D-CNN alongside hyperparameters optimization are used to diagnose the faults of rolling bearings. By using the Case Western Reserve University (CWRU) and Jiangnan University (JNU) datasets, when compared to other common diagnosis models, the results demonstrate the usefulness and dependability of the DE-GWO-CNN algorithm in fault diagnosis applications by demonstrating the increased diagnostic accuracy and superior anti-noise capabilities of the proposed method. The fault diagnosis methodology presented in this paper can accurately identify faults and provide dependable fault classification, thereby assisting technicians in promptly resolving faults and minimizing equipment failures and operational instabilities.
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Affiliation(s)
- Qiushi Wang
- Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
- Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
| | - Zhicheng Sun
- Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
- Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
| | - Yueming Zhu
- Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
- Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
| | - Chunhe Song
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China No.114 Nanta Street, Shenyang, Liaoning Province, China
- Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Dong Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China No.114 Nanta Street, Shenyang, Liaoning Province, China
- Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
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