Multistate fault diagnosis strategy for bearings based on an improved convolutional sparse coding with priori periodic filter group
[Display omitted] •A multistate fault diagnosis model for bearings based on PPFG/ICSC is proposed.•The construction and parameter selection method of PPFG is proposed.•Multistate fault components can be extracted by the PPFG/ICSC.•Experimental studies on bearing multistate fault signals are carried...
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| Veröffentlicht in: | Mechanical systems and signal processing Jg. 188; S. 109995 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
01.04.2023
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| ISSN: | 0888-3270 |
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| Abstract | [Display omitted]
•A multistate fault diagnosis model for bearings based on PPFG/ICSC is proposed.•The construction and parameter selection method of PPFG is proposed.•Multistate fault components can be extracted by the PPFG/ICSC.•Experimental studies on bearing multistate fault signals are carried out.
Bearings are a critical component of rotating machines; when they fail, critical equipment becomes unavailable, damage may occur beyond the bearing itself, and safety concerns arise. Determining that a bearing structure is compromised before catastrophic failure permits the protection of plant, people, and productivity. When bearings malfunction, the features of single and multiple faults are masked and accompanied by noise and other signal degrading artifacts affecting the signals from the vibrational sensors. In these circumstances, detection and diagnosis of multistate bearing faults is difficult. To overcome these challenges, an improved convolutional sparse coding (ICSC) model, based on a priori periodic filter groups (PPFG), is proposed to respond to the multistate fault problems of bearings. A Laplace wavelet is constructed with one-sided decay related to the vibration pattern of the signal. The best-matched wavelet is optimally determined by correlation analysis of the signal frequency domain parameters and the time domain damping parameters. The best-matched wavelet and the kurtosis criterion are used to construct a PPFG based on the theoretical period of the fault. The ICSC based on the PPFG obtains mapping coefficients characterizing different vibrational features of the signal. The envelope spectrum analysis of the various mapping coefficients identifies and confirms the fault-revealing components in the multistate signal. The ICSC results have a relatively good sparse time domain, and the fault-identifying features in the envelope spectrum are enhanced. Multiple faults can be easily identified. The effectiveness and robustness of the PPFG/ICSC are demonstrated through a complete experimental analysis of simulated, single-fault, and multifault signals, as well as a comparative analysis of the previous methods – Fast SK, CBPDN, and VMD-ICA – which verifies that the PPFG/ICSC is more robust, accurate, and efficient than the previous methods. |
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| AbstractList | [Display omitted]
•A multistate fault diagnosis model for bearings based on PPFG/ICSC is proposed.•The construction and parameter selection method of PPFG is proposed.•Multistate fault components can be extracted by the PPFG/ICSC.•Experimental studies on bearing multistate fault signals are carried out.
Bearings are a critical component of rotating machines; when they fail, critical equipment becomes unavailable, damage may occur beyond the bearing itself, and safety concerns arise. Determining that a bearing structure is compromised before catastrophic failure permits the protection of plant, people, and productivity. When bearings malfunction, the features of single and multiple faults are masked and accompanied by noise and other signal degrading artifacts affecting the signals from the vibrational sensors. In these circumstances, detection and diagnosis of multistate bearing faults is difficult. To overcome these challenges, an improved convolutional sparse coding (ICSC) model, based on a priori periodic filter groups (PPFG), is proposed to respond to the multistate fault problems of bearings. A Laplace wavelet is constructed with one-sided decay related to the vibration pattern of the signal. The best-matched wavelet is optimally determined by correlation analysis of the signal frequency domain parameters and the time domain damping parameters. The best-matched wavelet and the kurtosis criterion are used to construct a PPFG based on the theoretical period of the fault. The ICSC based on the PPFG obtains mapping coefficients characterizing different vibrational features of the signal. The envelope spectrum analysis of the various mapping coefficients identifies and confirms the fault-revealing components in the multistate signal. The ICSC results have a relatively good sparse time domain, and the fault-identifying features in the envelope spectrum are enhanced. Multiple faults can be easily identified. The effectiveness and robustness of the PPFG/ICSC are demonstrated through a complete experimental analysis of simulated, single-fault, and multifault signals, as well as a comparative analysis of the previous methods – Fast SK, CBPDN, and VMD-ICA – which verifies that the PPFG/ICSC is more robust, accurate, and efficient than the previous methods. |
| ArticleNumber | 109995 |
| Author | Lu, Wei Wang, Huaqing Song, Liuyang Cui, Lingli Han, Changkun |
| Author_xml | – sequence: 1 givenname: Changkun surname: Han fullname: Han, Changkun organization: College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China – sequence: 2 givenname: Wei surname: Lu fullname: Lu, Wei organization: Institute of Engineering Technology, Sinopec Catalyst Company Limited, Tongzhou District, Beijing 101100, China – sequence: 3 givenname: Huaqing orcidid: 0000-0001-5333-0829 surname: Wang fullname: Wang, Huaqing email: hqwang@mail.buct.edu.cn organization: College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China – sequence: 4 givenname: Liuyang surname: Song fullname: Song, Liuyang email: xq_0703@163.com organization: Key Laboratory of Health Monitoring and Self-recovery for High-end Mechanical Equipment, Beijing University of Chemical Technology, Beijing 100029, China – sequence: 5 givenname: Lingli surname: Cui fullname: Cui, Lingli organization: Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China |
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| Title | Multistate fault diagnosis strategy for bearings based on an improved convolutional sparse coding with priori periodic filter group |
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