Improved Optimized Minimum Generalized L<sub>p</sub>/L<sub>q</sub> Deconvolution and Application to Bearing Fault Detection
Locating the fault-induced cyclic impulses from corrupted vibration signals is a key step in detecting bearing fault characteristics. Recently, a novel deconvolution technique named the optimized minimum generalized L<sub>p</sub>/L<sub>q</sub> deconvolution (OMGD) was propose...
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MDPI AG
2025-03-01
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| author | Na Yang Zhigang Pan Yuanbo Xu |
| author_facet | Na Yang Zhigang Pan Yuanbo Xu |
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| description | Locating the fault-induced cyclic impulses from corrupted vibration signals is a key step in detecting bearing fault characteristics. Recently, a novel deconvolution technique named the optimized minimum generalized L<sub>p</sub>/L<sub>q</sub> deconvolution (OMGD) was proposed and has been validated as a useful technique to highlight the periodic impulses related to bearing faults. However, the performance of the OMGD is associated with the appropriate selection of prior parameters, such as the filter length. In addition, the OMGD faces edge effect issues, leading to a shorter duration of the enhanced signal when compared to the measured signal. To overcome the shortcomings of the OMGD, this study proposes an improved version, termed the IOMGD. The enhanced technique employs an advanced sparrow search algorithm to automatically ascertain the filter length, doing away with the need for a predetermined fixed value. To solve the problem of the edge effect, a data extension technique based on the autoregressive model (AR-DET) is proposed to adaptively recover the length of the filtered signal to match that of the raw signal based on the properties observed at the filtered signal. The IOMGD’s superiority over the original OMGD has been substantiated by its performance on various real-world bearing fault datasets. Furthermore, a comparative analysis is performed between the IOMGD and other commonly used bearing fault diagnosis methods, revealing the superiority of the IOMGD. |
| format | Article |
| id | doaj-art-96dd25fbcc7e4ce69f148bc68cf3f2dd |
| institution | OA Journals |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-03-01 |
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| series | Machines |
| spelling | doaj-art-96dd25fbcc7e4ce69f148bc68cf3f2dd2025-08-20T02:18:03ZengMDPI AGMachines2075-17022025-03-0113427010.3390/machines13040270Improved Optimized Minimum Generalized L<sub>p</sub>/L<sub>q</sub> Deconvolution and Application to Bearing Fault DetectionNa Yang0Zhigang Pan1Yuanbo Xu2School of Computer Science, Xijing University, Xi’an 710123, ChinaSchool of Automation, Xi’an University of Posts and Telecommunications, Xi’an 710121, ChinaSchool of Automation, Xi’an University of Posts and Telecommunications, Xi’an 710121, ChinaLocating the fault-induced cyclic impulses from corrupted vibration signals is a key step in detecting bearing fault characteristics. Recently, a novel deconvolution technique named the optimized minimum generalized L<sub>p</sub>/L<sub>q</sub> deconvolution (OMGD) was proposed and has been validated as a useful technique to highlight the periodic impulses related to bearing faults. However, the performance of the OMGD is associated with the appropriate selection of prior parameters, such as the filter length. In addition, the OMGD faces edge effect issues, leading to a shorter duration of the enhanced signal when compared to the measured signal. To overcome the shortcomings of the OMGD, this study proposes an improved version, termed the IOMGD. The enhanced technique employs an advanced sparrow search algorithm to automatically ascertain the filter length, doing away with the need for a predetermined fixed value. To solve the problem of the edge effect, a data extension technique based on the autoregressive model (AR-DET) is proposed to adaptively recover the length of the filtered signal to match that of the raw signal based on the properties observed at the filtered signal. The IOMGD’s superiority over the original OMGD has been substantiated by its performance on various real-world bearing fault datasets. Furthermore, a comparative analysis is performed between the IOMGD and other commonly used bearing fault diagnosis methods, revealing the superiority of the IOMGD.https://www.mdpi.com/2075-1702/13/4/270rolling bearingfault detectiondata extension techniqueenhanced sparrow search algorithmimproved optimized minimum generalized L<sub>p</sub>/L<sub>q</sub> deconvolution |
| spellingShingle | Na Yang Zhigang Pan Yuanbo Xu Improved Optimized Minimum Generalized L<sub>p</sub>/L<sub>q</sub> Deconvolution and Application to Bearing Fault Detection Machines rolling bearing fault detection data extension technique enhanced sparrow search algorithm improved optimized minimum generalized L<sub>p</sub>/L<sub>q</sub> deconvolution |
| title | Improved Optimized Minimum Generalized L<sub>p</sub>/L<sub>q</sub> Deconvolution and Application to Bearing Fault Detection |
| title_full | Improved Optimized Minimum Generalized L<sub>p</sub>/L<sub>q</sub> Deconvolution and Application to Bearing Fault Detection |
| title_fullStr | Improved Optimized Minimum Generalized L<sub>p</sub>/L<sub>q</sub> Deconvolution and Application to Bearing Fault Detection |
| title_full_unstemmed | Improved Optimized Minimum Generalized L<sub>p</sub>/L<sub>q</sub> Deconvolution and Application to Bearing Fault Detection |
| title_short | Improved Optimized Minimum Generalized L<sub>p</sub>/L<sub>q</sub> Deconvolution and Application to Bearing Fault Detection |
| title_sort | improved optimized minimum generalized l sub p sub l sub q sub deconvolution and application to bearing fault detection |
| topic | rolling bearing fault detection data extension technique enhanced sparrow search algorithm improved optimized minimum generalized L<sub>p</sub>/L<sub>q</sub> deconvolution |
| url | https://www.mdpi.com/2075-1702/13/4/270 |
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