Motor Fault Diagnosis Under Strong Background Noise Based on Parameter-Optimized Feature Mode Decomposition and Spatial–Temporal Features Fusion
As the mining motor is used long-term in a complex multi-source noise environment composed of equipment group coordinated operations and high-frequency start–stop, its vibration signal has the features of significant strong noise interference, weak fault features, and the superposition of multiple w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/4168 |
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| author | Jingcan Wang Yiping Yuan Fangqi Shen Caifeng Chen |
| author_facet | Jingcan Wang Yiping Yuan Fangqi Shen Caifeng Chen |
| author_sort | Jingcan Wang |
| collection | DOAJ |
| description | As the mining motor is used long-term in a complex multi-source noise environment composed of equipment group coordinated operations and high-frequency start–stop, its vibration signal has the features of significant strong noise interference, weak fault features, and the superposition of multiple working conditions coupling, which makes it arduous to efficiently extract and identify mechanical fault features. To address this issue, this study introduces a high-performance fault diagnosis approach for mining motors operating under strong background noise by integrating parameter-optimized feature mode decomposition (WOA-FMD) with the RepLKNet-BiGRU-Attention dual-channel model. According to the experimental results, the average accuracies of the proposed method were 97.7% and 93.38% for the noise-added CWRU bearing fault dataset and the actual operation dataset of the mine motor, respectively, which are significantly better than those of similar methods, showing that the approach in this study is superior in fault feature extraction and identification. |
| format | Article |
| id | doaj-art-3b986fbb6e2a4d8983bbf540c0edaffc |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3b986fbb6e2a4d8983bbf540c0edaffc2025-08-20T03:16:56ZengMDPI AGSensors1424-82202025-07-012513416810.3390/s25134168Motor Fault Diagnosis Under Strong Background Noise Based on Parameter-Optimized Feature Mode Decomposition and Spatial–Temporal Features FusionJingcan Wang0Yiping Yuan1Fangqi Shen2Caifeng Chen3School of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaSchool of Business, University of Leeds, Leeds LS2 9JT, UKSchool of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaAs the mining motor is used long-term in a complex multi-source noise environment composed of equipment group coordinated operations and high-frequency start–stop, its vibration signal has the features of significant strong noise interference, weak fault features, and the superposition of multiple working conditions coupling, which makes it arduous to efficiently extract and identify mechanical fault features. To address this issue, this study introduces a high-performance fault diagnosis approach for mining motors operating under strong background noise by integrating parameter-optimized feature mode decomposition (WOA-FMD) with the RepLKNet-BiGRU-Attention dual-channel model. According to the experimental results, the average accuracies of the proposed method were 97.7% and 93.38% for the noise-added CWRU bearing fault dataset and the actual operation dataset of the mine motor, respectively, which are significantly better than those of similar methods, showing that the approach in this study is superior in fault feature extraction and identification.https://www.mdpi.com/1424-8220/25/13/4168parameter-optimized feature mode decompositionstrong background noisedual-channel modelmotor fault diagnosis |
| spellingShingle | Jingcan Wang Yiping Yuan Fangqi Shen Caifeng Chen Motor Fault Diagnosis Under Strong Background Noise Based on Parameter-Optimized Feature Mode Decomposition and Spatial–Temporal Features Fusion Sensors parameter-optimized feature mode decomposition strong background noise dual-channel model motor fault diagnosis |
| title | Motor Fault Diagnosis Under Strong Background Noise Based on Parameter-Optimized Feature Mode Decomposition and Spatial–Temporal Features Fusion |
| title_full | Motor Fault Diagnosis Under Strong Background Noise Based on Parameter-Optimized Feature Mode Decomposition and Spatial–Temporal Features Fusion |
| title_fullStr | Motor Fault Diagnosis Under Strong Background Noise Based on Parameter-Optimized Feature Mode Decomposition and Spatial–Temporal Features Fusion |
| title_full_unstemmed | Motor Fault Diagnosis Under Strong Background Noise Based on Parameter-Optimized Feature Mode Decomposition and Spatial–Temporal Features Fusion |
| title_short | Motor Fault Diagnosis Under Strong Background Noise Based on Parameter-Optimized Feature Mode Decomposition and Spatial–Temporal Features Fusion |
| title_sort | motor fault diagnosis under strong background noise based on parameter optimized feature mode decomposition and spatial temporal features fusion |
| topic | parameter-optimized feature mode decomposition strong background noise dual-channel model motor fault diagnosis |
| url | https://www.mdpi.com/1424-8220/25/13/4168 |
| work_keys_str_mv | AT jingcanwang motorfaultdiagnosisunderstrongbackgroundnoisebasedonparameteroptimizedfeaturemodedecompositionandspatialtemporalfeaturesfusion AT yipingyuan motorfaultdiagnosisunderstrongbackgroundnoisebasedonparameteroptimizedfeaturemodedecompositionandspatialtemporalfeaturesfusion AT fangqishen motorfaultdiagnosisunderstrongbackgroundnoisebasedonparameteroptimizedfeaturemodedecompositionandspatialtemporalfeaturesfusion AT caifengchen motorfaultdiagnosisunderstrongbackgroundnoisebasedonparameteroptimizedfeaturemodedecompositionandspatialtemporalfeaturesfusion |