An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis
To address the challenge of extracting fault features and accurately identifying bearing fault conditions under strong noisy environments, a rolling bearing failure diagnostic technique is presented that utilizes parameter-optimized maximum second-order cyclostationary blind deconvolution (CYCBD) an...
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1495 |
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| author | Jixin Liu Liwei Deng Yue Cao Chenglin Wen Zhihuan Song Mei Liu Xiaowei Cui |
| author_facet | Jixin Liu Liwei Deng Yue Cao Chenglin Wen Zhihuan Song Mei Liu Xiaowei Cui |
| author_sort | Jixin Liu |
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| description | To address the challenge of extracting fault features and accurately identifying bearing fault conditions under strong noisy environments, a rolling bearing failure diagnostic technique is presented that utilizes parameter-optimized maximum second-order cyclostationary blind deconvolution (CYCBD) and bidirectional long short-term memory (BiLSTM) networks. Initially, an adaptive golden jackal optimization (GJO) algorithm is employed to refine important CYCBD parameters. Subsequently, the rolling bearing failure signals are filtered and denoised using the optimized CYCBD, producing a denoised signal. Ultimately, the noise-reduced signal is fed into the BiLSTM model to realize the classification of faults. The experimental findings demonstrate the suggested approach’s strong noise reduction performance and high diagnostic accuracy. The optimized CYCBD–BiLSTM improves the accuracy by approximately 9.89% compared with other methods when the signal-to-noise ratio (SNR) reaches −9 dB, and it can be effectively used for diagnosing rolling bearing faults under noisy backgrounds. |
| format | Article |
| id | doaj-art-e868be0c21a7465fa2ccbc6f25d55e4e |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e868be0c21a7465fa2ccbc6f25d55e4e2025-08-20T02:06:15ZengMDPI AGSensors1424-82202025-02-01255149510.3390/s25051495An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault DiagnosisJixin Liu0Liwei Deng1Yue Cao2Chenglin Wen3Zhihuan Song4Mei Liu5Xiaowei Cui6School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, ChinaSchool of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, ChinaSchool of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, ChinaSchool of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, ChinaSchool of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, ChinaSchool of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, ChinaSchool of Computer Science, Baicheng Normal University, Baicheng 137000, ChinaTo address the challenge of extracting fault features and accurately identifying bearing fault conditions under strong noisy environments, a rolling bearing failure diagnostic technique is presented that utilizes parameter-optimized maximum second-order cyclostationary blind deconvolution (CYCBD) and bidirectional long short-term memory (BiLSTM) networks. Initially, an adaptive golden jackal optimization (GJO) algorithm is employed to refine important CYCBD parameters. Subsequently, the rolling bearing failure signals are filtered and denoised using the optimized CYCBD, producing a denoised signal. Ultimately, the noise-reduced signal is fed into the BiLSTM model to realize the classification of faults. The experimental findings demonstrate the suggested approach’s strong noise reduction performance and high diagnostic accuracy. The optimized CYCBD–BiLSTM improves the accuracy by approximately 9.89% compared with other methods when the signal-to-noise ratio (SNR) reaches −9 dB, and it can be effectively used for diagnosing rolling bearing faults under noisy backgrounds.https://www.mdpi.com/1424-8220/25/5/1495fault diagnosismaximum second-order cyclostationarity blind deconvolution (CYCBD)bidirectional long short-term memory (BiLSTM)golden jackal optimization (GJO) |
| spellingShingle | Jixin Liu Liwei Deng Yue Cao Chenglin Wen Zhihuan Song Mei Liu Xiaowei Cui An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis Sensors fault diagnosis maximum second-order cyclostationarity blind deconvolution (CYCBD) bidirectional long short-term memory (BiLSTM) golden jackal optimization (GJO) |
| title | An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis |
| title_full | An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis |
| title_fullStr | An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis |
| title_full_unstemmed | An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis |
| title_short | An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis |
| title_sort | optimized maximum second order cyclostationary blind deconvolution and bidirectional long short term memory network model for rolling bearing fault diagnosis |
| topic | fault diagnosis maximum second-order cyclostationarity blind deconvolution (CYCBD) bidirectional long short-term memory (BiLSTM) golden jackal optimization (GJO) |
| url | https://www.mdpi.com/1424-8220/25/5/1495 |
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