Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application
Many researches have been carried out on incipient fault prediction technology for key machine components (such as bearings) based on historical and real-time condition monitoring data. However, there is still lack of well-understood systematic methodologies for detecting incipient fault for rotatin...
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9233373/ |
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| author | Qingfeng Wang Bingkun Wei Jiahe Liu Wensheng Ma |
| author_facet | Qingfeng Wang Bingkun Wei Jiahe Liu Wensheng Ma |
| author_sort | Qingfeng Wang |
| collection | DOAJ |
| description | Many researches have been carried out on incipient fault prediction technology for key machine components (such as bearings) based on historical and real-time condition monitoring data. However, there is still lack of well-understood systematic methodologies for detecting incipient fault for rotating machines. Based on machine learning technology, this paper studies an incipient fault prediction model applying with wavelet packet decomposition and dynamic kernel principal component analysis (WPD-DKPCA) to meet the needs of engineering applications. The incipient fault prediction WPD-DKPCA model, which does not require knowledge on equipment structure and failure mechanisms, only requires normal state data of the machine, and incipient fault prediction can be achieved through self-learning. Run-to-failure experimental data and engineering case data have been used to verify the constructed model, and the verification results show that the constructed model can reliably and accurately detect an incipient bearing fault. Comparisons of fault prediction effects prove that using T<sup>2</sup> statistic monitoring can detect upcoming faults of machines much earlier than Kurtosis and Root Mean Square (RMS). |
| format | Article |
| id | doaj-art-7659c1a1e8d44e2ca2766a0f2b1df54d |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7659c1a1e8d44e2ca2766a0f2b1df54d2025-08-20T03:13:40ZengIEEEIEEE Access2169-35362020-01-01819713419714610.1109/ACCESS.2020.30324459233373Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and ApplicationQingfeng Wang0https://orcid.org/0000-0003-3666-4610Bingkun Wei1Jiahe Liu2Wensheng Ma3Beijing Key Laboratory of Health Monitoring and Self-Recovery of High-End Machinery Equipment, Beijing University of Chemical Technology, Beijing, ChinaBeijing Key Laboratory of Health Monitoring and Self-Recovery of High-End Machinery Equipment, Beijing University of Chemical Technology, Beijing, ChinaBeijing Key Laboratory of Health Monitoring and Self-Recovery of High-End Machinery Equipment, Beijing University of Chemical Technology, Beijing, ChinaDiagnosis and Self-Recovery Engineering Research Center, Beijing University of Chemical Technology, Beijing, ChinaMany researches have been carried out on incipient fault prediction technology for key machine components (such as bearings) based on historical and real-time condition monitoring data. However, there is still lack of well-understood systematic methodologies for detecting incipient fault for rotating machines. Based on machine learning technology, this paper studies an incipient fault prediction model applying with wavelet packet decomposition and dynamic kernel principal component analysis (WPD-DKPCA) to meet the needs of engineering applications. The incipient fault prediction WPD-DKPCA model, which does not require knowledge on equipment structure and failure mechanisms, only requires normal state data of the machine, and incipient fault prediction can be achieved through self-learning. Run-to-failure experimental data and engineering case data have been used to verify the constructed model, and the verification results show that the constructed model can reliably and accurately detect an incipient bearing fault. Comparisons of fault prediction effects prove that using T<sup>2</sup> statistic monitoring can detect upcoming faults of machines much earlier than Kurtosis and Root Mean Square (RMS).https://ieeexplore.ieee.org/document/9233373/Data-drivenincipient fault predictionsystematic methodologyWPD-DKPCA modelfault prediction effect |
| spellingShingle | Qingfeng Wang Bingkun Wei Jiahe Liu Wensheng Ma Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application IEEE Access Data-driven incipient fault prediction systematic methodology WPD-DKPCA model fault prediction effect |
| title | Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application |
| title_full | Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application |
| title_fullStr | Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application |
| title_full_unstemmed | Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application |
| title_short | Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application |
| title_sort | data driven incipient fault prediction for non stationary and non linear rotating systems methodology model construction and application |
| topic | Data-driven incipient fault prediction systematic methodology WPD-DKPCA model fault prediction effect |
| url | https://ieeexplore.ieee.org/document/9233373/ |
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