Research on an ID-PCA Early Fault Detection Method for Rolling Bearings
Since the rolling bearing is complex during the signal acquisition process, there is a certain loss during the process of collecting the vibration signal. This has led to the weakness of the early fault characteristics of the rolling bearing, affecting the accuracy of the rolling bearing fault featu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/9/4267 |
| _version_ | 1797505729045725184 |
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| author | Jin Guo Yefeng Liu Kangju Li Qiang Liu |
| author_facet | Jin Guo Yefeng Liu Kangju Li Qiang Liu |
| author_sort | Jin Guo |
| collection | DOAJ |
| description | Since the rolling bearing is complex during the signal acquisition process, there is a certain loss during the process of collecting the vibration signal. This has led to the weakness of the early fault characteristics of the rolling bearing, affecting the accuracy of the rolling bearing fault feature extraction. In response to the above problems, an early fault detection method based on the Improved Deep Principal Component Analysis (ID-PCA) is proposed. The proposed method uses the time-series characteristic information of the vibration signal to establish a model, which solves the problem that the principal component analysis method cannot detect the vibration signal directly. Through the deep decomposition theorem, a multi-layer data processing model is established to fully mine the weak fault features in the vibration signal. It can solve the problem of inaccurate early fault detection results due to weak fault feature information. The reliability of this method is proved theoretically through sensitivity analysis. Finally, through experimental simulation, the accuracy and feasibility of this method are proved from the perspective of practice. |
| first_indexed | 2024-03-10T04:22:52Z |
| format | Article |
| id | doaj.art-6d054ec01f3a4e82b976d6ba8605f073 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:22:52Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6d054ec01f3a4e82b976d6ba8605f0732023-11-23T07:46:39ZengMDPI AGApplied Sciences2076-34172022-04-01129426710.3390/app12094267Research on an ID-PCA Early Fault Detection Method for Rolling BearingsJin Guo0Yefeng Liu1Kangju Li2Qiang Liu3School of Mechanical Engineering and Automation, Shenyang Institute of Technology, Fushun 113122, ChinaSchool of Mechanical Engineering and Automation, Shenyang Institute of Technology, Fushun 113122, ChinaLiaoning Key Laboratory of Information Physics Fusion and Intelligent Manufacturing for CNC Machine, Fushun 113122, ChinaState Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Shenyang 110819, ChinaSince the rolling bearing is complex during the signal acquisition process, there is a certain loss during the process of collecting the vibration signal. This has led to the weakness of the early fault characteristics of the rolling bearing, affecting the accuracy of the rolling bearing fault feature extraction. In response to the above problems, an early fault detection method based on the Improved Deep Principal Component Analysis (ID-PCA) is proposed. The proposed method uses the time-series characteristic information of the vibration signal to establish a model, which solves the problem that the principal component analysis method cannot detect the vibration signal directly. Through the deep decomposition theorem, a multi-layer data processing model is established to fully mine the weak fault features in the vibration signal. It can solve the problem of inaccurate early fault detection results due to weak fault feature information. The reliability of this method is proved theoretically through sensitivity analysis. Finally, through experimental simulation, the accuracy and feasibility of this method are proved from the perspective of practice.https://www.mdpi.com/2076-3417/12/9/4267rolling bearingsvibration signalsearly fault detectionweak fault characteristics |
| spellingShingle | Jin Guo Yefeng Liu Kangju Li Qiang Liu Research on an ID-PCA Early Fault Detection Method for Rolling Bearings Applied Sciences rolling bearings vibration signals early fault detection weak fault characteristics |
| title | Research on an ID-PCA Early Fault Detection Method for Rolling Bearings |
| title_full | Research on an ID-PCA Early Fault Detection Method for Rolling Bearings |
| title_fullStr | Research on an ID-PCA Early Fault Detection Method for Rolling Bearings |
| title_full_unstemmed | Research on an ID-PCA Early Fault Detection Method for Rolling Bearings |
| title_short | Research on an ID-PCA Early Fault Detection Method for Rolling Bearings |
| title_sort | research on an id pca early fault detection method for rolling bearings |
| topic | rolling bearings vibration signals early fault detection weak fault characteristics |
| url | https://www.mdpi.com/2076-3417/12/9/4267 |
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