EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings
Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time doma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/3/704 |
| _version_ | 1798041418119249920 |
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| author | Cai Yi Dong Wang Wei Fan Kwok-Leung Tsui Jianhui Lin |
| author_facet | Cai Yi Dong Wang Wei Fan Kwok-Leung Tsui Jianhui Lin |
| author_sort | Cai Yi |
| collection | DOAJ |
| description | Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are respectively proposed to measure the steady states of bearing vibration signals. Firstly, vibration data collected from some designed experiments are pre-processed by using ensemble empirical mode decomposition (EEMD). Then, the coefficient of variation is introduced to construct two steady-state indexes from pre-processed vibration data in a time domain and a frequency domain, respectively. A shape function is used to construct a steady-state index in a shape domain. At last, to distinguish normal and abnormal bearing health states, some guideline thresholds are proposed. Further, to identify axle bearings with outer race defects, a pin roller defect, a cage defect, and coupling defects, the boundaries of all steadiness indexes are experimentally established. Experimental results showed that the proposed condition monitoring and fault diagnosis scheme is effective in identifying different bearing health conditions. |
| first_indexed | 2024-04-11T22:21:15Z |
| format | Article |
| id | doaj.art-b32da17072c743a1bcfec3b7517023f8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:21:15Z |
| publishDate | 2018-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b32da17072c743a1bcfec3b7517023f82022-12-22T04:00:06ZengMDPI AGSensors1424-82202018-02-0118370410.3390/s18030704s18030704EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle BearingsCai Yi0Dong Wang1Wei Fan2Kwok-Leung Tsui3Jianhui Lin4School of Automobile and Transportation, Xihua University, Chengdu 610039, ChinaDepartment of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, ChinaDepartment of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, ChinaDepartment of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, ChinaState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaRailway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are respectively proposed to measure the steady states of bearing vibration signals. Firstly, vibration data collected from some designed experiments are pre-processed by using ensemble empirical mode decomposition (EEMD). Then, the coefficient of variation is introduced to construct two steady-state indexes from pre-processed vibration data in a time domain and a frequency domain, respectively. A shape function is used to construct a steady-state index in a shape domain. At last, to distinguish normal and abnormal bearing health states, some guideline thresholds are proposed. Further, to identify axle bearings with outer race defects, a pin roller defect, a cage defect, and coupling defects, the boundaries of all steadiness indexes are experimentally established. Experimental results showed that the proposed condition monitoring and fault diagnosis scheme is effective in identifying different bearing health conditions.http://www.mdpi.com/1424-8220/18/3/704steady-state indexthresholdrailway axle bearing fault diagnosismultiple bearing defectsthe coefficient of variationa shape function |
| spellingShingle | Cai Yi Dong Wang Wei Fan Kwok-Leung Tsui Jianhui Lin EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings Sensors steady-state index threshold railway axle bearing fault diagnosis multiple bearing defects the coefficient of variation a shape function |
| title | EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings |
| title_full | EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings |
| title_fullStr | EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings |
| title_full_unstemmed | EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings |
| title_short | EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings |
| title_sort | eemd based steady state indexes and their applications to condition monitoring and fault diagnosis of railway axle bearings |
| topic | steady-state index threshold railway axle bearing fault diagnosis multiple bearing defects the coefficient of variation a shape function |
| url | http://www.mdpi.com/1424-8220/18/3/704 |
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