Application of modified culture Kalman filter in bearing fault diagnosis
Rolling bearings are an important part of rotary machines. They are used most widely in various mechanical sectors, which are among the most vulnerable components in machines. This paper uses CKF algorithm to compile a signal analysis system, analyses the vibration signal of the rolling bearing, ext...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2018-11-01
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| Series: | Open Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/phys-2018-0095 |
| _version_ | 1819102109537140736 |
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| author | Hailun Wang Martinez Alexander |
| author_facet | Hailun Wang Martinez Alexander |
| author_sort | Hailun Wang |
| collection | DOAJ |
| description | Rolling bearings are an important part of rotary machines. They are used most widely in various mechanical sectors, which are among the most vulnerable components in machines. This paper uses CKF algorithm to compile a signal analysis system, analyses the vibration signal of the rolling bearing, extracts fault features, and realizes fault diagnosis. In order to improve the estimation accuracy of bearing fault diagnosis under nonlinear model, a nonlinear model of bearing fault diagnosis based on quaternion and low-accuracy high-noise sensors is established, and the attitude estimation has performed using the culture Kalman filter (CKF) algorithm. The sensor data comparison shows that the use of the volumetric Kalman filter algorithm can effectively improve the estimation accuracy of bearing fault diagnosis and stability. In this paper, the measured vibration signals of several groups of rolling bearings are analysed, and the signal characteristic frequency has extracted. The results show that using the analysis software designed in this paper, several typical faults of rolling bearings can be correctly identified. |
| first_indexed | 2024-12-22T01:29:20Z |
| format | Article |
| id | doaj.art-9d8d61b4acb243619a3eedf9b6bb917b |
| institution | Directory Open Access Journal |
| issn | 2391-5471 |
| language | English |
| last_indexed | 2024-12-22T01:29:20Z |
| publishDate | 2018-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Physics |
| spelling | doaj.art-9d8d61b4acb243619a3eedf9b6bb917b2022-12-21T18:43:32ZengDe GruyterOpen Physics2391-54712018-11-0116175776510.1515/phys-2018-0095phys-2018-0095Application of modified culture Kalman filter in bearing fault diagnosisHailun Wang0Martinez Alexander1Shanghai Maritime University, Logistics Engineering College, Quzhou University, College of Electrical and Information Engineering, Shanghai200135, ChinaNewcastle University, School of Computing, Newcastleupon Tyne NE1 7RU, United KingdomRolling bearings are an important part of rotary machines. They are used most widely in various mechanical sectors, which are among the most vulnerable components in machines. This paper uses CKF algorithm to compile a signal analysis system, analyses the vibration signal of the rolling bearing, extracts fault features, and realizes fault diagnosis. In order to improve the estimation accuracy of bearing fault diagnosis under nonlinear model, a nonlinear model of bearing fault diagnosis based on quaternion and low-accuracy high-noise sensors is established, and the attitude estimation has performed using the culture Kalman filter (CKF) algorithm. The sensor data comparison shows that the use of the volumetric Kalman filter algorithm can effectively improve the estimation accuracy of bearing fault diagnosis and stability. In this paper, the measured vibration signals of several groups of rolling bearings are analysed, and the signal characteristic frequency has extracted. The results show that using the analysis software designed in this paper, several typical faults of rolling bearings can be correctly identified.https://doi.org/10.1515/phys-2018-0095rolling bearingfault diagnosisvibration signalckf02.30.cj02.30.sa07.10.-h |
| spellingShingle | Hailun Wang Martinez Alexander Application of modified culture Kalman filter in bearing fault diagnosis Open Physics rolling bearing fault diagnosis vibration signal ckf 02.30.cj 02.30.sa 07.10.-h |
| title | Application of modified culture Kalman filter in bearing fault diagnosis |
| title_full | Application of modified culture Kalman filter in bearing fault diagnosis |
| title_fullStr | Application of modified culture Kalman filter in bearing fault diagnosis |
| title_full_unstemmed | Application of modified culture Kalman filter in bearing fault diagnosis |
| title_short | Application of modified culture Kalman filter in bearing fault diagnosis |
| title_sort | application of modified culture kalman filter in bearing fault diagnosis |
| topic | rolling bearing fault diagnosis vibration signal ckf 02.30.cj 02.30.sa 07.10.-h |
| url | https://doi.org/10.1515/phys-2018-0095 |
| work_keys_str_mv | AT hailunwang applicationofmodifiedculturekalmanfilterinbearingfaultdiagnosis AT martinezalexander applicationofmodifiedculturekalmanfilterinbearingfaultdiagnosis |