Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis
Bearings are critical parts of rotating machines, making bearing fault diagnosis based on signals a research hotspot through the ages. In real application scenarios, bearing signals are normally non-linear and unstable, and thus difficult to analyze in the time or frequency domain only. Meanwhile, f...
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doaj-ede1c0b04f2e4dcf94efac6fcf73b6992020-11-25T01:27:25ZengMDPI AGSensors1424-82202018-10-011810331210.3390/s18103312s18103312Self-Adaptive Spectrum Analysis Based Bearing Fault DiagnosisJie Wu0Tang Tang1Ming Chen2Tianhao Hu3School of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaBearings are critical parts of rotating machines, making bearing fault diagnosis based on signals a research hotspot through the ages. In real application scenarios, bearing signals are normally non-linear and unstable, and thus difficult to analyze in the time or frequency domain only. Meanwhile, fault feature vectors extracted conventionally with fixed dimensions may cause insufficiency or redundancy of diagnostic information and result in poor diagnostic performance. In this paper, Self-adaptive Spectrum Analysis (SSA) and a SSA-based diagnosis framework are proposed to solve these problems. Firstly, signals are decomposed into components with better analyzability. Then, SSA is developed to extract fault features adaptively and construct non-fixed dimension feature vectors. Finally, Support Vector Machine (SVM) is applied to classify different fault features. Data collected under different working conditions are selected for experiments. Results show that the diagnosis method based on the proposed diagnostic framework has better performance. In conclusion, combined with signal decomposition methods, the SSA method proposed in this paper achieves higher reliability and robustness than other tested feature extraction methods. Simultaneously, the diagnosis methods based on SSA achieve higher accuracy and stability under different working conditions with different sample division schemes.http://www.mdpi.com/1424-8220/18/10/3312fault diagnosisfeature extractionself-adaptive spectrum analysisbearing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jie Wu Tang Tang Ming Chen Tianhao Hu |
spellingShingle |
Jie Wu Tang Tang Ming Chen Tianhao Hu Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis Sensors fault diagnosis feature extraction self-adaptive spectrum analysis bearing |
author_facet |
Jie Wu Tang Tang Ming Chen Tianhao Hu |
author_sort |
Jie Wu |
title |
Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis |
title_short |
Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis |
title_full |
Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis |
title_fullStr |
Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis |
title_full_unstemmed |
Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis |
title_sort |
self-adaptive spectrum analysis based bearing fault diagnosis |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2018-10-01 |
description |
Bearings are critical parts of rotating machines, making bearing fault diagnosis based on signals a research hotspot through the ages. In real application scenarios, bearing signals are normally non-linear and unstable, and thus difficult to analyze in the time or frequency domain only. Meanwhile, fault feature vectors extracted conventionally with fixed dimensions may cause insufficiency or redundancy of diagnostic information and result in poor diagnostic performance. In this paper, Self-adaptive Spectrum Analysis (SSA) and a SSA-based diagnosis framework are proposed to solve these problems. Firstly, signals are decomposed into components with better analyzability. Then, SSA is developed to extract fault features adaptively and construct non-fixed dimension feature vectors. Finally, Support Vector Machine (SVM) is applied to classify different fault features. Data collected under different working conditions are selected for experiments. Results show that the diagnosis method based on the proposed diagnostic framework has better performance. In conclusion, combined with signal decomposition methods, the SSA method proposed in this paper achieves higher reliability and robustness than other tested feature extraction methods. Simultaneously, the diagnosis methods based on SSA achieve higher accuracy and stability under different working conditions with different sample division schemes. |
topic |
fault diagnosis feature extraction self-adaptive spectrum analysis bearing |
url |
http://www.mdpi.com/1424-8220/18/10/3312 |
work_keys_str_mv |
AT jiewu selfadaptivespectrumanalysisbasedbearingfaultdiagnosis AT tangtang selfadaptivespectrumanalysisbasedbearingfaultdiagnosis AT mingchen selfadaptivespectrumanalysisbasedbearingfaultdiagnosis AT tianhaohu selfadaptivespectrumanalysisbasedbearingfaultdiagnosis |
_version_ |
1725105675192762368 |