Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection
Weak fault detection of rolling bearing presents difficult, because the periodic transient signature produced via localized incipient damage is easily submerged by various interference components and background noise. Hybrid intelligent fusion method is a breakthrough strategy for revealing feature...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2022-11-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132221135738 |
| _version_ | 1811217054222516224 |
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| author | Lu Yan Du Juan Ding En-Jie Liu Ke |
| author_facet | Lu Yan Du Juan Ding En-Jie Liu Ke |
| author_sort | Lu Yan |
| collection | DOAJ |
| description | Weak fault detection of rolling bearing presents difficult, because the periodic transient signature produced via localized incipient damage is easily submerged by various interference components and background noise. Hybrid intelligent fusion method is a breakthrough strategy for revealing feature frequency of rolling bearing fault by comprehensively using a variety of intelligent signal processing technologies, possessing the advantage of each technology. Considering the rolling bearing often construct a transmission device combination with gear and shaft, its vibration signal is often vulnerable to other multi-morphology components, such as harmonic modulation, noise. Thus, how to identify the fault frequency in repetitive transients is crucial to accurately identify rolling bearing fault detection. To address this issue, a novel hybrid intelligent method is proposed to effectively apply on periodic transients extraction, enhancement and rolling bearing fault diagnosis. The innovation of this method is to solve three problems, namely, the separation of multi-morphology components, noise reduction without periodic transients distortion, weak fault frequency enhancement. The proposed method is tested and validated on simulated signal, rolling bearing fault signal from accelerated rolling bearing degradation rig. In addition, comparisons with other classical rolling bearing fault detection methods have been conducted to highlight the superiority of the proposed methodology. |
| first_indexed | 2024-04-12T06:47:43Z |
| format | Article |
| id | doaj.art-1080e708af6043c79bf7e402153981ab |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-04-12T06:47:43Z |
| publishDate | 2022-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-1080e708af6043c79bf7e402153981ab2022-12-22T03:43:28ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402022-11-011410.1177/16878132221135738Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detectionLu Yan0Du Juan1Ding En-Jie2Liu Ke3Shang Hai Dian Ji University, Shang Hai, ChinaShang Hai Dian Ji University, Shang Hai, ChinaChina University of Mining and Technology, Xu Zhou, ChinaShan Dong Energy Zibo Mining Group Information Center, Zibo, Shan Dong, ChinaWeak fault detection of rolling bearing presents difficult, because the periodic transient signature produced via localized incipient damage is easily submerged by various interference components and background noise. Hybrid intelligent fusion method is a breakthrough strategy for revealing feature frequency of rolling bearing fault by comprehensively using a variety of intelligent signal processing technologies, possessing the advantage of each technology. Considering the rolling bearing often construct a transmission device combination with gear and shaft, its vibration signal is often vulnerable to other multi-morphology components, such as harmonic modulation, noise. Thus, how to identify the fault frequency in repetitive transients is crucial to accurately identify rolling bearing fault detection. To address this issue, a novel hybrid intelligent method is proposed to effectively apply on periodic transients extraction, enhancement and rolling bearing fault diagnosis. The innovation of this method is to solve three problems, namely, the separation of multi-morphology components, noise reduction without periodic transients distortion, weak fault frequency enhancement. The proposed method is tested and validated on simulated signal, rolling bearing fault signal from accelerated rolling bearing degradation rig. In addition, comparisons with other classical rolling bearing fault detection methods have been conducted to highlight the superiority of the proposed methodology.https://doi.org/10.1177/16878132221135738 |
| spellingShingle | Lu Yan Du Juan Ding En-Jie Liu Ke Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection Advances in Mechanical Engineering |
| title | Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection |
| title_full | Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection |
| title_fullStr | Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection |
| title_full_unstemmed | Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection |
| title_short | Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection |
| title_sort | enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection |
| url | https://doi.org/10.1177/16878132221135738 |
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