An Intelligent Fault Diagnosis Framework for Rolling Bearings With Integrated Feature Extraction and Ordering-Based Causal Discovery
文献类型：期刊论文
| 作者 | Ding, Xu1; Wang, Junlong1; Wu, Hao1; Xu, Juan2; Xin, Miao3
|
| 刊名 | IEEE SENSORS JOURNAL
 |
| 出版日期 | 2024-05-15 |
| 卷号 | 24期号:10页码:16374-16386 |
| 关键词 | Fault diagnosis Feature extraction Vibrations Deep learning Rolling bearings Data models Sensors Causal discovery causal effect diagnosis framework feature extraction rolling bearing |
| ISSN号 | 1530-437X |
| DOI | 10.1109/JSEN.2024.3382345 |
| 通讯作者 | Xu, Juan(xujuan@hfut.edu.cn) |
| 英文摘要 | Recent advancements in data-driven deep-learning methods have significantly improved rolling bearing fault diagnosis. However, these frameworks face limitations due to data defects and inadequate feature extraction. To overcome these obstacles, this article proposes a novel feature extraction and causal model-based diagnosis framework. This framework leverages causal discovery to derive causal models from real data, accurately estimate causal effects, and enhance fault diagnosis performance. Specifically, the proposed framework utilizes the time-reassigned synchrosqueezing transform (TSST) to process collected rolling bearing signals, transforming them from the time domain to the time-frequency (TF) domain. This transformation enables the extraction of energy distribution and frequency components of vibration signals at different frequencies, effectively reducing noise interference. Subsequently, a directed acyclic graph (DAG) is constructed using an ordering-based causal discovery method to identify potential interfering factors that may impact vibration signals, facilitating causal effects estimation. Furthermore, by integrating the vision transformer (ViT) network with causal effects estimation techniques, the framework achieves end-to-end rolling bearing fault diagnosis. Experimental results on laboratory-bearing datasets demonstrate the superior performance of this proposed fault diagnosis framework compared to existing methods. |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Engineering ; Instruments & Instrumentation ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001267418100054 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/59184]  |
| 专题 | 类脑芯片与系统研究 |
| 通讯作者 | Xu, Juan |
| 作者单位 | 1.Hefei Univ Technol, Inst Ind & Equipment Technol, Anhui Prov Key Lab Aerosp Struct Parts Forming Tec, Hefei 230002, Peoples R China 2.Hefei Univ Technol, Sch Comp & Informat, Key Lab Knowledge Engn Big Data, Minist Educ, Hefei 230601, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ding, Xu,Wang, Junlong,Wu, Hao,et al. An Intelligent Fault Diagnosis Framework for Rolling Bearings With Integrated Feature Extraction and Ordering-Based Causal Discovery[J]. IEEE SENSORS JOURNAL,2024,24(10):16374-16386. |
| APA | Ding, Xu,Wang, Junlong,Wu, Hao,Xu, Juan,&Xin, Miao.(2024).An Intelligent Fault Diagnosis Framework for Rolling Bearings With Integrated Feature Extraction and Ordering-Based Causal Discovery.IEEE SENSORS JOURNAL,24(10),16374-16386. |
| MLA | Ding, Xu,et al."An Intelligent Fault Diagnosis Framework for Rolling Bearings With Integrated Feature Extraction and Ordering-Based Causal Discovery".IEEE SENSORS JOURNAL 24.10(2024):16374-16386. |
入库方式： OAI收割
来源：自动化研究所
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