方位(导航)
计算机科学
不变(物理)
稳健性(进化)
振动
加速
人工智能
数据挖掘
数学
声学
物理
生物化学
化学
数学物理
基因
操作系统
作者
Rijun Liao,Chunguang Wang,Fujun Peng,Lai Wei,Yijun Zhang,Zhang Xin
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:12: 8875-8888
标识
DOI:10.1109/access.2024.3351935
摘要
Fault diagnosis holds important significance in mitigating financial losses and ensuring equipment safety. As a crucial aspect of industrial machinery, bearing fault diagnosis becomes imperative. Nevertheless, in reality, identifying faults becomes challenging due to the presence of diverse variations in abnormal data, such as different vibration rotational speeds and different diameters of bearing. In this paper, we propose a novel framework for bearing fault diagnosis, called DTM-Bearing , built upon the diffusion transformation model (DTM). This approach can transfer signals across different vibration speeds into a standardized signal aligned with a speed template. The primary purpose of DTM-Bearing is to eliminate speed variations and extract speed-invariant features. Consequently, bolster the robustness of bearing fault diagnosis across diverse vibration speed scenarios. To the best of our knowledge, we are the first to combine the concepts of diffusion model and transformation in the domain of bearing fault diagnosis. We perform various experiments on some datasets with multiple different speeds, which shows our proposal can effectively improve the performance of bearing diagnosis.We believe the proposed framework based on the diffusion transformation model can further eliminate other variations, and enhance the utility of bearing diagnosis in real applications.
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