A Novel Fault Diagnosis Method Based on Topological Data Analysis
- Resource Type
- Conference
- Authors
- Wang, Yuqing; Li, Yibin; Song, Yan; Xu, Danya; Zheng, Weihong
- Source
- 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS) Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS), 2021 CAA Symposium on. :1-6 Dec, 2021
- Subject
- Components, Circuits, Devices and Systems
Robotics and Control Systems
Signal Processing and Analysis
Fault diagnosis
Wavelet transforms
Time-frequency analysis
Data analysis
Wavelet domain
Transfer learning
Feature extraction
Topological data analysis
- Language
In this paper, we proposed a new method for fault diagnosis of mechanical bearings. The method is based on topological data analysis (TDA) technology, such as persistent homology to analyze time series. We used Case Western Reserve University bearing dataset to conduct experiments for different fault diameters and different load conditions. Through comparison with previous work, the main contribution of this paper is to use topological data analysis to enhance the representativeness and expressiveness of the extracted features. The experimental results under different working conditions to verify the effectiveness and development of the proposed model. We provided a new idea for the field of fault diagnosis.