Deep Robust Autoencoder based Framework for Bearing Fault Detection
- Resource Type
- Conference
- Authors
- Cheng, Wei; Li, Zheng; Cheng, Fei
- Source
- 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) Reliability and Prognostics and Health Management (PHM-Yantai),2022 Global. :1-6 Oct, 2022
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Nuclear Engineering
Power, Energy and Industry Applications
Robotics and Control Systems
Transportation
Support vector machines
Vibrations
Fault detection
Transforms
Signal processing
Vibration measurement
Feature extraction
bearings
condition-based maintenance
deep learning
fault detection
robust deep autoencoder
vibration analysis
- Language
Bearings are important components in many systems and widely used in rotating machineries. The condition of bearing influences the reliability and useful life of rotating machinery greatly. Thus, it is of significant importance to find early-stage bearing faults timely and effectively. Recently, due to the increasing of data size and computation capacity, more and more attentions have been drawn in machine learning-based methods for fault detection of bearings. A new bearing fault detection method based on deep autoencoder and support vector machine (SVM) is proposed in this paper. Signal processing and feature extraction is first performed to calculate envelope spectra of the measured raw vibration signals. Then, a robust deep autoencoder is applied to reduce the dimension of the spectra points; the output of robust deep autoencoder is then used to train an SVM for the purpose of fault detection. A case study using bearing data obtained from Case Western Reserve University Bearing Data Center is provided to show the effectiveness of our fault detection method.