| Title | Multiple Sensors Fault Diagnosis for Rolling Bearing Based on Variational Mode Decomposition and Convolutional Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Hou, Qilin, Wang, Jinglin, Shen, Yong |
| Conference Name | 2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan) |
| Keywords | convolutional neural networks, convolutional neural networks (cnns), cyber physical systems, data mining, fault diagnosis, feature extraction, Human Behavior, human factors, Manuals, Metrics, multiple fault diagnosis, multiple sensors, pubcrawl, resilience, Resiliency, rolling bearing, Rolling bearings, sensor fusion, Variational Mode Decomposition (VMD) |
| Abstract | The reliability of mechanical equipment is very important for the security operation of large-scale equipment. This paper presents a rolling bearing fault diagnosis method based on Variational Mode Decomposition (VMD) and Convolutional Neural Network (CNN). This proposed method includes using VMD and CNN to extend multi-sensor data, extracting detailed features and achieve more robust sensor fusion. Representative features can be extracted automatically from the raw signals. The proposed method can extract features directly from data without prior knowledge. The effectiveness of this method is verified on Case Western Reserve University (CWRU) dataset. Compared with one sensor and traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. Because of the end-to-end feature learning ability, this method can be extended to other kinds of sensor mechanical fault diagnosis. |
| DOI | 10.1109/PHM-Jinan48558.2020.00087 |
| Citation Key | hou_multiple_2020 |
Multiple Sensors Fault Diagnosis for Rolling Bearing Based on Variational Mode Decomposition and Convolutional Neural Networks