| Title | Rolling Bearing Fault Diagnosis based on Deep Belief Network |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Liu, Pengjuan, Ma, Jindou |
| Conference Name | 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) |
| Keywords | belief networks, deep belief network, Deep Learning, electrical engineering, employee welfare, fault diagnosis, Metrics, Neural networks, principal component analysis, pubcrawl, Rolling bearings, Support vector machines |
| Abstract | In view of the characteristics that rolling bearing is prone to failure under actual working conditions, and it is difficult to classify the fault category and fault degree, the deep belief network is introduced to diagnose the rolling bearing fault. Firstly, principal component analysis is used to reduce the dimension of original input data and delete redundant input information. Then, the dimension reduced data are input into the deep belief network to extract the low dimensional fault feature representation, and the extracted features are input into the classifier for rolling bearing fault pattern recognition. Finally, the diagnosis effect of the proposed network is compared with the existing common shallow neural network. The simulation experiment is carried out through the bearing data in the United States. |
| DOI | 10.1109/AEECA55500.2022.9918927 |
| Citation Key | liu_rolling_2022 |
Rolling Bearing Fault Diagnosis based on Deep Belief Network