Intelligent fault diagnosis of hoist bearing based on feature transfer learning
| Autor: | PAN Xiaobo, GE Kunpeng, DONG Fei |
|---|---|
| Jazyk: | čínština |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Gong-kuang zidonghua, Vol 48, Iss 9, Pp 1-7, 32 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1671-251X 1671-251x 19886705 |
| DOI: | 10.13272/j.issn.1671-251x.17980 |
| Popis: | The complex actual working conditions of the hoist causes the problems of low accuracy and weak adaptability of existing fault diagnosis methods. In order to solve these problems, an intelligent fault diagnosis method of hoist bearing based on deep transferable feature selection(DTF) and balance distribution adaptation(BDA) is proposed. The bearing fault signals under different working conditions are subjected to time-frequency analysis. The time and frequency domain's statistical characteristics are extracted. The high-dimensional depth characteristics are extracted by adopting a deep belief network. In order to select features that are beneficial to fault mode identification and cross-domain fault diagnosis from a high-dimensional depth feature set, the transferable feature selection based on ReliefF and differences between domains(TFRD) method is adopted. The method carries out the quantitative evaluation of the transitivity of each feature. The TFRD method carries out the quantitative evaluation on the class discrimination and domain invariance of each feature. The ReliefF algorithm processes various feature data to obtain weight values representing class discrimination. This method calculates the maximum mean discrepancy of the same feature between different domains, and constructs a new quantitative index of feature transferability. Based on the TFRD method, depth features with high feature transferability are selected to construct feature subsets. The balance distribution adaptation is applied to carry out distribution adaptation on the feature subsets of the source domain and the target domain, so as to reduce the distribution difference between the two domains. The source domain feature set is used to train the fault pattern identification classifier, and the target domain samples are used for fault identification and classification. Eight fault diagnosis models are constructed by using the classical machine learning method, deep learning method and transfer learning method. The models are used for comparing the fault diagnosis accuracy with the proposed DTF-BDA fault diagnosis model. The results show the following points. ① The DTF-BDA fault diagnosis model can achieve better performance than other models, and the highest fault diagnosis accuracy can reach 100%. ② The TFRD method can effectively improve the performance of the fault diagnosis model based on the transfer learning method. The highest fault diagnosis accuracy can reach 96.46% and 97.67% respectively when combined with the transfer component analysis and joint distribution adaptation. |
| Databáze: | Directory of Open Access Journals |
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