| Autor: |
Zhang Bowen, Pang Xinyu, Guan Chongyang |
| Jazyk: |
čínština |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Jixie chuandong, Vol 47, Pp 113-119 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1004-2539 |
| DOI: |
10.16578/j.issn.1004.2539.2023.03.016 |
| Popis: |
Data-driven fault diagnosis methods have been widely used in the field of fault diagnosis of rotating machinery components. However, most of the current research methods mainly rely on a large amount of data generated by fixed-length data segmentation. The segmented data is usually a short-period small segment signal, and the actual long-period redundant signal cannot be directly used as a test sample for fault identification. In view of the above shortcomings, a new fault diagnosis method based on data probability density and one-dimensional convolutional neural network (DPD-1DCNN) is proposed. It has two characteristics: ①the density feature of the extracted signal resists the redundancy of the data; ②adapt redundant signals of different lengths as input to the diagnostic model. The method is verified on the planetary gearbox fault data generated by the DDS test bench, which not only ensures high diagnostic accuracy, but also enhances the adaptability of the diagnostic model. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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