Degradation trend feature generation improved rotating machines RUL prognosis method with limited run-to-failure data
| Autor: | Kai Zhang, Yantao Liu, Yisheng Zou, Kun Ding, Yongzhi Liu, Qing Zheng, Guofu Ding |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Measurement Science and Technology. 34:075019 |
| ISSN: | 1361-6501 0957-0233 |
| DOI: | 10.1088/1361-6501/accbde |
| Popis: | The success of rotating machines’ data-driven remaining useful life (RUL) prognosis approaches depends heavily on the abundance of entire life cycle data. However, it is not easy to obtain sufficient run-to-failure data in industrial practice. Data generation technology is a promising solution for enriching data but fails to address the intrinsic complexity of nonlinear stage degradation and the time correlation of long-term data. This research proposes an RUL prognosis approach improved by the degradation trend feature generation variational autoencoder. First, this study develops a framework combining degradation trend generation features to resolve the issue of capturing the elements of time distribution for run-to-failure data. Second, a generation variational autoencoder network with a tendency block is proposed to create high-quality time series data correlation features. Third, original and created degradation trend features are subjected to deep adaptive fusion and health indicator extraction. A bi-directional long short-term memory network is employed to predict the degradation trend and obtain the RUL prognosis. Finally, the proposed approach’s feasibility is confirmed by cross-validation experiments on a bearing dataset, which reduces the prediction error by 22.309%. |
| Databáze: | OpenAIRE |
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