| Citation: | LIN Y H，LI C B. Multidimensional degradation data generation method based on variational autoencoder[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（10）：2617-2627 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0760
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The data-driven remaining useful life (RUL) prediction method does not rely on complicated physical models; instead, it can use current monitoring data as well as historical operational data for the equipment, which is very important for developing a reasonable maintenance strategy and lowering the equipment's maintenance costs. However, the data-driven RUL prediction method relies on a large amount of historical data. When the data is insufficient, especially for multidimensional degradation data, the model is difficult to achieve good prediction results. To solve this problem, this paper proposes a multidimensional degradation data generation method.The technique creates a one-stage model using a conditional variational autoencoder as the generation model and a long short-term memory network as the RUL prediction model. The generation model can then be updated using the RUL prediction model, which can then be used to boost the RUL prediction model's performance in the absence of enough degradation data. On a dataset of aero-engine degradation, the approach is validated. The method is validated on an aero-engine degradation dataset. By comparing the performance of the RUL prediction model trained with and without generated data, the effectiveness of the method is demonstrated for RUL prediction with insufficient data.
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