摘要：AbstractRemaining useful life prediction is a key procedure for prognostics and health management. However, traditional data-driven methods rely on handcrafted feature selection from the whole range of time series data, which may not obtain the temporal information for complex systems. This study proposes a gated recurrent unit networks based approach to predict remaining useful life. First, time window approach is applied on sample preparation for multiple sensor data. In particular, unsupervised stacked sparse autoencoder is utilized to automatically extract nonlinear features, then the selected features are fed into gated recurrent unit based recurrent neural networks to predict remaining useful life. The effectiveness of the proposed method is demonstrated on the commercial modular aero-propulsion system simulation data from NASA. Experimental results validate that the proposed approach achieves better prediction performance than other methods.
关键词：Keywordsprognosticsremaining useful lifeautoencodergated recurrent unit
