An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation

Abstract

Effectively estimating remaining useful life (RUL) is crucially important for evaluating machine health. In the industry, there exists a high degree of inconsistency among the length of condition monitoring data. Thus, we propose an ensemble framework based on convolutional bi-directional long short-term memory with multiple time windows (MTW CNN-BLSTM Ensemble) for accurately predicting RUL under this circumstance. In the training phase, multiple CNN-BLSTM base models with different time window sizes are trained to capture various temporal dependencies between features. This setting expands the time window size and reduces the training error compared to traditional static time window size approaches. In the testing phase, test units are classified and suitable base models are applied according to the length of running time. A weighted average method is exploited to aggregate base models’ outcomes. This ensemble strategy can increase the utilization rate of the test data and further enhance prediction accuracy. The effectiveness of this framework is validated and the comparison with state-of-the-art methods available has been provided. The results have shown that this framework can achieve the minimum prediction error and provide stable support for equipment health management.