Remaining useful life prediction via K-means clustering analysis and deep convolutional neural network

To improve the prediction accuracy of remaining useful life (RUL), a deep learning method coupled with clustering analysis is proposed. K-means clustering algorithm is employed to analyze the operation settings in data set for matching different operating conditions, and a wise operation mechanism is utilized to normalize the sensor data and match the operation history corresponding to the time instances. The deep convolutional neural network (DCNN) architecture is constructed, which adopts time-sliding window-based sequence as network input. Moreover, it does not require expertise in prediction and signal processing. The CMAPSS dataset published by NASA is used for case study. The proposed approach is validated by comparing with other approaches. The results indicate its superiority on prediction performance of RUL for aeroengine.