Incremental Learning for Remaining Useful Life Prediction via Temporal Cascade Broad Learning System With Newly Acquired Data

Abstract

Deep neural networks have promoted the technology development of fault classification and remaining useful life (RUL) prediction for mechanical equipment due to their powerful nonlinear feature extraction capability. However, the performance of traditional deep learning models is limited by the depth of networks, which is directly related to the training consumption. In addition, the parameters of networks can only be updated by retraining when faced with newly acquired data. To address the above problems, an incremental learning method based on a temporal cascade broad learning system (TCBLS) is proposed for the RUL prediction of machinery with newly acquired data. Specifically, linear and nonlinear feature information is first learned by the TCBLS. The ridge regression method is developed to calculate the weights of the network and establish an end-to-end mapping between the feature information layer and the prediction layer. Finally, the incremental learning of new data and the incremental learning of nodes are proposed for adaptively updating the weights of the network in the face of newly acquired data and insufficient prediction accuracy. The effectiveness of the proposed method is verified by four run-to-failure datasets. The comparison results with classical deep learning models show that the proposed method is promising for RUL prediction as it achieves high prediction accuracy while saving training time consumption across orders of magnitude and effectively handling newly acquired data without retraining.