Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns

Abstract

Recent advances in multivariate data fusion technology have promoted the applications of neural network-based models for remaining useful life (RUL) prediction. However, the interpretability of these models is usually poor since they are developed in a black-box manner. It is difficult to use them in engineering systems with multiple failure modes (FMs) under various operation conditions (OCs). This work proposes an adaptive deep learning-based RUL prediction framework with FM recognition. First, a FM recognizer fusing physics-informed FM classifier with deep convolutional neural networks (DCNN) is developed, which improves the interpretability and the accuracy of the recognition model. Then, a framework which can adaptively train models and select them for RUL prediction according to FM recognition results is presented. An OC-based smoothing technique is proposed to improve the RUL prediction accuracy and robustness. Extensive experiments based on turbofan datasets are conducted to validate the effectiveness of the proposed framework. The results show that the RUL prediction accuracy is improved by 7% under the proposed framework when compared with other methods. It proves the performance gains of the proposed framework by incorporating prior FM recognition with RUL prediction. It also provides insights for RUL prognostics subject to distinct FMs and OCs.