A treelike framework combining fault diagnosis and RUL prediction

Abstract

Most existing deep learning methods consider the remaining useful life (RUL) prediction problem under a single failure mode and cannot solve the RUL prediction problem with multiple failure modes coexisting caused by component coupling in actual engineering systems. Thus, considering these issues, this paper proposes a novel tree network framework to address fault classification and RUL prediction in parallel, and the RUL prediction results are fused output, which are suitable for bearing RUL prediction with multiple faults. First, this paper develops a fault recognizer combining a frequency domain classifier and deep convolutional neural network to improve model selection accuracy. Secondly, this paper proposes a feature fusion algorithm based on the Gini coefficient, and the fused indicators are input into the RUL prediction sub-network for model training. Finally, the RUL sub-network prediction results are dynamically weighted and fused with the fault classification results to obtain the RUL based on SoftMax. The bearing dataset XJTU-SY is introduced to verify the efficiency of the proposed method, and computational results show that the developed framework can effectively predict RUL compared with other traditional methods, especially for RUL prediction under multiple failure modes.