A feature-level degradation measurement method for composite health index construction and trend prediction modeling

Abstract

Accurate measurement of degradation levels and evolution trend for mechanical equipment has great significance to achieve the goal of condition-based maintenance (CBM) and increase the economic benefit. In this paper, a feature-level degradation measurement method is proposed for composite health index (CHI) construction and trend prediction, systematically blending the multi-source signals analysis theory and deep learning technology. First, a CHI is innovatively constructed based on a novel hybrid stacked auto-encoder (HSAE) and self-organizing map (SOM) network to comprehensively evaluate the degradation levels, which achieves the feature-level data fusion for degradation measurement. Second, considering the significant characteristics of strong nonlinear and random fluctuations for the generated CHI sequence, a hybrid algorithm, combining the variational mode decomposition (VMD) and a dynamic step size-based fruit fly optimization algorithm (DSSFOA), is applied for effectively extracting the multiscale frequency components from raw sequence and capturing the inherent laws of health degradation from different scales. Specifically, by designing a hierarchical search structure with dynamic step sizes in DSSFOA, the parameters of VMD can be optimized with high efficiency and accuracy. Finally, a scale-adaptive attention (SAA)-based bidirectional long short-term memory (BiLSTM) network and the corresponding dual-stage training strategy are developed to adaptively estimate the attention weights of different components and further predict the degradation trend in future. Extensive experiments on the C-MAPSS turbofan engine dataset and the PSU dataset validate the more superior performance of the proposed method on both of health index construction and degradation trend prediction than other existing methods.