A novel training mechanism for health indicator construction and remaining useful lifetime (RUL) prediction

In the field of predicting the remaining useful lifetime (RUL) of equipment based on health indicators (HIs), the effective extraction of equipment status poses a persistent challenge. Numerous studies have focused on the extraction of HI of an equipment, frequently proposing training methods that utilize one-dimensional latent space autoencoders for computing the loss function with HI. This was imperative due to the compositional nature of HIs are real numbers. However, in cases where equipment status exhibits nonlinear and intricate structuring, the latent vector necessitates representation within a sufficiently expansive space. This paper introduces a methodology for mapping real numbers to higher dimensions to achieve a more efficient representation of HI. Upon applying our proposed methodology to various HI extraction models, we observed that in most instances, the performance of HI extraction was enhanced. Ultimately, this contributed to an improvement in the prediction accuracy to RUL. The efficacy of our learning mechanism was validated across four subsets (FD001 to FD004) of the C-MAPSS dataset provided by NASA. Notably, the methodology presented in this study holds significance as a learning mechanism adaptable to various approaches.