Mechanical health indicator construction and similarity remaining useful life prediction based on natural language processing model

Abstract

Remaining useful life (RUL) prediction, as an essential task for prediction and health management (PHM), can improve the reliability of degradation systems. In this paper, multi-dimensional mechanical monitoring readings are treated as natural language sequences and inputted to a natural language processing (NLP) model, Transformer, to extract semantic compression vectors. The health indicators (HI) are constructed from the changes of compressed semantic vectors during the mechanical system operation, reflecting the mechanical health status at each monitoring point. Subsequently, combined with the similarity matching method, the RUL prediction is achieved by weighted summation. Transformer mainly consists of attention layers and residual connection layers, which contain no recurrence and convolution. The attention layer can highlight the critical time step information during the HI construction. Moreover, the output of different time steps can be obtained simultaneously in a parallel computation manner. The proposed scheme is experimentally compared on two publicly available datasets and achieves competitive prediction performance

A vision subsampling probsparse self-attention-based informer for bearing remaining useful life prediction

“…The condition-based * Author to whom any correspondence should be addressed. maintenance (CBM) method needs to measure information related to the failure and reflects the machine's health status at all times [2]. Obviously, remaining useful life (RUL) prediction is a significant technology for application of CBM.…”

AI-enabled industrial equipment monitoring, diagnosis and health management

“…By constructing MFF-HI with an MFF depth network and leveraging WTCN with a new loss function, the proposed method achieved accurate RUL prediction for bearings with higher prediction accuracy across various datasets. Duan et al [41] introduced a method for RUL prediction by treating mechanical monitoring readings as natural language sequences, utilizing a transformer model to extract semantic compression vectors for constructing health indicators (HIs) and achieving RUL prediction through weighted summation. Chen et al [42] developed a novel spatial attention-based convolutional transformer for precise bearing RUL prediction without the need for feature engineering.…”