# RUL prediction method based on sequential health index evaluation with multidimensional coupled degradation data

## TL;DR

This paper introduces a new RUL prediction method that uses a health index evaluation to improve accuracy without needing high-quality labeled data.

## Contribution

The novel approach combines a CNN-Transformer model with a sequential health index evaluation to reduce computational demands and improve prediction accuracy.

## Key findings

- The proposed method outperforms LSTM, Transformer, and Att-BiGRU in RUL prediction accuracy.
- The chunk-interaction mechanism reduces model complexity and computational demands.
- The sequential evaluation scheme effectively constructs health indices without relying on high-quality labeled data.

## Abstract

Remaining Useful Life (RUL) prediction is crucial for implementing predictive maintenance strategies, however, RUL prediction is severely constrained by the lack of high-quality labeled life-cycle data. Moreover, complex coupling relationships exist within the obtained multidimensional degradation data, making it difficult to construct an accurate health index (HI) for the system. To address this challenge, we propose an RUL prediction method based on sequential healthy index evaluation which incorporate two parts: the parameter prediction process and the health index fusion process. The core innovation of this study is an RUL prediction method that integrates a CNN-Transformer hybrid model with a sequential health index evaluation scheme. Compared to traditional data-driven methods, our approach incorporates a chunk-interaction mechanism into the multi-head attention design, thereby reducing model complexity and computational demands. Simultaneously, the sequential evaluation scheme dynamically constructs the health index based on the Mahalanobis distance and the Sequential Evaluation Ratio (SER), which eliminates the reliance on high-quality labeled life-cycle data. Experimental results demonstrate that the proposed method outperforms existing deep learning approaches (such as LSTM, Transformer, and Att-BiGRU) across multiple datasets, exhibiting higher prediction accuracy and robustness, particularly in label-scarce scenarios.