Attention-Gaussian-LSTM-Wiener based remaining useful life prediction method

Abstract Most machine learning-based remaining useful life (RUL) prediction methods only yield point predictions, and their “black-box” nature results in low interpretability. Stochastic process-based modeling can predict RUL probability density function (PDF), yet it often suffers from inaccurate modeling and failure to fully utilize historical degradation data of the same equipment type. To overcome these limitations, this paper integrates the two approaches and proposes an Attention-Gaussian-LSTM-Wiener (AG-LSTM-Wiener)-based RUL prediction method, enabling dynamic weighted fusion of predicted PDFs. An AG-LSTM-Wiener model with a two-branch structure is constructed. Health indicator (HI) is fed into the corresponding branch models to generate two different PDF curves. Decision blocks are employed to estimate RUL, from which weights are derived to achieve dynamic weighted fusion of the PDFs. Experiments on the CMPASS turbofan engine degradation dataset validate the proposed method’s effectiveness. Results demonstrate that the proposed method not only prevents PDF curve distortion but also improves the prediction accuracy compared with other methods. With the root mean squared error (RMSE) and Score reduced by 32.8% and 46.1% on average, and the mean squared error of PDF ( $\mathrm{MSE}_{\mathrm{PDF}} $ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>MSE</mml:mi> <mml:mi>PDF</mml:mi> </mml:msub> </mml:math> ) improved by 99.3% compared to AG-LSTM, which exhibits the best performance among the contrast methods.