A Quantile Regression Approach for Remaining Useful Life Estimation with State Space Models
This addresses predictive maintenance in industrial settings (Industry 4.0/5.0) by improving RUL estimation, though it appears incremental as it builds on existing SSM and quantile regression techniques.
This paper tackles the problem of predicting equipment Remaining Useful Life (RUL) for predictive maintenance by introducing a novel approach that combines State Space Models (SSM) with Simultaneous Quantile Regression (SQR) to handle uncertainty. Results show superior accuracy and computational efficiency compared to traditional methods like LSTM and Transformer on the C-MAPSS dataset.
Predictive Maintenance (PdM) is pivotal in Industry 4.0 and 5.0, proactively enhancing efficiency through accurate equipment Remaining Useful Life (RUL) prediction, thus optimizing maintenance scheduling and reducing unexpected failures and premature interventions. This paper introduces a novel RUL estimation approach leveraging State Space Models (SSM) for efficient long-term sequence modeling. To handle model uncertainty, Simoultaneous Quantile Regression (SQR) is integrated into the SSM, enabling multiple quantile estimations. The proposed method is benchmarked against traditional sequence modelling techniques (LSTM, Transformer, Informer) using the C-MAPSS dataset. Results demonstrate superior accuracy and computational efficiency of SSM models, underscoring their potential for high-stakes industrial applications.