Towards Reliable Prognostics: RUL Uncertainty Estimation with Transformers and Monte Carlo Dropout

It is commonly accepted that being able to accurately predict the Remaining Useful Life (RUL) is a key part of good Prognostics and Health Management (PHM). Deep learning models have made a lot of progress on this task in the last few years. The Transformer architecture, in particular, has shown that it can make accurate predictions on complicated time-series data. However, their “black-box” nature still makes it hard to use them in safety-critical fields like aerospace engineering, where reliability and interpretability are just as important as accuracy. We suggest a way to measure how uncertain predictions are in Transformer-based RUL models using Monte Carlo Dropout (MCD) in this study. The method is tested on the difficult C-MAPSS FD002 dataset, which has many different operating conditions and failure modes. Our experimental findings indicate that the proposed method not only attains competitive accuracy but also offers a statistically robust metric of predictive confidence. We see a positive correlation (r=0.5296) between the estimated uncertainty and the absolute prediction error. This means that the uncertainty estimates are properly defined and escalate as engines near their end-of-life, which is consistent with engineering intuition. These results are a step toward making AI-based predictions more open and reliable, which will help engineers make decisions based on data with a better understanding of the risks involved.