An Explainable Regression Framework for Predicting Remaining Useful Life of Machines
This work addresses the need for trust and interpretability in predictive maintenance for stakeholders, but it is incremental as it applies existing XAI methods to a known domain without introducing new techniques.
The paper tackled the problem of predicting the Remaining Useful Life (RUL) of machines by proposing an explainable regression framework that integrates Explainable AI (XAI) techniques like LIME and SHAP to provide insights into predictions, while evaluating various ML algorithms including classical and neural network-based solutions.
Prediction of a machine's Remaining Useful Life (RUL) is one of the key tasks in predictive maintenance. The task is treated as a regression problem where Machine Learning (ML) algorithms are used to predict the RUL of machine components. These ML algorithms are generally used as a black box with a total focus on the performance without identifying the potential causes behind the algorithms' decisions and their working mechanism. We believe, the performance (in terms of Mean Squared Error (MSE), etc.,) alone is not enough to build the trust of the stakeholders in ML prediction rather more insights on the causes behind the predictions are needed. To this aim, in this paper, we explore the potential of Explainable AI (XAI) techniques by proposing an explainable regression framework for the prediction of machines' RUL. We also evaluate several ML algorithms including classical and Neural Networks (NNs) based solutions for the task. For the explanations, we rely on two model agnostic XAI methods namely Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). We believe, this work will provide a baseline for future research in the domain.