论文标题
一个可解释的回归框架，用于预测机器的剩余使用寿命
An Explainable Regression Framework for Predicting Remaining Useful Life of Machines
论文作者
论文摘要
对机器剩余使用寿命的预测（RUL）是预测维护的关键任务之一。该任务被视为一种回归问题，其中使用机器学习（ML）算法来预测机器组件的规则。这些ML算法通常用作黑匣子，完全关注性能，而无需确定算法的决策及其工作机制背后的潜在原因。我们认为，仅凭平方误差（MSE）等方面的表现不足以建立在ML预测中利益相关者的信任，而是对预测背后的原因的更多见解。在本文中，我们通过为预测机器Rul的可解释回归框架提出了可解释的回归框架来探讨可解释的AI（XAI）技术的潜力。我们还评估了几种ML算法，包括基于经典和神经网络（NNS）的解决方案。为了解释，我们依靠两种模型不可知的XAI方法，即局部可解释的模型 - 敏捷解释（Lime）和Shapley添加说明（SHAP）。我们相信，这项工作将为未来在域中进行研究提供基准。
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.
