On the Soundness of XAI in Prognostics and Health Management (PHM)

Abstract

The aim of predictive maintenance, within the field of prognostics and health management (PHM), is to identify and anticipate potential issues in the equipment before these become serious. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effectively before it fails, which is known as remaining useful life (RUL). Deep learning (DL) models, such as Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks, have been widely adopted to address the task, with great success. However, it is well known that these kinds of black box models are opaque decision systems, and it may be hard to explain their outputs to stakeholders (experts in the industrial equipment). Due to the large number of parameters that determine the behavior of these complex models, understanding the reasoning behind the predictions is challenging. This paper presents a critical and comparative revision on a number of explainable AI (XAI) methods applied on time series regression models for PM. The aim is to explore XAI methods within time series regression, which have been less studied than those for time series classification. This study addresses three distinct RUL problems using three different datasets, each with its own unique context: gearbox, fast-charging batteries, and turbofan engine. Five XAI methods were reviewed and compared based on a set of nine metrics that quantify desirable properties for any XAI method. One of the metrics introduced in this study is a novel metric. The results show that Grad-CAM is the most robust method, and that the best layer is not the bottom one, as is commonly seen within the context of image processing.

Explainable Predictive Maintenance of Rotating Machines Using LIME, SHAP, PDP, ICE

“…Study in [65], [49] and [3] presents the use of SHAP in explaining predictions made for bearing faults. Another study [66] proposes Grad-CAM method superior in comparison with LIME and SHAP in explaining the predictions made for gearbox faults in rotating machines. In most comparable studies, researchers predominantly concentrate on a singular fault type, such as bearings, rather than addressing multiple faults.…”

A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing

“…In this section, we introduce the metrics used in the found literature to assess XAI methods. The following are the desirable characteristics that each XAI method should accomplish [16]:…”

“…Ref. [16] used regression CNN models on time series data for PM. Then, they applied different XAI methods to explore their performance.…”

“…The study [16] worked on time-series data using the gearbox, fast-charging batteries, and turbofan datasets from the full dataset, named Turbofan Engine Degradation Simulation-2. They applied CNN as a black box model to predict the remaining useful life and reviewed five XAI methods (LIME, SHAP, LRP, GradCAM, and ISCSal).…”