difLIME

Predictive maintenance, within the field of Prognostics and Health Management (PHM), aims to identify and anticipate potential issues in equipment before they become serious problems. Deep Learning (DL) models, such as Deep Convolutional Neural Networks (DCNN), Long Short-Term Memory (LSTM) networks, and Transformers, have been widely adopted for this task and have shown great success. However, these models are often considered "black boxes" due to their opaque decision-making processes, making it challenging to explain their outputs to industrial equipment experts. The complexity and vast number of parameters in these models further complicate understanding their predictions. This paper introduces a novel Explainable AI (XAI) algorithm, an extension of the well-known Local Interpretable Model-agnostic Explanations (LIME). Our approach uses a conditioned Probabilistic Diffusion Model to generate altered samples in the neighborhood of the original sample studied. We validate our method using various rotating machinery diagnosis datasets. Additionally, we compare our approach with state-of-the-art XAI methods, employing nine metrics to evaluate the desirable properties of any XAI method.