A Deep Explainable Model for Fault Prediction Using IoT Sensors

Abstract

IoT sensors and deep learning models can widely be applied for fault prediction. Although deep learning models are considerably more potent than many conventional machine learning models, they are not transparent. This paper first examines different deep learning techniques to carry out univariate time series analysis based on vibration sensors installed on four industrial bearings to predict a fault occurring in a predefined time window. Several recurrent neural networks are used to develop fault prediction models. An empirical evaluation of these models shows that all models perform well; however, hybrid models outperform other models when the time window increases. Then, instance-wise feature selection has been considered to highlight the most contributing features for its outputs regarding any input. In this problem, the main challenge is to propose a trainable feature selection model with the minimum number of selected features whilst its performance is close to the baseline model. This paper develops a novel explainable method called the Gumbel-Sigmoid eXplanator (GSX) to tackle these problems. In a nutshell: (i) we have developed a differentiable and trainable selector, and (ii) we utilize regularization to control the number of features for each instance flexibly. The proposed method is model agnostic, and empirical evaluations on two datasets show that GSX can not only solve the problems identified with two other state-of-the-art methods but also outperform them in terms of accuracy and run-time.