Explainable AI for supporting operators in manufacturing machines maintenance : Evaluating different techniques of explainable AI for a machine learning model that can be used in a manufacturing environment

Abstract: Monitoring and predicting machine breakdowns are of vital importance in the manufacturing industry. Machine Learning models could be used to improve these breakdown predictions. However, the operators responsible for the machines need to trust and understand the predictions in order to base their decisions on the information. For this reason, Explainable Artificial Intelligence, XAIs, was introduced. It is defined as the set of Artificial Intelligence systems that can provide predictions in an intelligible and trustful form. Hence, the purpose of this research is to study different techniques of Explainable Artificial Intelligence XAIs in order to discover the most suitable methodology for allowing people without a machine learning background, employed in a manufacturing environment, to understand and trust predictions. Four XAI interfaces have been tested: three integrated XAI techniques were identified through a literature review, and one was presenting an experimental XAIs facility based on a machine learning model for outliers identification. In order to predict future machines’ states, classifiers based on Random Forest were built, while for identifying anomalies a model based on Isolation Forest was built. In addition, a user study was carried out in order to discern end-users perspectives about the four XAI interfaces. Final results showed that the XAI interface based on anomalous production values gained high approval among users with no or basic machine learning knowledge.