Adding interpretability to predictive maintenance by machine learning on sensor data

Abstract Condition-based maintenance (CBM) is becoming more commonplace within the petrochemical industry. While we find that previous research leveraging machine learning has provided high accuracy in the predictive aspect of machine breakdowns, the diagnostic aspect of these approaches is often lacking. This paper implements a supervised machine learning approach, with the goal of both prediction and diagnosis of machinery breakdowns, emphasizing the latter. To achieve this, it uses an XGBoost model trained on a combination of sensor and report data, and enriches the model with Shapley values for diagnostic insights. We show that this combination of statistical methods, combined with a proper data treatment, can be used to great effect and can vastly improve the diagnostic value of machine learning approaches. The insights that follow from the analysis can subsequently be leveraged by plant operators in CBM strategies or root-cause analyses.