Optimized Task Assignment and Predictive Maintenance for Industrial Machines using Markov Decision Process

The importance of predictive maintenance is well-recognized in the industrial sector for several reasons, e.g., it allows for the reduction in machine downtime, it helps in reducing the production cost, and it is useful in enhancing the life of machines. Consequently, predictive maintenance is one of the key areas of research among the scientific community. Initially, the predictive maintenance used to be time-based but later on (with the advances in sensing technology), condition-based maintenance (CBM) gained more popularity. Maintenance of machine tools involve two key stages, i.e., diagnosis and prognosis. Prognosis deals with the prediction of remaining useful life (RUL) of the machine whereas diagnosis is concerned with detection and identification of various faults in the machine. Major approaches for prognosis include data-based approaches, knowledge-based approaches, and physics (model) based approaches. Diagnosis on the other hand is based on centralized or distributed approaches [1]. Key challenges in predictive maintenance include 1) Dealing with the noisy sensor data, 2) Uncertainty in the operating conditions, and 3) Diversity of tasks assigned to the machine. A comparison between time-based and condition-based maintenance strategies has been presented in [2].