Challenges and Opportunities of Condition-based Predictive Maintenance: A Review

Abstract The concept of maintenance has advanced significantly over the last decades from a reactive service activity towards a pro-active one with more C-level attention. Among them, predictive maintenance has become a widely used term in industrial arena and academic research. This is being developed constantly by engineers and researchers based on monitoring historical data, modeling, simulation, and failure probabilities to predict fault and system deterioration over their useful life. Generally, the effective lifetime of machines depends on available and accessible data. However, certain unexpected situations may arise that are hard to predict, such as shock damage and unwanted degradation of the tool. Researchers are still working on understanding these problems and their effects on predictive maintenance. This paper presents an overview of condition-based predictive maintenance solutions that aim to avoid unexpected and unplanned failures during the manufacturing and operational process based on advanced data analytics. Furthermore, a brief illustration & discussion is presented on the challenges and opportunities of condition-based predictive maintenance and conclude a summary on future research.