Machine Learning Based Remaining Useful Life Estimation—Concept and Case Study

With advancements in technology and machinery, human dependencies on them are increasing. This increased reliance makes maintenance in industrial applications indispensable. Traditional methods, like Reactive Maintenance, fail to detect problems beforehand and can jeopardize resources and/or lives. Proactive Maintenance measures, especially Predictive Maintenance has gained popularity with the advent of tons of data-handling resources. Remaining Useful Life (RUL) is an integral and principal measure of Predictive Maintenance that can give a fair indication of the usefulness of the component to decide when it needs to be replaced or repaired. Accurate prediction/estimation of RUL calls for developing data-driven methods like Machine Learning algorithms. We elaborate on relevant predictive maintenance concepts and describe how ML techniques can be effectively applied to predict the remaining useful life of machine components. We also demonstrate a case study using NASA’s CMAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset. The case study incorporates the successful implementation of ML algorithms and the subsequent use of Evolutionary Computing techniques like Particle Swarm Optimization for optimization.