The role of machine learning in predictive maintenance for industry 4.0

This paper aims to assess the importance of ML in the field of Predictive maintenance of Industry 4.0. Industry 4.0 is a move to smart factories with automation and integration of things. Therefore, predictive maintenance enables a strategy for minimizing costs and maximizing equipment reliability and availability. While traditional maintenance methodologies entail repair after equipment has failed or routine checks are made after a set time, predictive maintenance works hand in hand with machine learning algorithms, big data, and IoT sensors to estimate when equipment is likely to fail. Methods like supervised learning, unsupervised learning, time series, and learning and deep learning make it possible to predict failure rates because of data from equipment used in the production process. Introducing and, most importantly, integrating the predicting maintenance technique is more efficient in reducing production loss due to regular maintenance, is cheaper to conduct than the conventional methods, and uses little resources on regular maintenance, as informed by the maintenance of predictive analysis. However, as stated by several authors, there is still more work to be done in order to explore the potential of ML to support predictive maintenance fully, specifically data quality, interpretability of the model, and scalability. With industries introducing more uses of ML and IoT, predictive maintenance will continue to be a norm in industrial processes, leading to improvement in reliability, low operational risks, and increased competitiveness.