Smart Machine Health Prediction Based on Machine Learning in Industry Environment

Abstract

In an industrial setting, consistent production and machine maintenance might help any company become successful. Machine health checking is a method of observing the status of a machine to predict mechanical mileage and predict the machine’s disappointment. The most often utilized traditional approaches are reactive and preventive maintenance. These approaches are unreliable and wasteful in terms of time and resource utilization. The use of system health management in conjunction with a predictive maintenance strategy allows for the scheduling of maintenance times in such a way that device malfunction is avoided, and thus the repercussions are avoided. IoT can help monitor equipment health and provide the best outcomes, especially in an industrial setting. Internet of Things (IoT) and machine learning models are quite successful in providing ongoing knowledge and comprehensive study on infrastructure performance. Our suggested technique uses a mobile application that seeks to anticipate the machine’s health status using a classification method utilizing IoT and machine learning technologies, which might benefit the industry environment by alerting the appropriate maintenance team before inflicting significant harm to the system and disrupting normal operations. A comparison of decision tree, XGBoost, SVM, and KNN performance has been carried out. According to our findings, XGBoost achieves higher classification accuracy compared to the other algorithms. As a result, this model is selected for creating a user-based application that allows the user to easily check the state of the machine’s health.