Machine Learning-Driven Predictive Maintenance Models for Industrial Equipment and Manufacturing Systems

Abstract

There has been an escalation in the complexity of industrial machines and factory structures which have exacerbated the necessity of smart maintenance systems that can minimize unexpected downtime and operation expenses. This study is a detailed work on the predictive maintenance models with machine learning, applying to a multivariate sensor data of industrial machinery. Data-driven models of fault classification, anomaly detection and the prediction of the remaining useful life have been developed using vibration, temperature, pressure, acoustic and electrical signals. Four machine learning algorithms, namely Random Forest, Support Vector machine, Long Short-term memory (LSTM), and Isolation Forest were applied and tested in the context of the realistic industrial setting. As shown in the experimental results LSTM model has provided the best accuracy in classification of fault of 95.1 percent with an F1-score of 0.94 which is best in the sense that it is able to capture the temporal degradation patterns. Random Forest model also performed well with an accuracy of 94.2 which is better in providing better interpretability and fast inference. In unsupervised anomaly detection, Isolation Forest reported the highest rate of detection as 88.7% and false alarm low rate of 6.4% and therefore is feasible in early fault detection when there are scarce labeled data. In the case of the useful life prediction, the LSTM model minimized the error involved in prediction, whereby its mean absolute error was 11.2 hours, which is lower than 18.6 hours in case of the conventional models. On the whole, the findings prove the idea that machine learning-based predictive maintenance contributes to a stronger degree of reliability, optimization of the maintenance process, and helps to make decisions based on the available data in the context of the contemporary manufacturing system.