Predictive Maintenance of Equipment Driven by Machine Learning

Industry is the leading sector of the national economy, and equipment maintenance, as a key link within it, ensures the stability of industrial production, improves production efficiency, and is crucial to industrial development. This paper studies the current application status and future development of machine learning-based equipment predictive maintenance in mechanical manufacturing through literature review and case analysis. The research reveals that machine learning algorithms, such as supervised learning, unsupervised learning, and deep learning, can significantly enhance the accuracy of predictive maintenance. Case studies on medical equipment using Weibull Distribution, new energy vehicles using Convolutional Neural Networks (CNN), and concrete-filled steel tubular shear walls using Random Forest demonstrate the effectiveness of these algorithms in predicting failures and optimizing maintenance strategies. The findings suggest that integrating machine learning with predictive maintenance can reduce costs, improve efficiency, and enhance safety. Future work should focus on multi-technology integration and industry-specific customization to further advance the field.