Mechanical Equipment Failure Prediction Based on Machine Learning

Abstract

Mechanical equipment plays a central role in the manufacturing industry. However, they often suffer from problems such as aging and wear, which pose a major challenge to maintaining the stability of the production environment and improving economic efficiency. In order to solve this problem, this article conducted an in-depth study using the usage and failure data of a certain enterprise's mechanical equipment. First, this article establishes an XGBoost classification model to determine whether mechanical equipment has malfunctioned. The model performed well, with performance metrics showing high precision, recall, and F1 scores. Furthermore, this paper used the XGBoost model to successfully predict the faults of 19 mechanical equipment, classified these devices, and accurately determined the types of faults, including heat dissipation faults, power faults and overload faults. Through the weight analysis of various types of fault data using the CRITIC weight analysis method, the main causes of various types of faults are clarified, which provides an important reference for further improvement and maintenance of this mechanical equipment.