Reliability Evaluation and Prediction of Mechanical System Based on Machine Learning Technology

The traditional mechanical reliability evaluation method has high demand for data quality and data quantity, and requires a lot of time and manpower in data collection and processing, and there are some errors in the analysis results. The application of machine learning techniques in reliability assessment can predict errors in mechanical systems and make maintenance decisions. By exploring the influence of machine learning technology on the reliability evaluation and prediction of mechanical system, it is found that the application of machine learning techniques can increase the prediction precision and recovery of reliability assessment models for mechanical systems. The accuracy rate has increased by about 8%, the recall rate has increased by 10%, and users are very satisfied with the reliability assessment model of mechanical systems based on metal learning techniques. The results of this paper prove that machine learning technology has broad application prospect and practical significance in the reliability evaluation and prediction of mechanical systems.