Fault Diagnosis Technology of Large Electromechanical Equipment Based on Machine Learning

Modern mechanical and electrical equipment is prone to failure due to its complex structure, force condition and large-scale influence of environmental factors. At present, these problems have been studied at home and abroad. In this paper, the traditional experience summary method and Bayesian network method are used to model and simulate a large mechanical equipment, and the conclusion is drawn. At the same time, this paper proposes an algorithm to solve the above problems, and verifies the rationality and accuracy of the model. The feasibility and effectiveness of the algorithm are verified by the experimental results. The results are as follows: as time goes on, the response time increases gradually, which are $0.8,1.2,0.9,1.5$ and 0.7 respectively. At the fourth time point, the response time reached the highest value of $\mathbf{1. 5}$. These changes illustrate the trend of increasing failure response time over time. The low response time indicates that the system can quickly detect and diagnose faults, improve the reliability and availability of the equipment, and provide a reference value for the future application of artificial intelligence technology in electromechanical equipment.