This paper is based on the theoretical knowledge of electrical automation and the composition and basic principles of AC drive systems to determine the overall design of the system integration structure in this paper. The reliability of the system equipment is assessed jointly by combining the Weibull distribution, least-squares regression fitting, and Bayesian network after completing the system integration design. The results show that the fitted regression equation for the equipment failure data is y = 0.3372x − 3.0521, which explains that this data can be used for reliability analysis. The highest a posteriori probability value (0.00899) for the AC motor in the Bayesian network model of the AC drive system indicates that the AC motor should be prioritized for overhauling in the course of system failure to achieve the purpose of intelligent detection and control of the system.