Abstract: This paper takes the Yongqing 330 kV substation as a case study to develop a condition-based maintenance technology system for high-voltage electrical equipment. During the implementation stage, real-time condition monitoring of key equipment such as circuit breakers, current transformers, and cable systems is achieved by deploying online monitoring devices and multi-source data acquisition systems. A BP neural network and expert system fusion algorithm are adopted to perform equipment health assessment and fault classification diagnosis. Meanwhile, a hierarchical maintenance strategy and differentiated response mechanism are formulated. The test results show that the system achieves a recognition accuracy of over 96%, a response time within 10 ms, and excellent data integrity. The research results indicate that this approach can effectively enhance the intelligence, predictability, and safety of substation operation and maintenance, and is suitable for the digital management requirements of high-voltage substation equipment in the process of rural electrification.