This study introduces a new diagnostic framework that combines improved particle swarm optimization, K-means clustering algorithms, support vector machine (SVM), and learning vector quantization neural networks to provide a comprehensive fault diagnosis and pre-diction model. This study introduces a new diagnostic framework that combines improved particle swarm optimization, K-means clustering algorithms, support vector machine (SVM), and learning vector quantization neural networks to provide a comprehensive fault diagnosis and pre-diction model. Then, due to the particularity of historical statistical data, a weight calculation method combining analytical hierarchy process (AHP) and entropy weight method is adopted to eliminate subjective factors in the weight calculation process. Meanwhile, the equipment operation risk level was. To promptly detect the faults of the relay protection system and the circuit breakers in time and to ensure the operational reliability of these protective devices, this paper proposes a fault tracing method for a relay protection system–circuit breaker based on improved Random Forest. This study introduces a new diagnostic framework that combines improved particle swarm optimization, K-means clustering.