This study develops a visualization monitoring system for substation equipment operational status, utilizing mobile monitoring technologies. The system architecture integrates a core functional module aligned with comprehensive system requirements, enhanced by a BP neural network to optimize the server’s data mining capabilities. The research focuses on the analysis of typical faults in crucial substation electrical equipment, applying a Fourier algorithm for preprocessing the fault data. Employing the diagnostic principles of the BP neural network, the study designs a thermal fault diagnosis process for the substation apparatus. Experimental scenarios were established to evaluate the BP neural network’s performance by comparing three linear regression sample values. The practical application of the BP neural network model was assessed through integration with substation field data. Cross-validation of the field data indicates that the fault location algorithm accurately identifies 11 types of faults from 85 alarm signals in the secondary condition monitoring of substations, achieving a reliability of 98% or higher, which underscores its high applicability and operational feasibility.