Research on multisource data fusion-based collective intelligence collaborative diagnosis algorithm models for power equipment

Intelligent algorithms are increasingly applied in complex industrial equipment condition characterization, anomaly detection, and health assessment. Multi-source heterogeneous data fusion has become a critical pathway to enhance the accuracy of collective diagnostics for power equipment. To address the limitation of single data sources in revealing inter-device correlations and dynamic evolution characteristics, this study constructs a multi-source data fusion and collaborative diagnostic model system for power equipment clusters, guided by the requirements of collective collaborative characterization. Methodologically, a cross-modal data alignment strategy is designed to achieve unified representation of multi-source features including electrical signals, mechanical vibrations, and thermal imaging. Furthermore, a swarm intelligence collaborative reasoning mechanism is introduced, enhancing diagnostic stability through interactive feature sharing and incremental confidence updating. Experimental validation employs a comprehensive dataset constructed from a substation scenario, conducting comparative tests on typical fault conditions. Analysis indicates that this approach outperforms traditional models in accuracy, recall, and group consistency metrics. It effectively captures latent coupling relationships among multi-source information and significantly enhances the robustness of group equipment diagnostics. The research concludes that multi-source fusion and group collaboration strategies offer distinct advantages, providing technical support for the intelligent operation and maintenance of large-scale power equipment.