Central air conditioning fault prediction method based on multi-source data fusion

Abstract

The application discloses a central air conditioner fault prediction method based on multi-source data fusion, relates to the technical field of air conditioner fault monitoring, and specifically comprises a closed-loop fault prediction system including a weak feature adaptive detection module, a multi-source data dynamic fusion module, a fault explainable attribution positioning module, a verification feedback module and a feedback scheduling center; a model-specific weak feature library is called to focus on, enhance and determine the weak features related to early faults; the multi-source features are dynamically weighted, redundantly identified and fused; based on the explicit mapping relationship and the feature contribution quantification results, the fault type, fault position and confidence are output; then, the consistency verification is performed in combination with the subsequent operation state, after-sales records and / or artificial review results, and the collaborative regulation instructions are formed through the feedback scheduling center to realize the closed-loop optimization of the model and rules. The method can improve the identification capability of the early weak faults of the central air conditioner, the fusion effectiveness and the explainability of the fault positioning.

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