Radiator abnormal state detection method based on sensing data fusion

By deploying multiple types of sensors in the radiator and performing data fusion processing, a directed acyclic graph is constructed for causal analysis, which solves the problem of accurate positioning of radiator anomaly detection in the existing technology and improves the detection accuracy and response efficiency.

CN120597162AInactive Publication Date: 2025-09-05DONGGUAN SHIRUI MECHANICAL & ELECTRICAL TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510711476.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-29
Publication Date
2025-09-05
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing radiator anomaly detection cannot accurately locate the root cause of the anomaly, resulting in the detection system relying on manual experience, which is time-consuming and prone to misjudgment, affecting response efficiency and maintenance accuracy.

Method used

Based on the sensor data fusion method, multiple types of sensors are deployed in the radiator structure, acquisition time alignment and sliding time window segmentation processing are performed, frequency domain texture features are constructed, a directed acyclic graph is generated, causal weight calculation and abnormal node identification are performed, and the root cause node is traced back.

Benefits of technology

The accuracy and traceability of radiator system anomaly detection have been improved, the control response capability has been enhanced, and the reliance on misjudgment and manual intervention has been reduced.

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Abstract

The invention discloses a sensing data fusion-based radiator abnormal state detection method, relates to the technical field of radiator state detection, and is used for solving the problem of poor radiator state detection efficiency. According to the method, multiple types of sensors are arranged in combination with a radiator structure, acquisition time alignment is completed, segmentation and de-noising processing is performed on multi-modal data based on a sliding time window, and a frequency domain texture feature is constructed by extracting a frequency spectrum information entropy and a high-frequency energy ratio; constructing the feature values into graph nodes, establishing a complete connection graph, compressing the complete connection graph into a skeleton graph through conditional independence test, generating a directed acyclic graph in combination with an intersection structure and topological sorting, and calculating a causal weight to realize structure updating; and extracting a current window node state, comparing the current window node state with a prediction state, identifying an abnormal node, executing path backtracking, calculating path cost, tracing to a root cause node, extracting path evaluation information, and generating a response signal, thereby improving the abnormality diagnosis precision and scheduling linkage responsivity of the radiator.
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Citation Information

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