Intelligent fault diagnosis method and system based on multi-source data

By integrating multi-source data and Bayesian inference, the problems of lagging fault detection and difficulty in fault location in industrial networks have been solved, enabling early warning and precise location, and improving the efficiency and accuracy of fault diagnosis.

CN121940274AActive Publication Date: 2026-04-28ENTERPRISE ONLINE (BEIJING) NETWORK CO LTD
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Patent Information

Application Number
CN202610405226.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-31
Publication Date
2026-04-28
Estimated Expiration
2046-03-31

AI Technical Summary

Technical Problem

Existing industrial network fault diagnosis methods suffer from problems such as delayed fault detection, difficulty in fault location, insufficient root cause analysis, and weak multi-source data fusion capabilities, which prevent early warning and accurate location from being achieved.

Method used

A multi-source data fusion method is adopted, which involves synchronously collecting equipment operation logs, network traffic, protocol messages and I/O point data, extracting multi-dimensional feature vectors, establishing a dynamic baseline model, and combining Bayesian inference and network topology to locate fault sources and perform root cause analysis.

Benefits of technology

It enables early warning, precise location, and intelligent diagnosis of industrial network faults, improving operation and maintenance efficiency and diagnostic accuracy, reducing false alarms, and providing complete diagnostic reports and handling suggestions.

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Abstract

The invention discloses an industrial network fault intelligent diagnosis method and system based on multi-source data, and the method comprises the steps: synchronously collecting data from a plurality of data sources of an industrial network, and extracting a time sequence statistical feature, a flow entropy feature and a protocol conformity feature to form a multi-dimensional feature vector; establishing a dynamic baseline model by adopting a sliding window online learning method, and calculating a comprehensive anomaly score for anomaly detection; the fault suspicion degree is calculated based on the equipment incidence matrix and the fault propagation model to realize fault source positioning; carrying out fault type identification and root cause analysis by adopting Bayesian reasoning and a knowledge rule base; and outputting a structured diagnosis report containing the fault source, the type, the root cause and the disposal suggestion. According to the invention, early warning, accurate positioning and intelligent diagnosis of industrial network faults are realized, and the operation and maintenance efficiency, safety and reliability of the industrial control network are significantly improved.
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