Online abnormality monitoring and diagnosis method and system

Abstract

The invention provides an online abnormality monitoring and diagnosis method and system. Among them, the method includes: defining the causality and conditional relationship of all signals based on the directed graph model; based on the definition of the signal by the directed graph model, classifying the signals in the acquired historical data and establishing a training set of historical data for model training And determine the model parameters; based on the real-time data theoretical value of the signal in the real-time data obtained by putting the real-time data obtained by the online monitoring into the trained model, determine whether the signal in the real-time data is abnormal. A system applying the method and a computer-readable medium storing a program for executing the method are also included. To solve the problem of building a complete logical relationship and causal relationship for the entire process of industrial production and operation through a directed graph model, and combining machine learning to achieve a more reasonable, correct, accurate and efficient online production abnormality diagnosis and prediction.

Description

technical field [0001] The present application relates to abnormal diagnosis when a fault occurs in industrial production, and in particular to an online abnormal monitoring and diagnosis method and system. Background technique [0002] In industrial production, the existing abnormal monitoring and fault diagnosis are usually based on a large amount of production data, and the logical causal relationship is discovered through algorithms, so as to help administrators realize monitoring and diagnosis. The practicability of monitoring and diagnosis is not strong, and the false alarm rate is extremely high, sometimes even reaching more than 99%. [0003] Taking the coal mill system of the power plant system as an example, usually, the operation monitoring of the coal mill system is carried out by using the centralized and distributed control system DCS. DCS installs sensors on the terminal equipment of the coal mill system, and the sensors convert the collected signals through ...

AI Technical Summary

Problems solved by technology

[0007] The main purpose of this application is to provide an online abnormality monitoring and diagnosis method and system to solve the problem of building a complete logical relationship and causal relationship for the entire process of industrial production and operation through a directed graph model, and combine machine learning to achieve a more reasonable, more correct, More accurate and efficient diagnosis of predicted problems

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