Process early warning method and system for analysis and estimation of full-measurement-point coupling structure

Abstract

The invention discloses a process early warning method and system for analysis and estimation of a full measurement point coupling structure. The invention provides a full-measurement-point synchronous estimation and monitoring model based on analysis of a coupling structure between measurement points and measurement point estimation errors, and aims to solve the problems that an original monitoring method based on working condition estimation is incomplete in monitoring and insufficient in modeling of a coupling relation between the measurement points. On the basis that full measurement points are regarded as a full measurement point diagram, a multi-kernel graph convolutional layer is provided and applied to a full measurement point synchronous estimation and early warning model, and synchronous working condition estimation and monitoring of full sensor measurement points are achieved by explicitly modeling the coupling relation between the measurement points. According to the method, a self-iteration strategy based on feature approximation is designed for the provided model, so that the problem that estimation values of part of measuring points are abnormal due to strong coupling between the measuring points when a system is abnormal in a traditional method is solved, and the method has important significance on accurate monitoring of the industrial process.

Description

technical field [0001] The invention belongs to the field of industrial equipment operation process monitoring, and includes a self-iterative multi-core graph convolution estimation model for full-measurement point coupling structure analysis, a feature approximation-based self-iteration strategy, and a process early warning method and system applying the model and the strategy. Background technique [0002] Industrial process operation status monitoring is of great significance for improving production efficiency and ensuring production safety. Recognition of operating conditions in complex industrial environments and intelligent modeling of complex industrial systems are also one of the important research directions of industrial artificial intelligence. Due to the continuous and uninterrupted operation of industrial processes, the propagation and evolution of faults among different levels makes the monitoring and diagnosis of industrial processes a complex problem. In or...

AI Technical Summary

Problems solved by technology

However, when the physical quantities measured by some measuring points change abnormally for some reason, it may cause the model to estimate the normal measuring points to be abnormal at the same time, that is, generate false alarms; or cause the model to estimate some abnormal measuring points Anomalies occur, resulting in the inability to identify some abnormal measuring points, that is, missing alarms, which is also the main difficulty in abnormal monitoring based on soft measurement or estimation methods

This type of method lacks an effective error correction mechanism for abnormal situations

Images