Online self-adaptive fault monitoring and diagnosis method for process industry course

Abstract

The invention discloses an online self-adaptive working condition monitoring and fault diagnosis method for a process industrial course and belongs to the technical field of fault monitoring and diagnosis of complex industrial courses. The method comprises the following steps of firstly, analyzing historical observation data under a normal working condition, introducing an elastic regression network combining Lasso constraints with Ridge constraints to establish an industrial course fault monitoring model on the basis of sparse principal component analysis, and then obtaining a course controllimit to industrial course fault monitoring statistics; during online monitoring of industrial course faults, adopting an order-1 matrix correcting algorithm for resolving a covariance matrix of the online monitoring data, conducting recursion updating on a load matrix of the sparse monitoring model to obtain the course control limit to the course fault monitoring statistics matched with the working condition, and achieving self-adaptive fault detection in the process industry course; finally, according to the detected faults, adopting a contribution plot method for obtaining specific causes of the faults. By means of the method, the faults of the process industry course with complex and changeable working conditions can be self-adaptively monitored for a long time; the method has the advantages of low calculation complexity, high precision, a low report missing rate and the like.

Description

technical field [0001] The invention relates to the field of industrial process automation monitoring, in particular to a method and technology for fault monitoring and diagnosis of process industrial processes. Background technique [0002] Process industry, also known as process industry, refers to the production process through physical changes and chemical changes. The process industry mainly includes basic raw material industries such as petroleum, chemical industry, iron and steel, non-ferrous metals, and building materials. It is the pillar and basic industry of the national economy and an important supporting force for my country's sustained economic growth. [0003] Process safety, product quality, energy conservation, emission reduction and efficiency enhancement are the core goals of the modern process industry. A good industrial process operating condition is the key to stabilizing production indicators, ensuring product quality, and realizing stable and optimize...

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Problems solved by technology

[0006] However, the traditional multivariate statistical analysis method usually assumes that the process variables are static, that is, the linear relationship between the variables does not change with the operation of the process. However, in most actual industrial production processes, the raw material composition of process production is complex Various factors such as variability, human disturbance in process operation, wear and tear of production equipment, sensor drift, and environmental differences in physical and chemical reactions in the process will cause changes in the state of industrial processes.

In this case, the normal working condition monitoring model established based on static historical data is often unable to adapt to the dynamic changes of complex industrial processes, especially for process monitoring and fault diagnosis of long-term continuous operation The model is extremely prone to problems such as false positives, missed negatives, and insufficient response

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