Plant-level process fault detection and diagnosis method based on distributed data model

Abstract

The invention discloses a plant-level process fault detection and diagnosis method based on a distributed data model. Aiming at the complex large plant-level production process, the existing commonly used fault detection and diagnosis methods are mostly built on the basis of a data driving model, wherein the most typical method includes first dividing the whole plant-level process into a plurality of sub modules according to operation units and functions and then building corresponding fault detection and diagnosis models aiming at the different sub modules respectively. The methods often rely on process knowledge to a large extent, and the requirement is harsh for the complex plant-level process. A novel method for dividing the sub modules completely based on data analysis is provided, and the corresponding fault detection and diagnosis method is constructed on the basis to achieve the whole situation monitoring of the plant-level process. The method has important application value for improving fault detection and diagnosis performance and product quality in the plant-level production process.

Description

technical field [0001] The invention belongs to the field of safety monitoring and quality control of industrial production process, in particular to a plant-level process fault detection and diagnosis method based on a distributed data model. Background technique [0002] With the increasing production scale of modern chemical process, a new plant-level process has attracted more and more attention. The modeling, monitoring and control of plant-level processes has also received extensive attention from academia and industry. A typical plant-level process usually includes multiple operating units, multiple process devices, and even multiple operating workshops. For such a complex large-scale chemical process, it will be very difficult to adopt traditional fault detection and diagnosis methods based on mechanism models. In contrast, due to the large amount of data accumulated in the process, the data-based method is more suitable for complex and large-scale plant-level proc...

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Problems solved by technology

However, such methods often rely heavily on expert knowledge of plant-level processes, which is very demanding for complex plant-level processes

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