Pipeline state recognition method based on monitoring data multi-attribute feature fusion

Abstract

The invention discloses a pipeline state recognition method based on monitoring data multi-attribute feature fusion, and the method comprises the steps: building a state recognition framework based onpipeline state monitoring data, enabling the interior of the framework to comprise normal, blocked and leaked state modes, and extracting the data features of a pipeline operation state based on thestate modes contained in the state recognition framework; selecting the data features by adopting an evaluation index of comprehensive sensitivity and volatility, taking the obtained features as evidence bodies of a state recognition framework, processing the to-be-recognized state data of the pipeline according to the to-be-recognized state data of the pipeline, obtaining corresponding evidence bodies of the to-be-recognized state data of the pipeline, obtaining basic probability values of the evidence bodies, and utilizing D- S evidence theory synthesis rule to perform fusion analysis on allevidence information of the current state, determining the type of the current state belonging to an identification framework, and realizing the identification and judgment of the pipeline operationstate. The invention provides a pipeline operation state identification technology with guiding significance, which is of great significance for improving the accuracy of technical personnel for deciding and judging the pipeline operation state.

Description

technical field [0001] The invention belongs to the technical field of pipeline operation state identification, and in particular relates to a pipeline state identification method based on multi-attribute feature fusion of monitoring data. Background technique [0002] Pipelines are unit parts used to transport liquids, gases, and other substances. They are an important part of modern industrial production systems and are widely used in complex production systems such as petroleum, chemical, and water conservancy. An important part of. Because pipelines usually transport flammable, explosive, and toxic media, and are affected by factors such as load-carrying stress, corrosion of transported media, and aging of parts, they are prone to leakage, blockage, and other failures, which often cause significant personnel and property losses. Therefore, how to use the condition monitoring data during pipeline service to realize the identification and discovery of faults such as small...

AI Technical Summary

Problems solved by technology

However, this method is limited by the accuracy and detail of the operating mechanism of the pipeline system, limited by empirical knowledge and modeling knowledge, and lacks the effective use of a large amount of accumulated pipeline operation status and failure mode data; at the same time, the process of pipeline failure It is a small and gradual process, how to effectively extract the feature information of the failure mode, how to evaluate and select the high-quality features that characterize the pipeline operating state, and conduct information fusion analysis have become problems that need to be solved

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