Oil delivery pump fault identification method, device and system based on multi-source information fusion

Abstract

The embodiment of the invention discloses an oil delivery pump fault identification method, device and system based on multi-source information fusion. The method comprises the following steps that amulti-source heterogeneous risk scene database corresponding to an oil delivery pump is constructed; a target risk scene is screened from the multi-source heterogeneous risk scene database according to the scene importance degree of the risk scenes in the multi-source heterogeneous risk scene database; monitoring parameters corresponding to the target risk scene are acquired, wherein the monitoring parameters at least comprise one or more kinds of a degradation state characterization parameter and a process parameter; and fusion parameters of the oil delivery pump are determined according to the monitoring parameters corresponding to the target risk scene, and the fusion parameters are utilized to perform fault identification on the oil delivery pump. By utilizing the embodiments of the specification, the accuracy of fault identification of the oil delivery pump can be improved.

Description

technical field [0001] The present invention relates to the technical field of pipeline oil transportation equipment diagnosis, in particular, to a method, device and system for identifying failures of oil transportation pumps based on multi-source information fusion. Background technique [0002] With the development of the national economy, pipeline transportation has occupied an increasingly important position in modern industry. As the key equipment for pipeline oil transportation, the oil pump is an important factor to ensure the normal operation of pipeline transportation. With the continuous advancement of the digitalization process, more and more sensors such as vibration sensors, temperature sensors, and pressure sensors are applied to the on-site status monitoring of oil pumps, and a large amount of monitoring data is generated every day. How to use a large amount of monitoring data to realize accurate and efficient diagnosis of fuel pump component failures is of ...

AI Technical Summary

Problems solved by technology

This makes the data finally used for fault identification unable to guarantee the characterization ability of the system state, which in turn affects the accuracy of oil pump fault diagnosis

Secondly, the existing fault identification methods have less consideration for the redundancy between parameters

There is often a certain redundancy between the parameters. For example, some features (such as mean value, peak-peak value, etc.) extracted from the vibration signal have a certain similarity, that is, there is information redundancy between the features. If not processed, It will cause the data used for fault identification to be biased towards redundant information, which will affect the accuracy of identification results

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