Compressor unit health prediction method based on time domain data association modeling

Abstract

The invention discloses a compressor unit health prediction method based on time domain data association modeling, relates to the technical field of pipe network operation assembly detection, and aims to solve the problem that early warning is difficult to provide at the initial stage of equipment failure in a failure and post-failure analysis mode. The method is technologically characterized by comprising the steps of establishing a model, establishing a state data sensing model, and establishing a fault mechanism correlation model; collecting normal parameters, obtaining a normal working condition state health degree reference value through a state data perception model, and recording fault change rules by a fault mechanism association model under different fault conditions; performing comparative analysis of data extraction: analyzing the state data sensing model and the failure mechanism correlation model for multiple times to obtain an average value; and performing data abnormity alarm. And the effects of prediction diagnosis, early warning and timely reminding of compressor unit equipment faults are achieved.

Description

Technical field [0001] Technical Field The present invention relates to the detection operation of the pipe network components, particularly to a prediction method of a compressor set of health data based on the associated time-domain modeling. Background technique [0002] Normal operation of the compressor safety device group related to the normal transport network, and stable operation at an early stage of compressor equipment failure due to a malfunction wherein relatively insignificant, little effect on the normal operation of the network, the prior art methods are not monitoring changes early failure stage equipment, it is often easily overlooked, and when a fault developed to a more serious stage, there is an alarm occurred is detected, the damage to the equipment often already occurred, in addition to the need for equipment maintenance requires a lot of manpower outside material and the device itself may stop working at any time thereby affecting the stable operation of t...

AI Technical Summary

Problems solved by technology

[0004] The existing technical solutions mentioned above have the following defects: it is difficult to realize the overall and quantitative health of the operation status of the compressor unit equipment in the way of analysis during and after the failure It is also difficult to provide early warning in the early stage of equipment failure, and the model driven by the data model also has the disadvantages that the early warning is not qualitative, there are many false alarms in the early warning, and the practicability is not strong

Images