Real-time online instrument verification and diagnosis method through RBF particle swarm optimization algorithm

Abstract

The invention discloses a real-time online instrument verification and diagnosis method through an RBF particle swarm optimization algorithm. The method comprises the following steps: S1, establishinga flow network model; S2, iterating field actual measurement data, and calculating and determining parameters in the model through an RBF particle swarm optimization algorithm to enable the model tobe available; S3, restarting the above steps regularly, and optimizing parameters; S4, verifying the sampled variables one by one by utilizing the model in a stable flow field state; S5, after the suspected failure point is eliminated, performing inverse iterative operation by using other data, and reversely deriving a theoretical calculation value of the suspected failure point; S6, eliminating process condition changes, performing comparative analysis on the actual instrument signal by using the theoretical calculation value, realizing verification and fault diagnosis, and determining a signal health level; and S7, recording a sampling signal and a calculation signal according to the measurement time, and giving an alarm and positioning a fault according to a deterministic fault diagnosis condition. Instrument faults can be found and reported early, results can be corrected intelligently, and work efficiency is improved.

Description

Technical field [0001] The invention relates to an instant online meter verification and diagnosis method. Background technique [0002] In recent years, more and more attention has been paid to the intelligence and automation of industrial production. In the intelligent manufacturing process, the intelligence of the instrument is an important part of it. At present, mainstream instruments mostly use manual periodic inspections to make judgments one by one. The staff cannot judge whether the measured values ​​of the instruments are accurate in time, which delays the time for processing and affects the entire production activities. When the instrument is working, the intelligent diagnosis of traditional instruments or electronic equipment is only for the instrument itself, and can only perform open-loop self-checking, and cannot verify the accuracy of the data and whether the drift network system is operating normally. Summary of the invention [0003] The purpose of the present ...

AI Technical Summary

Problems solved by technology

At present, most of the mainstream instruments are judged by manual periodic inspection one by one. The staff cannot timely and accurately judge whether the measured value of the instrument is accurate, which delays the timing of processing and affects the entire production activities.

When the instrument is working, the intelligent diagnosis of the traditional instrument or electronic equipment is only for the instrument itself, which can only perform open-loop self-verification, and cannot verify the accuracy of the data and whether the flow network system is running normally.

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