Method for detecting multi-period oscillation behaviors of industrial control loops

Abstract

The invention discloses a method for detecting multi-period oscillation behaviors of industrial control loops. The method includes the following steps: (1) collecting a set of process data xt in the to-be-detected control loops, (2) carrying out wavelet packet conversion decomposition on the process data xt to obtain an original binary-tree-shaped decomposition structure T0, (3) simplifying the original binary-tree-shaped decomposition structure T0 according to the minimum shannon entropy rule, the non-Gaussian indexes and the quasi oscillation indexes to obtain an optimal binary-tree-shaped decomposition structure Tbest, and (4) signing subspace corresponding to non-Gaussian distribution leaf nodes according to the non-Gaussian indexes TG of all leaf nodes on the optimal binary-tree-shaped decomposition structure Tbest to obtain a coordinate position set I corresponding to the leaf nodes with the signed subspace. By means of the method, the multi-period oscillation behaviors of the industrial control loops can be quantitatively detected, the regulation degrees and the periods of oscillation components of multi-period oscillation are obtained, and abundant data supports are provided for evaluation and fault source diagnoses of the oscillation behaviors.

Description

technical field [0001] The invention relates to the field of performance evaluation in industrial control systems, in particular to a multi-period oscillation behavior detection method of an industrial control loop. Background technique [0002] Modern industrial process equipment has the characteristics of large scale, high complexity, many variables, and operates under closed-loop control. For complex chemical production processes, there are often thousands of loops, and these loops affect each other due to coupling. The oscillation phenomenon of the control loop greatly affects the economic benefit and stability of the operation of industrial process equipment due to the ubiquity of controller over-tuning, external disturbance and non-linear working characteristics of the control valve. [0003] Preliminary and accurate oscillation detection of industrial process equipment can reduce downtime, increase the safety of industrial process equipment operation, and reduce manuf...

AI Technical Summary

Problems solved by technology

The detection method based on time-domain statistics and autocorrelation function domain analysis has three limitations in practical application: first, this method requires a certain prior understanding of the detection process and the determination of empirical parameters; The oscillation period cannot be fully automatic and non-intervention detection, and a targeted pre-designed filter is required for data stabilization and oscillation separation. Third, most detection algorithms cannot quantitatively calculate the degree of oscillation regularity

At present, the oscillation detection method based on signal decomposition of process data has progressed compared with the above detection methods, but the main limitation is that the number of sub-signals obtained by the existing signal decomposition technology is redundant, and the optimal decomposition structure suitable for oscillation detection cannot be obtained. , the oscillating component signal obtained by this non-orthogonal decomposition is not well representative, and the fitting degree to the trend of the non-stationary signal is relatively poor, and the computational complexity is relatively high

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