Subsystem fluctuation signal analysis method based on causal analysis

Abstract

The invention discloses a subsystem fluctuation signal analysis method based on causal analysis, and the method comprises the steps: carrying out the decomposition of a multi-dimensional signal through employing an MEMD method, obtaining the Imf of each layer of all signals, carrying out the grouping of each layer Imf of each signal based on a normalized correlation coefficient, correcting the group based on a sparse index, and carrying out the analysis of a fluctuation signal. A harmonic detection method is adopted to search for fundamental waves and harmonic waves Imf which are in an integral multiple relation in the groups, signals with the fundamental waves and the harmonic waves at the same time are selected to serve as the result of grouping harmonic detection feature selection, denoising and periodic term removing processing is conducted on fluctuation signals subjected to feature selection, the remaining part is reconstructed, and corresponding target sub-signals are obtained; and calculating cross mapping indexes of the reconstructed sub-signal pairs under different time lag by using an extended CCM method, judging whether to converge according to a convergence threshold, obtaining a causal relationship of each signal pair, further obtaining a fluctuation propagation path, and positioning a fluctuation source. According to the method, the fluctuation propagation path in the industrial control system can be quickly and accurately diagnosed, and the fluctuation source can be positioned.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of industrial control systems, in particular to a method for analyzing fluctuation signals of subsystems based on causal analysis. Background technique [0002] Over-tuning of controllers, external disturbances and nonlinearities may cause fluctuations in industrial process subsystems. Among them, nonlinearity mainly includes the inherent nonlinear properties of the process itself or the nonlinearity caused by the failure of sensors and actuators, which is one of the main reasons for the fluctuation of the industrial process control system subsystem. [0003] In the field of industrial process control, the detection of fluctuations caused by nonlinearity has a lot of research results after decades of development. In recent years, industrial process fault diagnosis based on causal analysis has developed rapidly. [0004] Thornhill used the spectrum PCA method to group the fluctuation subsystems, an...

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Problems solved by technology

Since the method involves EMD decomposition, there are problems such as mode aliasing

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