Nonlinear similarity index based fault classification and diagnosing method

Abstract

The invention discloses a nonlinear similarity index based fault classification and diagnosing method, which aims to solve the three key problems facing the fault classification and diagnosing in practical industrial process: 1) limited number of available training samples in reference to fault type; 2) available fault training data in the initial stage of operation state conversion in which the non-linear degree of the data is relatively strong; and 3) overlapping occurrence of sampled data for different faults in the spatial distribution. According to the invention, firstly, the characteristic variables are selected for each referred fault type; and the characteristic variables which can distinguish the fault from normal data are selected. Then, the characteristic variables are just used to match the similarity between the online fault data window and the reference fault data window. The similarity calculation method is based on the non-linear similarity index of window data, and the fault type can be determined online according to the minimum nonlinear similarity value. Compared with the traditional classification and diagnosing method, the method of the invention significantly reduces the dimension of the variables by the selection of the characteristic variables, which not only reduces the limitation of the training data greatly, but also eliminates the negative impact of non-characteristic variables on the calculation of the nonlinear similarity index. In addition, this method implements the fault similarity matching through the non-linear similarity matching of the window data in the spatial distribution, and can avoid the misclassification of overlapping data maximally.

Description

technical field [0001] The invention relates to a fault diagnosis method, in particular to a fault classification diagnosis method based on a nonlinear similarity index. Background technique [0002] The increasingly fierce market competition has put forward higher and higher requirements for the continuous and normal operation of the production process. Timely detection of faults in the production process and correct identification of fault types occupy an important position in the entire production automation system. For modern large-scale and complex industrial production systems, accurate mechanism models are difficult to obtain. Instead, due to the wide application of DCS, the production process can measure and store massive industrial data. These sampling data hide information on process operation status and product quality, which provides a solid foundation for data-driven process monitoring methods. In recent years, data-driven process monitoring methods have receiv...

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Problems solved by technology

In this way, the constraint of insufficient sample size on the establishment of models is greatly reduced.

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