Fault diagnosis method of air valve for reciprocating compressor based on statistical learning theory

Abstract

The invention belongs to the technical field of compressors, and particularly relates to a fault diagnosis method of an air valve for a reciprocating compressor based on a statistical learning theory. The method comprises the following steps: determining the degree of each data point belonging to a certain cluster; optimizing the objective function through iteration, obtaining the degree of membership of each clustering center and each data point to each class, and classifying samples; realizing the data classification by searching an optimal classification hyperplane; obtaining a dual problem and a classification decision function; constructing a kernel function classifier by utilizing a radial basis kernel function, and solving a dual problem by adopting a sequence minimum optimization algorithm; solving a clustering center of each subset; obtaining M-1 support vector machines SVM1,..., SVMM-1, and forming a root node and an intermediate node of the binary tree; training the support vector machines one by one, optimizing a radial basis kernel function parameter gamma and a penalty parameter C, and realizing fault diagnosis of the air valve for the reciprocating compressor. Therefore, accurate diagnosis of the fault of the air valve can be efficiently realized.

Description

technical field [0001] The invention belongs to the technical field of compressors, and in particular relates to a fault diagnosis method for an air valve used in a reciprocating compressor based on statistical learning theory. Background technique [0002] Reciprocating compressors are among the most commonly used machinery in chemical production processes in the oil and gas industry. With the continuous improvement of high performance and high safety requirements, ensuring its operation safety has become an important research topic. Vibration-based measurement and analysis techniques have been proven to be very effective in machinery health monitoring and fault diagnosis; because vibration signals can reflect a large amount of operating status information when equipment is in service, using some analysis methods can successfully detect Faults in rotating machinery such as gearboxes and bearings. However, due to factors such as gaps, nonlinear stiffness of bearings, unbal...

AI Technical Summary

Problems solved by technology

According to statistics, about 60% of the faults of reciprocating compressors are caused by air valves. Therefore, it is of practical significance to ca

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