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Research and Optimization of Gas Regulator Fault Diagnosis Based on Multiple Combination Algorithm

Aiming at the problem of low accuracy of single model in fault diagnosis of intelligent gas regulator, a fault diagnosis method based on multi-combination algorithm is proposed in this paper. In the existing fault diagnosis on the basis of the theory and method, using principal component analysis (PCA) and linear discriminant method (LDA) two methods of data processing, with radial basis function neural network (RBF) and support vector machine (SVM) is the basis of the two kinds of fault diagnosis algorithm, taking the sub-high pressure gas regulator as the research object, and manufacturer cooperation has carried on the concrete experiment, optimize and improve the recognition rate of single fault diagnosis model. At the same time, aiming at the problem that the single fault diagnosis model has different adaptability under different flow rates in actual operating conditions, two new intelligent combination diagnosis schemes are proposed, which saves the manpower and material resources of gas companies, improves the safety and accuracy, and lays a foundation for subsequent applications.