Stacking sewage treatment fault diagnosis method based on weighted integration of plurality of meta-classifiers

Abstract

The invention discloses a stacking sewage treatment fault diagnosis method based on weighted integration of a plurality of meta-classifiers. A two-layer overlapping type frame structure is constructed, three classification algorithms with good classification effects on unbalanced data are selected, namely, SVM, RVM and WELM are used as base classifiers, a prediction result of the base classifiersfor the original training set is obtained as input of a second-layer meta-classifier, similarly SVM, RVM and WELM are selected as meta-classifiers, weighted integration is performed on the meta-classifiers to obtain a final diagnosis model, and a classification result is output, namely a fault diagnosis result corresponding to the to-be-tested data, through the final diagnosis model. Experiments prove that the diversity and stability of the algorithm and the classification accuracy of sewage treatment faults are improved by performing weighted integration on a plurality of meta-classifiers, sothat the overall performance of fault diagnosis in the sewage treatment process is effectively improved.

Description

technical field [0001] The invention relates to the technical field of sewage treatment fault diagnosis, in particular to a stacking sewage treatment fault diagnosis method based on weighted integration of multiple element classifiers. Background technique [0002] Wastewater treatment is a complex, multivariable biochemical process. The failure of sewage treatment plants can easily lead to a series of serious sewage pollution problems. The fault diagnosis of sewage treatment process can be transformed into a classification problem of pattern recognition. The sewage data is composed of the data collected regularly by the sewage plant and its current working status, and the sewage data within a period of time constitutes the sewage data set. Due to the low frequency of faults in qualified sewage treatment plants, the data in the fault state in the collected sewage data set is often far less than the data in the normal state. This results in a highly unbalanced distribution...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the defect that the sewage fault identification effect is not good in the existing learning algorithm, and proposes a stacking sewage treatment fault diagnosis method based on the weighted integration of multiple meta-classifiers, by constructing a two-layer superposition Framework structure, choose 3 classification algorithms that have good classification effect on unbalanced data, namely SVM, RVM, WELM as the base classifier, and get the prediction result of the base classifier on the original training set as the input of the second layer meta-classifier, Also select SVM, RVM, and WELM as the meta-classifier, and perform weighted integration on the meta-classifier to obtain the final diagnostic model

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