FastRVM (fast relevance vector machine) wastewater treatment fault diagnosis method

Abstract

The invention discloses a FastRVM(fast relevance vector machine) wastewater treatment fault diagnostic method. The method includes the following steps that: 1) samples with incomplete properties in samples to be recognized in wastewater data are removed, since the dimensions of the properties of the samples are different, the samples are normalized to an interval of [0, 1]; 2) based on a clustering fast relevance vector machine, the majority of types of data are compressed; 3) the synthetic minority over samplingtechnique is adopted to expand the minority of types of data; 4) a "one-to-one" fast relevance vector machine multi-classification model is established; and 5) fast relevance vector machine wastewater fault diagnosis modeling is carried out. According to the FastRVM wastewater treatment fault diagnosis method of the invention, the majority of types of data are compressed based on the clustering fast relevance vector machine, and the minority of types of data are expanded through the synthetic minority over sampling technique, and therefore, the imbalance of wastewater data can be decreased; and the fast RVM is adopted to establish a multi-classification model for a wastewater biochemical treatment process, and therefore, the accuracy of fault diagnosis on a wastewater biological wastewater treatment system can be effectively improved.

Description

technical field [0001] The invention relates to the field of sewage treatment, in particular to a FastRVM sewage treatment fault diagnosis method. Background technique [0002] With the rapid development of my country's industrial and agricultural economy, the process of urbanization is accelerating, and the discharge of industrial and agricultural wastewater and domestic sewage is increasing day by day, which not only increases the load of municipal sewage treatment, but also greatly damages the water resources that human beings rely on for survival. Various degrees of eutrophication have appeared in major lakes, and the safety of urban water environment has become the focus of public attention. As a key protective barrier for natural water bodies, sewage treatment plants will directly affect the safety of the water environment if their operation is good or bad. The sewage biochemical treatment process is complex, and there are many influencing factors. It is difficult for ...

AI Technical Summary

Problems solved by technology

However, these technologies also have certain limitations. Expert systems have knowledge acquisition bottlenecks. If the established expert knowledge base is incomplete, it may lead to reasoning confusion; The calculation of sensitive parameters is too large and other limitations; rough set theory is often powerless in dealing with abnormal or noisy data, and requires a large number of data samples when building a model; neural networks are prone to fall into local optimum, and have overfitting and convergence The disadvantage of slow speed; and how to deal with the balance between sewage data types without affecting the performance of fault diagnosis is rarely mentioned in the current sewage fault diagnosis research

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