Sewage treatment fault diagnosis method based on weighted extreme learning machine integrated algorithm

Abstract

The invention discloses a sewage treatment fault diagnosis method based on a weighted extreme learning machine integrated algorithm, and the method comprises the steps: employing an integrated algorithm Adaboost as the overall algorithm frame of classification learning; initializing the method through employing an improved sample weight value; employing a weighted extreme learning machine as a base classifier, carrying out the iterative updating of the characteristics of the sample weight value through an integrated algorithm, processing the imbalance data, and combining with the nonlinear mapping of a kernel function to improve the linearly separable degree of data. On the basis of the integrated algorithm, the method employs a weighted extreme learning machine as the base classifier, can achieve the classification of imbalance data of a plurality of classes, improves the classification performance of imbalance data, and effectively improves the fault diagnosis accuracy in a sewage treatment process.

Description

technical field [0001] The invention relates to the technical field of sewage treatment fault diagnosis, in particular to a sewage treatment fault diagnosis method based on a weighted extreme learning machine integration algorithm. Background technique [0002] Sewage treatment is a complex biochemical process with many influencing factors. It is difficult for sewage treatment plants to maintain long-term stable operation, and failures may easily cause serious problems such as substandard effluent quality, increased operating costs, and secondary pollution of the environment. Therefore, it is necessary to treat sewage The operating status of the treatment plant is monitored, and operating faults are diagnosed and dealt with in a timely manner. [0003] The fault diagnosis of sewage treatment process is actually a problem of pattern recognition, and the problem of unbalanced distribution of sewage data sets is often encountered in the classification process. The traditional ...

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Problems solved by technology

[0002] Sewage treatment is a complex biochemical process with many influencing factors. It is difficult for sewage treatment plants to maintain long-term stable operation, and failures may easily cause serious problems such as substandard effluent quality, increased operating costs, and secondary pollution of the environment. Therefore, it is necessary to treat sewage Monitor the operating status of the treatment plant, diagnose operating faults and deal with them in a timely manner

[0003] The fault diagnosis of sewage treatment process is actually a problem of pattern recognition, and the problem of unbalanced distribution of sewage data sets is often encountered in the classification process

Traditional machine learning methods tend to make the classification accuracy biased toward the majority class, but in actual classification, what is more important is the classification accuracy rate of the minority class, that is, the classification accuracy rate of the fault class.

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