On-line fault diagnosis method of weighted extreme learning machine sewage treatment on the basis of kernel function

Abstract

The invention discloses an on-line fault diagnosis method of weighted extreme learning machine sewage treatment on the basis of a kernel function. The on-line fault diagnosis method comprises the following steps: 1) rejecting data with an incomplete attribute in sewage data, and then, carrying out data normalization processing to determine a historical data set and an update test set; 2) selecting a kernel function and a weighting scheme, and then, determining model parameters according to an optimal model; 3) according to the selected weighting scheme, endowing a weight for each sample of the historical data set; 4) training the model, and calculating a kernel matrix according to the kernel function; 5) adding a new sample into the model from a new test set for testing, and updating the historical data set; and 6) returning to 3), training the model again, and continuously repeating the above process until the on-line data test is finished so as to realize the identification of the on-line operation state of the sewage treatment process. The method has the advantages of short update time and high classification accuracy rate and has an important meaning for diagnosing operation faults in real time, guaranteeing the safe operation of sewage treatment works and improving the operation efficiency of the sewage treatment works.

Description

technical field [0001] The invention relates to the technical field of sewage treatment, in particular to a kernel function-based weighted extreme learning machine sewage treatment online fault diagnosis method. Background technique [0002] With the rapid development of modern industry and the growth of population, a large amount of domestic sewage and industrial waste water are discharged into water bodies, and the water resources that human beings rely on for survival have been greatly damaged. Water pollution has become one of the main factors restricting human development. In order to prevent further deterioration of the water environment, many countries have taken active measures to build a large number of sewage treatment plants. However, the sewage biochemical treatment process is very complicated, and there are many influencing factors. It is difficult for the sewage treatment plant to maintain long-term stable operation in the actual operation process, and it is ea...

AI Technical Summary

Problems solved by technology

However, the sewage biochemical treatment process is very complicated, and there are many influencing factors. It is difficult for the sewage treatment plant to maintain long-term stable operation in the actual operation process, and it is easy to cause serious problems such as substandard effluent quality, increased operating costs, and secondary environmental pollution. Therefore, it is necessary to Monitor the operation status of the sewage treatment plant, diagnose and deal with the faults in the sewage treatment process in time

[0003] The fault diagnosis of sewage treatment operation status is essentially a pattern classification proble

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