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

An extreme learning machine and fault diagnosis technology, applied in computing, computer parts, instruments, etc., can solve problems such as uneven distribution of sewage data sets, substandard effluent quality, and difficulty in sewage treatment plants, and solve the problem of data distribution between classes Unbalanced problem, imbalanced distribution between good data classes, and the effect of speeding up classification learning

Inactive Publication Date: 2017-06-20
SOUTH CHINA UNIV OF TECH
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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

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

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[0044] Example

[0045] See attached figure 1 with figure 2 , Attached figure 1 And figure 2 It is a flowchart of the sewage treatment fault diagnosis method based on the weighted extreme learning machine integration algorithm in this embodiment. The experimental simulation data comes from the University of California database (UCI), which is the daily monitoring data of a sewage treatment plant. The dimension of each sample of the entire data set is 38. There are 380 complete records of all attribute values. The monitored water bodies are in total. There are 13 states, each state is replaced by a number. In order to simplify the complexity of classification, we divide the samples into 4 categories according to the nature of the sample categories, as shown in Table 1. In Table 1, category 1 is a normal condition, category 2 is a normal condition with performance exceeding the average value, category 3 is a normal condition with low water inflow, and category 4 is a secondary...

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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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Application Information

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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/04G06F18/2148G06F18/25G06F18/24
Inventor 许玉格孙称立陈立定
Owner SOUTH CHINA UNIV OF TECH
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