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Medium-speed coal mill fault early warning method and system based on least square support vector machine algorithm

A technology of support vector machine and least squares, applied in the field of equipment failure early warning, can solve problems such as failure

Pending Publication Date: 2021-10-01
CHINA DATANG CORP SCI & TECH RES INST CO LTD EAST CHINA BRANCH +1
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Problems solved by technology

This method only predicts the current of the coal mill, and judges the vibration fault of the coal mill according to the prediction result, and cannot use other parameters to predict other faults

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  • Medium-speed coal mill fault early warning method and system based on least square support vector machine algorithm
  • Medium-speed coal mill fault early warning method and system based on least square support vector machine algorithm
  • Medium-speed coal mill fault early warning method and system based on least square support vector machine algorithm

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Embodiment Construction

[0073] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are part of the present invention Examples, not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0074] like figure 1 As shown, the medium-speed coal mill fault warning method based on the least squares support vector machine algorithm includes the following steps:

[0075] Step 1. Collect the historical data of the coal mill, standardize the data, and select the parameters with high correlation with the target parameters according to the Pearson correlation system; specifi...

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Abstract

The invention provides a medium-speed coal mill fault early warning system based on a least square support vector machine algorithm, and the system comprises the steps: (1) collecting historical data of a coal mill, preprocessing of the data, and selecting a parameter which is greatly related to a target regression parameter according to a Pearson's correlation coefficient; (2) inputting selected historical parameters into an LS-SVM algorithm for training to obtain an optimal regression result model; and (3) inputting actual parameters into the trained algorithm to obtain an actual value regression result, calculating an adaptive threshold interval, judging whether the actual value is in the adaptive threshold interval or not, considering that the parameters are abnormal if the actual value exceeds the adaptive threshold interval for continuous 15 seconds, and performing early warning on an abnormal state. The method can predict different faults according to different parameters.

Description

technical field [0001] The invention relates to the technical field of equipment failure early warning, in particular to a medium-speed coal mill failure early warning method and system based on the least squares support vector machine algorithm. Background technique [0002] Coal mills in thermal power plants are an important factor affecting the safe operation of boilers as auxiliary equipment. With the development of information technology, DCS systems in power plants generate a large number of equipment operating parameters. How to efficiently process and analyze these data resources is to further improve the management level of power plants and guarantee important means of safe operation. [0003] The abnormality or failure of the equipment is manifested by the change of operating parameters during the operation of the equipment. Taking the main abnormality or failure during the operation of the equipment as a clue to obtain the characteristic parameters reflecting the ...

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Application Information

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IPC IPC(8): G06F30/27G06N20/10G06F111/10G06F119/02
CPCG06F30/27G06N20/10G06F2111/10G06F2119/02
Inventor 王远鑫许文良陈俊徐民吴万范程时鹤
Owner CHINA DATANG CORP SCI & TECH RES INST CO LTD EAST CHINA BRANCH
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