Evaluation value calibration method of equipment intelligent early warning system

An early warning system and calibration method technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the impact assessment value, reduce the accuracy and effectiveness of the equipment early warning system, and cannot fundamentally change the redundancy of measuring points. It can improve the accuracy, ensure the real-time early warning capability, and achieve the effect of good versatility.

Active Publication Date: 2016-02-03
SHANDONG LUNENG SOFTWARE TECH
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Problems solved by technology

For example, in the paper "SVM Short-Term Load Forecasting Based on ARMA Error Correction and Adaptive Particle Swarm Optimization" (Power System Protection and Control, 2011, No. 14), the optimization limitation of the support vector machine regression model is that the ant colony algorithm and the particle swarm optimization algorithm can only For Kernel Bandwidth , error penalty factor Global optimization of two parameters; and the national patent "A Method and System for Prediction and Early Warning of Grain Situation Based on SVM" (application number 201410068731.X) mentions the use of preprocessed historical health multi-parameter sample data to establish support vector machines The regression prediction model realizes the real-tim...

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  • Evaluation value calibration method of equipment intelligent early warning system
  • Evaluation value calibration method of equipment intelligent early warning system
  • Evaluation value calibration method of equipment intelligent early warning system

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Embodiment 1

[0052] A kind of evaluation value calibration method of the equipment intelligent early warning system based on SVM, as attached figure 1 Shown, whole design of the present invention comprises following several processes:

[0053] Process 1 establishes an intelligent early warning model for equipment. This process mainly includes four main steps.

[0054] Step 1.1 Training data import

[0055] The selected training data is the historical health data of multiple measuring points of a single device. The general operation process is: read the historical operating status data of the relevant equipment for a sufficient time from the power plant database based on the selected measuring point; then use The set screening rules screen out the healthy equipment data in good operating condition from all the historical data as the training data for constructing the support vector machine regression model.

[0056] The filtering rules of the training data are to obtain healthy operation ...

Embodiment 2

[0163] In order to further illustrate the implementation process of the present invention, the present invention selects the important measuring point data of the A primary fan equipment in the No. 1 unit boiler auxiliary equipment of a certain thermal power plant to verify the beneficial assistance of the present invention to the early warning of equipment status.

[0164] The main steps of the present invention's evaluation value calibration method based on support vector machine to A primary fan are as follows:

[0165] 1. Using the historical health data of A primary fan to construct a multi-input and multi-output intelligent early warning model process

[0166] First, select the important parameter measurement points that will participate in the construction of the SVM regression model, and extract enough time data from the power plant database, and obtain training data according to the screening rules.

[0167] In this example, the measuring point of primary fan A is sel...

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Abstract

The invention belongs to the technical field of equipment state early warning, and particularly relates to an evaluation value calibration method of an equipment intelligent early warning system based on an SVM regression model. The evaluation value calibration method of the equipment intelligent early warning system can remove deviation of an abnormal detecting point with respect to the evaluation values of other normal measuring points in real time sequentially through establishment of an equipment intelligent early warning model, influence relation curve fitting, identification of abnormal measuring points and calibration of interfered evaluation values, has good universality and can be grafted to other regression algorithms for performance optimization. When the evaluation value of an interfered measuring point of equipment is adjusted on line, no excessive delay effect is generated, and a real-time early warning capability of the model for the equipment is ensured; discrimination and analysis for abnormal measuring points can be carried out in real time, so that the real-time performance of judgment is ensured, and online state analysis for equipment data can be carried out; and evaluation values without interference of the normal measuring points can be output steadily. According to the evaluation value calibration method of the equipment intelligent early warning system, the reliability of the early warning model is higher, and the life cycle is long.

Description

(1) Technical field [0001] The invention belongs to the technical field of equipment state early warning, and in particular relates to an evaluation value calibration method of an equipment intelligent early warning system based on an SVM regression model. (2) Background technology [0002] As we all know, the operating status of important equipment has a great impact on factory production, such as key equipment such as boilers and generators in power plant units. The functional characteristics, appearance characteristics and electrical characteristics reflected in the operation process of the equipment under normal working conditions are different from those under abnormal conditions. Intelligent early warning of equipment means that when there are signs of abnormality in equipment components, an early warning signal is sent to equipment management personnel in advance to prevent abnormalities from becoming uncontrollable faults, ensure the safety of production personnel, a...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 邢宏伟安佰京徐扬张华伟赵俊
Owner SHANDONG LUNENG SOFTWARE TECH
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