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Online evaluation method for air feeder state of power plant

A technology for blowers and power plants, applied in engine functions, engine control, machines/engines, etc., can solve the problems of low calculation accuracy, inconsistent analysis results, and high sampling frequency of spectrum analysis methods, so as to reduce the requirements of maintenance personnel and ensure calculation. Accuracy and the effect of ensuring the rapidity of calculation

Inactive Publication Date: 2019-08-27
SHANGHAI POWER EQUIP RES INST
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] (1) There is a certain effect in distinguishing the failure mode of the blower, but the accuracy of the analysis results often depends on the experience of professional technicians and the familiarity with the equipment, and staff with different technical capabilities may obtain inconsistent analysis for the same result result;
[0006] (2) For the evaluation of the faulty parts of the blower, the accuracy of the current technical calculation is low, and the fault mode can often be judged but the faulty part cannot be inferred, so that the staff of the power plant cannot evaluate the current status of the blower and decide when to shut it down for maintenance;
[0007] (3) The sampling frequency of the spectrum analysis method is high, and a large amount of data is generated in the same period of time. It is difficult to complete real-time calculation and online analysis and judgment. It is often used in the analysis after the fault, and cannot meet the requirements of online assessment of the status of the blower.

Method used

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  • Online evaluation method for air feeder state of power plant
  • Online evaluation method for air feeder state of power plant
  • Online evaluation method for air feeder state of power plant

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] For a certain type of blower, during the service period of the blower, use figure 1 the device shown, figure 2 The flowchart shown, image 3 The computer software shown and Figure 4 According to the fault code and hazard table shown in the table, during the operation, it was found that the blower had vibration and noise.

[0047] Step 1: Enter the online state quantity data into the state quantity data engineering server to complete data cleaning;

[0048] Step 2: After cleaning the online state quantity data, normalize it;

[0049] Step 3: Input the normalized online state quantity data into the deep learning fault classification server, and use the classification model of faulty components and historical state quantity data to input the state quantity data of the above steps into the classification model for calculation.

[0050] Step 4: Calculate the code value H of the faulty component of the blower 0 =1.26, according to the calculation formula H=Round(1.26)=...

Embodiment 2

[0054] For a certain type of blower, during the service period of the blower, use figure 1 the device shown, figure 2 The flowchart shown, image 3 The computer software shown and Figure 4 According to the fault code and hazard degree table shown, it is found that the temperature of the blower bearing has risen during operation.

[0055] Step 1: Enter the online state quantity data into the state quantity data engineering server to complete data cleaning;

[0056] Step 2: After cleaning the online state quantity data, normalize it;

[0057] Step 3: Input the normalized online state quantity data into the deep learning fault classification server, and use the classification model of faulty components and historical state quantity data to input the state quantity data of the above steps into the classification model for calculation.

[0058] Step 4: Calculate the code value H of the faulty component of the blower 0 =1.91, according to the calculation formula H=Round(1.91)...

Embodiment 3

[0062] For a certain type of blower, during the service period of the blower, use figure 1 the device shown, figure 2 The flowchart shown, image 3 The computer software shown and Figure 4 According to the fault code and hazard degree table shown, during the operation, it is found that a slight metal friction sound can be heard at the casing of the blower.

[0063] Step 1: Enter the online state quantity data into the state quantity data engineering server to complete data cleaning;

[0064] Step 2: After cleaning the online state quantity data, normalize it;

[0065] Step 3: Input the normalized online state quantity data into the deep learning fault classification server, and use the classification model of faulty components and historical state quantity data to input the state quantity data of the above steps into the classification model for calculation.

[0066] Step 4: Calculate the code value H of the faulty component 0 =3.17, according to the calculation formula...

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Abstract

The invention provides an online evaluation method for an air feeder state of a power plant. Due to different state quantities, the state of an air feeder is represented, and a state measuring point is arranged on the air feeder; and through the state measuring point, detection data of each state quantity are acquired through the state measuring point. According to the online evaluation method forthe air feeder state of the power plant, historical data and online data are combined, the deep learning technology is utilized, according to online state quantity data, a fault part coded value of the air feeder is calculated in real time, and a draught fan fault part and the potential harm are obtained; and a state evaluation value of the air feeder is obtained due to calculation, and accordingto the state evaluation value, maintenance guiding is performed.

Description

technical field [0001] The invention relates to an online evaluation method for the state of a blower fan in a power plant, which belongs to the technical field of blower fans. Background technique [0002] In a power plant, the blower is one of the seven auxiliary machines, and its operating status is directly related to the safety and stability of the power plant. According to the classification of importance to the power plant, power plant equipment can be divided into key equipment, necessary equipment and auxiliary equipment, and fans are listed as key equipment. Therefore, it is extremely important to evaluate the condition of the blower. [0003] The faulty parts of the blower mainly include rotating parts, bearings, supporting parts and motor parts, etc., which manifest as different failure modes such as unbalanced, misaligned, loose and bearing failures of the blower. When the blower has the above failure modes, how to judge the faulty parts of the blower and then...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): F04D27/00
CPCF04D27/001F04D27/008F05D2260/80F05D2270/709
Inventor 郭荣范佳卿邓志成汪勇张强孙猛臧剑南陈荣泽
Owner SHANGHAI POWER EQUIP RES INST
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