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Heat exchange unit fault diagnosis method and system based on edge calculation and neural network

A technology for heat exchange units and fault diagnosis, applied in neural learning methods, biological neural network models, heating systems, etc., can solve problems such as increased data delay, congestion calculation complexity, enhancement, etc., to improve efficiency and slow down Network bandwidth pressure, the effect of improving accuracy

Pending Publication Date: 2021-11-12
CHANGZHOU ENGIPOWER TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, problems such as network transmission congestion and increased computational complexity caused by the rapid increase in data volume have caused the delay in data transmission to increase continuously. In addition, heat exchange unit equipment and construction are often located in remote locations and harsh environments. Problems with bandwidth and network connectivity

Method used

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  • Heat exchange unit fault diagnosis method and system based on edge calculation and neural network
  • Heat exchange unit fault diagnosis method and system based on edge calculation and neural network

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Experimental program
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Effect test

Embodiment 1

[0054] figure 1 It is a flow chart of the method for diagnosing a heat exchanger unit fault based on edge computing and a neural network involved in the present invention.

[0055] like figure 1 As shown, the present invention provides a method for fault diagnosis of heat exchanger units based on edge computing and neural network, including the following steps:

[0056] Step S1, deploy different types of sensors on the heat exchange unit equipment, collect various data under different working conditions of the equipment in real time to form a sample data set, and transmit it to the edge computing nodes deployed nearby; the collected data include at least vibration data, pressure Data, temperature data and flow data; working conditions include at least normal operation, vibration, leakage and blockage failure of heat exchange unit equipment, heat exchange unit equipment includes circulating pumps, heat exchangers, filters and pipelines;

[0057] Step S2, the edge computing no...

Embodiment 2

[0089] figure 2 It is a functional block diagram of the fault diagnosis system for heat exchange units based on edge computing and neural network involved in the present invention.

[0090] Such as figure 2 As shown, the second aspect of the present invention also proposes a heat exchange unit fault diagnosis system based on edge computing and neural network. The heat exchange unit fault diagnosis system includes:

[0091] The data acquisition module is used to deploy different types of sensors on the heat exchange unit equipment, collect various data under different working conditions of the equipment in real time to form a sample data set, and transmit it to the edge computing nodes deployed nearby; the collected data includes at least vibration Data, pressure data, temperature data and flow data; working conditions at least include normal operation, vibration, leakage and blockage of heat exchange unit equipment, heat exchange unit equipment includes circulating pumps, h...

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Abstract

The invention discloses a heat exchange unit fault diagnosis method based on edge calculation and a neural network, wherein the method comprises the steps that: all data of heat exchange unit equipment under different working conditions are collected in real time to form a sample data set, and the sample data set is transmitted to an edge calculation node which is disposed adjacently; the edge calculation node adopts a sliding window strategy for vibration data and extracts corresponding time domain features, frequency domain features and time-frequency domain features to form a vibration fault diagnosis feature vector; principal component analysis is carried out on the pressure data, the temperature data and the flow data, a plurality of principal component features are selected, the first-m principal component features with the contribution rate reaching a preset value are selected as principal component features, and leakage and blockage fault diagnosis feature vectors are formed; a fault diagnosis model based on a convolutional neural network is constructed according to the fault diagnosis feature vectors; and after feature extraction is carried out on the collected real-time operation data of a heat exchange unit, the fault diagnosis feature vectors are input into the fault diagnosis model for diagnosis, and a diagnosis result is generated.

Description

technical field [0001] The invention belongs to the technical field of intelligent heating fault diagnosis, and in particular relates to a fault diagnosis method and system for a heat exchange unit based on edge computing and a neural network. Background technique [0002] Due to the large amount of heat source, high thermal efficiency, low unit fuel consumption, labor saving and small occupied area, central heating is generally used in urban heating. The so-called central heating is a cyclic process that consists of three parts: heat source-heat exchange station-user, and uses certain pipelines to transport heat to the residents' houses in various ways. The heat exchange station is an important part of the central heating system. The heat exchange station is the place where the system heat supply network and the heat supply users are connected, and is the intermediate link between the heat source and the heat source to the destination. Therefore, the equipment of the heat ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M99/00F24D19/10G01D21/02G06K9/62G06N3/00G06N3/04G06N3/08
CPCG01M99/005G01M99/002G01D21/02G06N3/08G06N3/006F24D19/1009G06N3/045G06F18/211G06F18/2135
Inventor 时伟穆佩红谢金芳
Owner CHANGZHOU ENGIPOWER TECH
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