Supercharge Your Innovation With Domain-Expert AI Agents!

Health degree prediction method, system and computer-readable storage medium of HVAC system

A prediction method and health degree technology, applied in the field of big data analysis, can solve the problem of inability to achieve accurate and effective health degree prediction, inability to intuitively and effectively obtain HVAC system health degree change status and trend analysis results, and inability to accurately and intuitively predict. HVAC system failure and other problems, to achieve the effect of accurate and intuitive prediction of failure, accurate and effective health prediction

Active Publication Date: 2020-06-12
云科(山东)电子科技有限公司
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Existing methods cannot achieve accurate and effective health prediction, cannot accurately and intuitively predict HVAC system failures, and cannot intuitively and effectively obtain HVAC system health changes and trend analysis results

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Health degree prediction method, system and computer-readable storage medium of HVAC system
  • Health degree prediction method, system and computer-readable storage medium of HVAC system
  • Health degree prediction method, system and computer-readable storage medium of HVAC system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] see figure 1 As shown, a method for predicting the health of an HVAC system provided by Embodiment 1 of the present invention mainly includes:

[0045]Step 101, obtaining time series historical data of working state parameters in the HVAC system, where the working state parameters include at least one type of working state parameters.

[0046] Specifically, the working state parameters may include at least one of the following parameter types: accumulative running time of the unit, accumulative running time of the compressor, operating percentage of the chiller, chilled water outlet temperature, chilled water inlet temperature, cooling water outlet temperature, cooling Water inlet and outlet temperature, condensing pressure, evaporating pressure, evaporator saturation temperature, condenser saturation temperature, small temperature difference of condenser, small temperature difference of evaporator, oil pressure, oil temperature, compressor start and stop times, compres...

Embodiment 2

[0080] Such as figure 2 As shown, the method for predicting the health of an HVAC system provided by Embodiment 2 of the present invention, after step 103 in Embodiment 1 above, further includes:

[0081] Step 104, determine potential fault source information according to health degree information analysis. Specifically:

[0082] Fault source information corresponding to each order of fault symptoms in the transition probability matrix of preset multi-order fault symptoms;

[0083] According to the transition probability matrix of the multi-order fault symptom corresponding to the health degree, the probability information of the target fault caused by the corresponding fault source is calculated.

[0084]Analyze the health curve of the HVAC system. If the changing trend of the response in the health curve and the value of the health degree meet the preset failure warning conditions, it is determined that there is a potential failure risk. In practical applications, the fa...

Embodiment 3

[0086] Corresponding to the health degree prediction method of the HVAC system in the embodiment of the present invention, the embodiment of the present invention also provides a health degree prediction system of the HVAC system, such as image 3 As shown, the system mainly includes:

[0087] The historical data obtaining unit 10 is used to obtain the time series historical data of each working state parameter in the HVAC system, and the working state parameter includes at least one parameter type;

[0088] The symptom occurrence probability obtaining unit 20 is used to calculate the transition probability of the multi-order fault symptom corresponding to each working state parameter according to the obtained time series historical data of the working state parameters in the HVAC system;

[0089] The health degree information obtaining unit 30 is configured to calculate and obtain the health degree information of the HVAC system according to the transition probabilities of th...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a health degree prediction method and system for a heating and ventilation system and a computer readable storage medium, and the method comprises the steps: obtaining time series historical data of working state parameters in the heating and ventilation system, the working state parameters comprising at least one type of working state parameters; according to the obtainedtime sequence historical data of the working state parameters in the heating and ventilation system, calculating the transition probability of multi-order fault symptoms corresponding to the working state parameters; and according to the transition probability of the multi-order fault symptom corresponding to each working state parameter, calculating and obtaining the health degree information ofthe heating and ventilation system. By implementing the method and the system, the health degree of the heating and ventilation system can be predicted accurately and effectively.

Description

technical field [0001] The present invention relates to the technical field of big data analysis, and in particular to a health degree prediction method, system and computer-readable storage medium of a heating and ventilation system. Background technique [0002] The health detection of the HVAC system is an important task in daily maintenance. In the prior art, a method of regularly detecting a specific parameter is usually adopted, and the failure of the HVAC system is directly judged and predicted based on the result of the measured parameter. Existing methods cannot achieve accurate and effective health prediction, cannot accurately and intuitively predict HVAC system failures, and cannot intuitively and effectively obtain HVAC system health changes and trend analysis results. Contents of the invention [0003] In view of this, the present invention provides a method, system, and computer-readable storage medium for predicting the health of an HVAC system, so as to a...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q10/06
CPCG06Q10/04G06Q10/0639
Inventor 籍宏飞徐鹏李彬姜丛斌侯博伟
Owner 云科(山东)电子科技有限公司
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com