Household air conditioner fault diagnosis method based on Bayesian network

A Bayesian network and fault diagnosis technology, applied in household heating, household appliances, applications, etc., can solve problems such as inability to accurately describe operating characteristics, achieve efficient fault diagnosis, and reduce costs

Active Publication Date: 2018-03-16
ZHEJIANG UNIV
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Problems solved by technology

Household air conditioners often do not record operating data, and physical modeling cannot be used to accurately describe their operating characteristics. It is necessary to propose a new type of fault detection and diagnosis algorithm at the theoretical level

Method used

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  • Household air conditioner fault diagnosis method based on Bayesian network
  • Household air conditioner fault diagnosis method based on Bayesian network
  • Household air conditioner fault diagnosis method based on Bayesian network

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Embodiment Construction

[0022] The present invention will be further explained and described below in conjunction with the drawings and embodiments.

[0023] The fault diagnosis method of household air conditioner based on Bayesian network includes the following steps:

[0024] (1) Through in-depth analysis of the mechanism of home air-conditioning system failures, symptoms, etc., with the help of expert knowledge, field maintenance personnel experience, historical research results, etc., a list of typical failures and symptoms of home air conditioners are summarized. Fully obtain the operation and maintenance information of the household air conditioner to be diagnosed, including the current operating status of the air conditioner, air conditioner maintenance records and operation records, etc., to form an additional information list for auxiliary diagnosis.

[0025] (2) Organize the additional information, fault information, and symptom information, and determine the causal relationship between the additi...

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Abstract

The invention provides a household air conditioner fault detection and diagnosis method based on the Bayesian network. The structure of the Bayesian network qualitatively describes the complicated causal relationship between most typical faults of household air conditioners and dependence occurrence conditions and fault symptoms thereof. The condition probability table represented by a directed edge of the Bayesian network qualitatively describes the probability value in the causal relationship. By means of the method, qualitative diagnosis information and quantitative data can be integrated,the knowledge and experience of industry experts and the additive information of diagnosis objects are sufficiently utilized, the fault diagnosis efficiency and accuracy are improved, and accurate fault diagnosis is achieved under the condition that diagnosis information is incomplete and inaccurate. The Bayesian network provided by the invention can effectively detect and diagnose most faults ofthe household air conditioners.

Description

Technical field [0001] The invention belongs to the field of air-conditioning system fault detection and diagnosis and artificial intelligence, and relates to knowledge reasoning under uncertain and incomplete information, and in particular to a method and technology for fault diagnosis of household air conditioners based on Bayesian networks. Background technique [0002] With the general improvement of people's living standards, the use of household air conditioners has increased year by year, and has become the most widely used form of air conditioners. In the actual operation of the household air conditioner, various failures may occur, which affect the indoor thermal comfort and cause energy waste. The traditional air conditioner fault diagnosis solution relies on the expert experience of engineers, requires high professional quality, and is time-consuming and labor-intensive. The development of intelligent diagnostic tools is of great significance for improving the efficie...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): F24F11/38
Inventor 赵阳李婷婷张学军
Owner ZHEJIANG UNIV
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