Discrete Bayesian network water chilling unit fault diagnosis method based on information entropy

A Bayesian network and chiller technology, applied in computer components, instruments, calculations, etc., can solve problems such as information loss, Bayesian classifiers are not easy to handle continuous attributes, and sensor feature information is not fully utilized. Achieve the effect of improving the accuracy rate, overcoming the main limitations, and reducing information loss

Active Publication Date: 2019-06-07
XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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Problems solved by technology

However, there are still limitations in existing research: Bayesian classifiers are not easy to deal with continuous attributes. One way to solve this problem is to discretize continuous attribut

Method used

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  • Discrete Bayesian network water chilling unit fault diagnosis method based on information entropy
  • Discrete Bayesian network water chilling unit fault diagnosis method based on information entropy
  • Discrete Bayesian network water chilling unit fault diagnosis method based on information entropy

Examples

Experimental program
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Example Embodiment

[0094] Example: The historical fault data used in this example comes from the ASHRAE RP-1043 fault experiment. It is a 90-ton (about 316kW) centrifugal chiller. 7 types of typical faults (including 4 Kind of degradation grade), see Table 1 for details. The test data of 64 features are obtained, and the data collection time interval is 10s.

[0095] Table 1 Typical faults and their degradation levels

[0096]

[0097] Step 1: Data collection.

[0098] The historical fault data used in this embodiment comes from the ASHRAE RP-1043 fault experiment. In the RP-1043 fault simulation experiment, a total of 64 characteristic parameters can be collected, of which 48 are directly measured by sensors and 16 are real-time calculations by VisSim software.

[0099] Step 2: Use the existing steady-state filtering method to perform steady-state filtering on the original data.

[0100] Step 3: Feature selection.

[0101] From the foregoing, each sample contains 64 feature parameters. In fact, some fe...

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Abstract

The invention discloses a discrete Bayesian network water chilling unit fault diagnosis method based on information entropy, and the method comprises the steps: obtaining the historical data of a fault through the historical data stored in an experiment or on site, and carrying out the steady-state screening and feature selection; carrying out discretization processing on historical data by utilizing an information entropy-based discretization algorithm, carrying out statistics on frequency to determine conditional probability, and constructing a network model; and verifying the performance ofthe model. According to the method, the main limitation of traditional Bayesian network water chilling unit fault diagnosis based on expert discretization is effectively overcome, and the possibilityof field application of a fault diagnosis system is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of water chillers in air conditioning systems, and in particular relates to a fault diagnosis (FD) method for chillers based on information entropy discrete Bayesian network (EBD-DBN). Background technique [0002] The chiller is the main energy-consuming equipment in the HVAC system. When a failure occurs, the performance of the unit will gradually deteriorate and the life will be reduced. By applying FD technology to the chiller, the fault can be found and eliminated in time, which is beneficial to the HVAC system. Reliable operation and energy saving are of great significance. [0003] The core of the fault diagnosis system is how to quickly locate the first fault point of the fault and carry out preventive maintenance according to the diagnosis results. The Bayesian network has great reasoning advantages for solving failures caused by uncertain factors in complex systems. It is consid...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06K9/62
Inventor 王智伟王亚兰王占伟丁书久
Owner XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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