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Application of dynamic Bayesian network to intelligent diagnosis of mechanical equipment failure

A Bayesian network and equipment failure technology, applied in special data processing applications, design optimization/simulation, instruments, etc., can solve problems such as not much room for improvement, lack of fault data samples, large data volume, etc.

Inactive Publication Date: 2017-02-01
BEIJING UNIV OF CHEM TECH
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Problems solved by technology

Although these methods have achieved some results, there are still many deficiencies. For example, these methods do not start from the fault mechanism well, so that the accuracy of early warning is low; sometimes a large amount of data is required, and many fault data samples are lacking, etc. , so there has not been much room for improvement in the field of early warning

Method used

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  • Application of dynamic Bayesian network to intelligent diagnosis of mechanical equipment failure
  • Application of dynamic Bayesian network to intelligent diagnosis of mechanical equipment failure
  • Application of dynamic Bayesian network to intelligent diagnosis of mechanical equipment failure

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Embodiment Construction

[0042] Taking a large-scale reciprocating compressor in a domestic petrochemical company as an example, the following will combine the flow chart ( figure 1 ) to further describe the concrete flow process of the present invention.

[0043] 1) Determining the fault characteristics of unit equipment, and establishing an overall Bayesian network framework related to equipment: in the present invention, a three-layer Bayesian network intelligent fault diagnosis framework structure is established.

[0044]2) Understand the equipment faults and fault characteristics of the unit, determine and establish each network layer of the Bayesian network for fault diagnosis: determine the three network layers of the Bayesian network for intelligent diagnosis according to the equipment fault and its corresponding fault feature table . In the three-layer Bayesian network structure, the first layer is the unit information layer, which contains node information including the unit’s previous main...

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Abstract

Application of a dynamic Bayesian network to intelligent diagnosis of a mechanical equipment failure belongs to the field of failure diagnosis. According to the application, the dynamic three-layer Bayesian network is applied to intelligent diagnosis of the mechanical equipment failure for the first time; and a possible failure and the failure probability of related mechanical equipment can be calculated and deduced by acquiring operating state information of the mechanical equipment and setting and controlling related relation among Bayesian network nodes and the probability. The method has the advantages of real-time property, continuity, high accurate rate and the like, and is suitable for failure diagnosis of various kinds of mechanical equipment.

Description

technical field [0001] The invention belongs to the field of intelligent fault diagnosis of equipment, relates to fault diagnosis for mechanical equipment, and in particular relates to a method for applying a dynamic Bayesian network in intelligent fault diagnosis. Background technique [0002] In national industrial production and national life, all kinds of large-scale machinery play a pivotal role, especially in petroleum, chemical, gas pipeline industries and other industries that are related to the lifeline of the national economy. Due to the complex structure and many vibration excitation sources, once a fault occurs, it will affect the production at least, and the machine will be destroyed at the worst. Therefore, research on the methods of early warning of these large-scale equipment faults, so as to detect abnormalities in time, has become a current research hotspot. [0003] At present, there are relatively few domestic researches on the intelligent diagnosis of la...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 马波刘嘉濛江志农赵祎
Owner BEIJING UNIV OF CHEM TECH
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