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equipment fault prediction and health assessment method based on a fuzzy Bayesian network

A Bayesian network and equipment failure technology, applied in the direction of specific mathematical models, predictions, calculation models, etc., can solve the problems of not considering the influence of the system, low accuracy of failure prediction, and failure to discover the interaction of single-machine equipment failures, etc.

Active Publication Date: 2019-06-11
BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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Problems solved by technology

[0005] Due to the lack of information obtained from equipment failure information mining, the above fault prediction method cannot discover the interaction between the faults of each single machine equipment in the continuous production process, and does not consider the impact of the associated faults on the system, so it cannot find the main fault. The accuracy of failure prediction is low

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  • equipment fault prediction and health assessment method based on a fuzzy Bayesian network
  • equipment fault prediction and health assessment method based on a fuzzy Bayesian network
  • equipment fault prediction and health assessment method based on a fuzzy Bayesian network

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Embodiment Construction

[0068] Below in conjunction with accompanying drawing, further describe the present invention through embodiment, but do not limit the scope of the present invention in any way.

[0069] The following embodiment adopts the assembly line X of a household appliance enterprise from September 2017 to July 2018 a Data (there are 9 stand-alone devices on this assembly line), describe in detail the implementation process of the prediction method provided by the present invention.

[0070] Method flow chart as figure 1 shown. The method of the present invention includes: 1) using the failover matrix to select three types of faults that have a greater impact on the equipment as a Bayesian network training sample set; 2) dividing the stand-alone equipment into on-line production groups, and calculating the Failure rate and failure loss degree; 3) Determining the health status level of connection production group and assembly line based on fuzzy algorithm; 4) Training Bayesian network ...

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Abstract

The invention discloses an equipment fault prediction and health assessment method based on a fuzzy Bayesian network. The method comprises the following steps: extracting main faults of equipment, quantifying the influence of interaction between single-machine equipment faults in a continuous production process on a system as a fault loss degree, constructing a fuzzy Bayesian network, and realizing equipment fault prediction and health assessment. According to the method, the fault information of the equipment can be fully utilized, representative faults are found, the equipment fault prediction result is more accurate, health assessment can better conform to the actual situation through the fault loss degree, design is reasonable, operation is easy and convenient, and wide application value is achieved.

Description

technical field [0001] The invention belongs to the technical field of intelligent factory equipment construction, and relates to equipment failure prediction and health assessment methods, in particular to a fuzzy Bayesian network-based equipment failure prediction and health assessment method. Background technique [0002] Intelligent factory equipment failure analysis and prediction is the requirement of lean production, and it is also one of the key tasks in building a smart factory. With the increasing integration and complexity of smart factories, the probability of system failure and function failure is gradually increasing, and once a failure occurs, it will cause great harm, and seriously cause the entire system to fail and be paralyzed. Product damage, system breakdown, and catastrophic accidents can be avoided to a large extent if faults are detected in an early stage, that is, before they cause any damage to the system, and a reliable maintenance strategy is impl...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06N7/00
Inventor 于重重宁亚倩姜珍苏维均
Owner BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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