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A Method of Equipment Fault Prediction and Health Assessment Based on Fuzzy Bayesian Network

A Bayesian network and equipment failure technology, applied in the direction based on specific mathematical models, predictions, calculation models, etc., can solve problems such as failure to consider the impact of the system, low failure prediction accuracy, failure to find major failures, etc., to prevent damage. The effect of preventing sexual accidents, improving maintenance and support capabilities, and timely and efficient troubleshooting

Active Publication Date: 2021-06-04
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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  • A Method of Equipment Fault Prediction and Health Assessment Based on Fuzzy Bayesian Network
  • A Method of Equipment Fault Prediction and Health Assessment Based on Fuzzy Bayesian Network
  • A Method of Equipment Fault Prediction and Health Assessment Based on 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 a method for equipment failure prediction and health assessment based on fuzzy Bayesian network, which includes: extracting the main failures of the equipment, and quantifying the influence of the interaction between the failures of each single machine equipment in the continuous production process on the system as a failure Loss degree, construct fuzzy Bayesian network, realize equipment failure prediction and health assessment. The method of the invention can make full use of the fault information of the equipment, find representative faults, make the prediction result of the equipment fault more accurate, and can make the health assessment more in line with the actual situation through the fault loss degree, the design is reasonable, the operation is simple, and it has wide application value .

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...

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Application Information

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Patent Type & Authority Patents(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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