Bayesian Network Fault Prediction Method for Modular Complex Equipment

A Bayesian network and fault prediction technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as poor practicability, achieve strong practicability, reduce maintenance costs, and improve maintenance efficiency

Active Publication Date: 2017-01-18
SHANGHAI GUAN AN INFORMATION TECH
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Problems solved by technology

[0005] In order to overcome the shortcomings of poor practicability of existing equipment failure prediction methods, the present invention provides a Bayesian network failure prediction method for modular complex equipment

Method used

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  • Bayesian Network Fault Prediction Method for Modular Complex Equipment
  • Bayesian Network Fault Prediction Method for Modular Complex Equipment
  • Bayesian Network Fault Prediction Method for Modular Complex Equipment

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Embodiment Construction

[0047] refer to Figure 1-7 . The specific steps of the Bayesian network fault prediction method for modular complex equipment of the present invention are as follows:

[0048] Step 1. According to the physical boundary between components and the actual fault prediction object, the complex equipment system is decomposed into multiple independent and interrelated sub-modules. The specific method is as follows:

[0049] In this embodiment, taking "head up display (HUD)" as the failure mode, according to the original design data of HUD and its system structure composition, first decompose the HUD into functional modules of different levels according to the function, and the decomposed system modules such as figure 2 shown. Table 1 lists the failure modes and descriptions corresponding to the modules at each level of the HUD.

[0050] Table 1 Failure modes of each module of HUD

[0051] mode number level Module number module failure mode coding 1 0 ...

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Abstract

The invention discloses a modularized complicated equipment Bayesian network failure prediction method, which is used for solving the technical problem of poor practicability of the existing equipment failure prediction method. The modularized complicated equipment Bayesian network failure prediction method has the technical scheme that firstly, complicated equipment is decomposed and modularized according to actual requirements and physical boundaries among components; then, a FPBN (Failure Prediction Bayesian Network) model of each module is built for each module of the complicated equipment by different modeling methods, and the model of each module is further modified so that each module is modularized for forming a FPBNM (Failure Prediction Bayesian Network Module); then, each PBNM is integrated for building an FPBN integrated model of the whole complicated equipment system; and finally, the actual operation state of the equipment is predicted on the basis of the built fault prediction integrated model through using detection information as a drive by using a probability theory formula. The modularized complicated equipment Bayesian network failure prediction method has the advantages that the method is used for the maintenance and the guarantee of the complicated equipment on the basis of the modularization and the Bayesian network; the time state of the complicated equipment can be fast and accurately predicted; the maintenance efficiency is effectively improved; the maintenance cost is reduced; and the practicability is high.

Description

technical field [0001] The invention relates to an equipment failure prediction method, in particular to a modular complex equipment Bayesian network failure prediction method. Background technique [0002] Document "Cai Zhiqiang, Sun Shudong, Si Shubin, et al. FMECA-based Bayesian Network Modeling for Fault Prediction of Complex Equipment [J]. Systems Engineering Theory and Practice, 2013, 33(1): 187-193." A Bayesian network model modeling method for failure prediction based on failure mode, effects and criticality analysis (FMECA) knowledge. Based on the analysis of the fault information contained in the existing FMECA knowledge, this method proposes a fault prediction Bayesian network (FPBN) network structure transformation method and a calculation method of FPBN probability parameters based on FMECA units, and establishes a corresponding FPBN unit model. Then, the FPBN unit models corresponding to each component of the complex equipment are connected to construct the F...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/00
Inventor 蔡志强司伟涛司书宾张帅李淑敏王宁
Owner SHANGHAI GUAN AN INFORMATION TECH
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