CN111539457B - Fault fusion diagnosis method based on Bayesian network - Google Patents

Fault fusion diagnosis method based on Bayesian network Download PDF

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CN111539457B
CN111539457B CN202010255330.0A CN202010255330A CN111539457B CN 111539457 B CN111539457 B CN 111539457B CN 202010255330 A CN202010255330 A CN 202010255330A CN 111539457 B CN111539457 B CN 111539457B
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fault
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CN111539457A (en
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刘文静
刘成瑞
李文博
武云丽
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Beijing Institute of Control Engineering
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    • G06F18/24155—Bayesian classification
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    • G06F18/00—Pattern recognition
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Abstract

The invention relates to a fault fusion diagnosis method based on a Bayesian network, which comprises the following steps: (1) Carrying out {1,2,3, …, N } numbering on faults of a spacecraft control system, and calculating occurrence probability of each fault; (2) For each fault diagnosis method, calculating the false alarm rate, false alarm rate and accuracy rate when the fault q occurs; and (3) calculating to obtain a fault diagnosis fusion result. The method overcomes the difference of diagnosis results caused by the difference of information acquisition paths, information quantity and diagnosis methods adopted in the fault diagnosis process, and greatly improves the fault diagnosis precision of the spacecraft control system.

Description

Fault fusion diagnosis method based on Bayesian network
Technical Field
The invention relates to a fault fusion diagnosis method.
Background
The fault diagnosis of the spacecraft control system consists of an on-board autonomous diagnosis part and a ground diagnosis part. The information acquired by the on-orbit autonomous fault diagnosis unit reflects the original information of the most direct faults and anomalies of the spacecraft, has the characteristics of good real-time performance and no limitation of measurement and control areas, and limits the application of some advanced fault diagnosis methods due to the calculation amount of the on-orbit computer and the limitation of the storage space. The ground fault diagnosis unit has strong capability of computer software and hardware, and can use various complex models and reasoning methods to comprehensively and deeply diagnose the faults of the spacecraft by combining historical data and expert knowledge of the spacecraft.
Therefore, due to the difference of acquired information, the difference of resource allocation and the difference of personnel intervention degree, different diagnosis methods can be adopted. The diversity of fault diagnosis methods and the limitations of application in the diagnosis of faults in an aircraft result in the possible occurrence of conflicts or differences in the fault diagnosis results obtained.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: aiming at the problem of fusion of fault diagnosis results, giving an online self-adaptive updating strategy of fault occurrence probability, false alarm rate and false alarm rate; on the basis, the fault fusion diagnosis method based on the Bayesian network is provided, the diagnosis result difference caused by the difference of the information acquisition path, the information quantity and the diagnosis method adopted in the fault diagnosis process is overcome, and the fault diagnosis precision of the spacecraft control system is greatly improved.
The technical scheme adopted by the invention is as follows: a fault fusion diagnosis method based on a Bayesian network comprises the following steps:
(1) Carrying out {1,2,3, …, N } numbering on faults of a spacecraft control system, and calculating occurrence probability of each fault; n is the total number of faults of the spacecraft control system and is a positive integer;
(2) For each fault diagnosis method, calculating the false alarm rate, false alarm rate and accuracy rate when the fault q occurs;
(3) And calculating to obtain a fault diagnosis fusion result.
In the step (1), the estimated probability P of occurrence of the fault q at the time of k q (k)：
Wherein Y is q (j) According to D 0 (j) Obtaining:
wherein: d (D) 0 (j) The fault diagnosis fusion result given at the moment j is represented; q=1, 2,3, …, N, k, j are positive integers.
In the step (1), P of the current period q When (k-1) is known, the fault occurs at time kProbability of q occurrence P q (k) The following iterative form is adopted to obtain:
initial value P q (0) And the fault model influence analysis is carried out.
In the step (2), when the fault q occurs, the failure diagnosis method l has a failure report rate beta at the time k l,q (k) The iterative calculation formula of (2) is as follows:
wherein d l,q (j) Representation d l,q The value at the moment j, the data transmitted to the diagnosis result fusion unit by the diagnosis method l is D l ＝{d l,q }，d l,q ∈{0,1}，l＝1,2,…,M；d l,q The expression of =1 indicates that the diagnostic method l considers that the fault q occurs, d l,0 =1 means that diagnostic method i considers no fault to occur; m represents the number of fault diagnosis methods;
β l,q (0) Beta is l,q (k) Is obtained by mathematical simulation statistics.
In the step (2), when the fault q occurs, the fault diagnosis method l identifies the fault q as the false positive rate ζ of the fault p l,p,q (k) The iterative formula of (2) is:
wherein at time kξ l,p,q (0) Is xi l,p,q (k) Is obtained by mathematical simulation statistics.
In step (2), when a fault q occurs, the accuracy ψ of the diagnostic method l l,q (k) The method comprises the following steps:
ψ l,q (k)＝1-β l,q (k)-ξ l,p,q (k)。
in step (3), the diagnosis results d= (D) given for the M fault diagnosis methods 1 ,D 2 ,…,D M ) The fault diagnosis fusion result at the time k+1 is as follows:
wherein,
P 0 (k) The probability that no failure occurs at time k is indicated.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention utilizes the Bayesian network to fuse the fault diagnosis results, can fully consider the multi-factor influences such as the fault occurrence probability, the failure report rate, the false report rate and the like, and effectively ensures the rationality of the fault diagnosis results.
(2) The invention solves the problem of low accuracy of the fault fusion result caused by the simple criterion of 'minority compliance' at present, and the method is simple and clear and is suitable for engineering design.
Detailed Description
The invention is further illustrated below with reference to examples.
Example 1
A fault fusion diagnosis method based on a Bayesian network comprises the following steps:
(1) The faults of the spacecraft control system are numbered {1,2,3, …, N }, and the occurrence probability of each fault is calculated.
Fault q occurrence probability P estimated at k time q (k) The following formula may be used for description:
wherein Y is q (j) According to D 0 (j) Obtaining:
wherein: d (D) 0 (j) The fault diagnosis fusion result given at the moment j is represented; k. j are positive integers.
P of the current period q When (k-1) is known, the probability P of occurrence of the fault q at time k q (k) The following iterative form can also be used to obtain:
wherein the initial value P q (0) Can be obtained by Failure Mode Effect Analysis (FMEA).
(2) For each fault diagnosis method, the rate of false alarm and the rate of accuracy when the fault q occurs are calculated.
Assuming M fault diagnosis methods, N faults are considered, and the data transmitted to the diagnosis result fusion unit by the diagnosis method I is D l ＝{d l,q }(d l,q E {0,1}, l=1, 2, …, M, q=1, 2, …, N). If d l,q The expression of =1 indicates that the diagnostic method l considers that the fault q occurs, d l,0 =1 indicates that diagnostic method i considers no fault to occur.
When a fault q occurs, the failure diagnosis method l has a false alarm rate beta at the time k l,q (k) The calculation formula is as follows:
the above is rewritten into an iterative form:
wherein d l,q (j) Representation d l,q The value at time j, beta l,q (0) Beta is l,q (k) Is typically obtained by multiple mathematical simulation statistics.
When a fault q occurs, the fault diagnosis method l considers the fault q as the false alarm rate xi of the fault p l,p,q (k) The method comprises the following steps:
wherein, at the moment j
The above is rewritten into an iterative form:
at time k
In the same way, xi l,p,q (0) Is xi l,p,q (k) Is typically obtained by multiple mathematical simulation statistics.
Further, it is known that the accuracy ψ of the diagnostic method l when the fault q occurs l,q (k) The method comprises the following steps:
ψ l,q (k)＝1-β l,q (k)-ξ l,p,q (k)
(3) And calculating to obtain a fusion result according to the fault diagnosis fusion rule.
Diagnostic results d= (D) given for M fault diagnostic methods 1 ,D 2 ,…,D M ) The fault diagnosis fusion result at the time k+1 is as follows:
wherein,
D 0 (k+1) represents a fault diagnosis fusion result obtained at time k+1;
P 0 (k) The probability that no failure occurs at time k is indicated.
Although the present invention has been described in terms of the preferred embodiments, it is not intended to be limited to the embodiments, and any person skilled in the art can make any possible variations and modifications to the technical solution of the present invention by using the methods and technical matters disclosed above without departing from the spirit and scope of the present invention, so any simple modifications, equivalent variations and modifications to the embodiments described above according to the technical matters of the present invention are within the scope of the technical matters of the present invention.
The invention, in part not described in detail, is within the skill of those skilled in the art.

Claims (6)

1. A fault fusion diagnosis method based on a Bayesian network is characterized by comprising the following steps:
(1) Carrying out {1,2,3, …, N } numbering on faults of a spacecraft control system, and calculating occurrence probability of each fault; n is the total number of faults of the spacecraft control system and is a positive integer;
(2) For each fault diagnosis method, calculating the false alarm rate, false alarm rate and accuracy rate when the fault q occurs;
(3) Calculating to obtain a fault diagnosis fusion result;
in step (3), the diagnosis results d= (D) given for the M fault diagnosis methods 1 ，D 2 ，…，D M ) The fault diagnosis fusion result at the time k+1 is as follows:
wherein,
P 0 (k) The probability of failure at time k is represented;
P q (k) The occurrence probability of the fault q estimated at the moment k;
β l,q (k) The failure diagnosis method l has a missing report rate at the time k when the failure q occurs;
d l,q (j) Representation d l,q The value at the moment j, the data transmitted to the diagnosis result fusion unit by the diagnosis method l is D l ＝{d l,q }，d l,q ∈{0,1}，l＝1，2，…，M；
ξ l,p,q (k) When the fault q occurs, the fault diagnosis method l determines the fault q as the false alarm rate of the fault p;
ψ l,q (k) Indicating the accuracy of the diagnostic method i when a fault q occurs.
2. The bayesian network-based fault fusion diagnosis method according to claim 1, wherein in the step (1), the probability P of occurrence of the fault q estimated at the time k is determined q (k)：
Wherein Y is q (j) According to D 0 (j) Obtaining:
wherein: d (D) 0 (j) The fault diagnosis fusion result given at the moment j is represented; q=1, 2,3, …, N, k, j are positive integers.
3. The bayesian network-based fault fusion diagnosis method according to claim 2, wherein in the step (1), P of the current period q When (k-1) is known, the probability P of occurrence of the fault q at time k q (k) The following iterative form is adopted to obtain:
initial value P q (0) And the fault model influence analysis is carried out.
4. A bayesian network-based fault fusion diagnosis method according to claim 3, wherein in step (2), when a fault q occurs, the failure diagnosis method i has a failure report rate β at time k l,q (k) The iterative calculation formula of (2) is as follows:
wherein d l,q (j) Representation d l,q The value at the moment j, the data transmitted to the diagnosis result fusion unit by the diagnosis method l is D l ＝{d l,q }，d l,q ∈{0,1}，l＝1，2，…，M；d l,q The expression of =1 indicates that the diagnostic method l considers that the fault q occurs, d l,0 =1 means that diagnostic method i considers no fault to occur; m represents the number of fault diagnosis methods;
β l,q (0) Beta is l,q (k) Is obtained by mathematical simulation statistics.
5. The bayesian network-based fault fusion diagnosis method according to claim 4, wherein step (2)) In the method, when a fault q occurs, the fault diagnosis method l considers the fault q as the false alarm rate xi of the fault p l,p,q (k) The iterative formula of (2) is:
wherein at time kξ l,p,q (0) Is xi l,p,q (k) Is obtained by mathematical simulation statistics.
6. The bayesian network-based fault fusion diagnosis method according to claim 5, wherein in the step (2), when the fault q occurs, the accuracy ψ of the diagnosis method i is set l,q (k) The method comprises the following steps:
ψ l,q (k)＝1-β l,q (k)-ξ l,p,q (k)。
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107544460A (en) * 2017-09-05 2018-01-05 北京控制工程研究所 Consider the diagnosticability quantization method of spacecraft control non-fully failure of removal
CN109117353A (en) * 2018-08-20 2019-01-01 中国石油大学(北京) The fusion method and device of fault diagnosis result
CN109961027A (en) * 2019-03-13 2019-07-02 东北大学 Subspace multi-angle of view electric melting magnesium furnace fault monitoring method is shared based on similarity measurement

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107544460A (en) * 2017-09-05 2018-01-05 北京控制工程研究所 Consider the diagnosticability quantization method of spacecraft control non-fully failure of removal
CN109117353A (en) * 2018-08-20 2019-01-01 中国石油大学(北京) The fusion method and device of fault diagnosis result
CN109961027A (en) * 2019-03-13 2019-07-02 东北大学 Subspace multi-angle of view electric melting magnesium furnace fault monitoring method is shared based on similarity measurement

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
Fault Diagnosis Based on Deep Learning;Feiya Lv等;《2016 American Control Conference (ACC)》;第6851-6856页 *
Principal Polynomial Analysis for Fault Detection and Diagnosis of Industrial Processes;XINMIN ZHANG等;《IEEEAccess》;第52298-52307页 *
Satellite Fault Diagnosis U sing Support Vector Machines Based on a Hybrid Voting Mechanism;Hong Yin等;《Hindawi》;第1-11页 *
基于人工免疫系统的机组复合故障诊断技术研究;岑健;《中国博士学位论文全文数据库工程科技Ⅱ辑》;C029-10 *
基于贝叶斯网络和AMESim仿真的液压系统故障诊断方法;姚成玉;刘文静;赵静一;冯中魁;;机床与液压(13);第172-177页 *
数据驱动的在线ＭＷ-MSPCA故障诊断;胡友强等;《重庆大学学报》;第100-106页 *

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