CN116203920A - Fault diagnosis design method and system based on adjustment and measurement experience knowledge - Google Patents
Fault diagnosis design method and system based on adjustment and measurement experience knowledge Download PDFInfo
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Abstract
The invention discloses a fault diagnosis design method and system based on test experience knowledge, which belong to the field of aviation comprehensive equipment test, and comprise the following steps: s1, preprocessing debugging fault data; s2, constructing a diagnosis knowledge model; s3, designing a similar matching algorithm for matching the target fault phenomenon in a fault experience knowledge base; s4, perfecting a fault knowledge base. The invention improves the debugging and testing fault diagnosis efficiency of aviation comprehensive equipment and changes the existing mode of debugging and testing fault diagnosis by technicians.
Description
Technical Field
The invention relates to the field of adjustment and measurement of aviation comprehensive equipment, in particular to a fault diagnosis design method and system based on adjustment and measurement experience knowledge.
Background
The conventional debugging and testing fault diagnosis of the aviation comprehensive equipment is mainly carried out by technicians, and because the aviation comprehensive equipment is high in integration level, debugging and testing technology intervals among different models and different projects are large, the cultivation period of the technicians is too long, so that the production requirements of the comprehensive equipment cannot be met by the aid of the technicians for carrying out fault diagnosis, the debugging and testing fault diagnosis efficiency of the aviation comprehensive equipment is low, and the technicians in the field need to change the conventional debugging and testing fault diagnosis mode by the aid of the technicians.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a fault diagnosis design method and system based on test experience knowledge, which improves test fault diagnosis efficiency of aviation comprehensive equipment, changes the mode of test fault diagnosis by technicians and the like.
The invention aims at realizing the following scheme:
a fault diagnosis design method based on tuning experience knowledge comprises the following steps:
s1, preprocessing debugging fault data;
s2, constructing a diagnosis knowledge model;
s3, designing a similar matching algorithm for matching the target fault phenomenon in a fault experience knowledge base;
s4, perfecting a fault knowledge base.
Further, in step S1, the debug fault data preprocessing specifically includes: analyzing the debugging fault attribute, extracting debugging fault elements and converting debugging fault data;
the debugging fault attribute analysis realizes the classification of equipment debugging fault product attributes, environment attributes and logic attributes based on experience fault data through the analysis of the debugging process of the aviation comprehensive equipment, and forms attribute classes;
the debugging fault element extraction is carried out by analyzing the existing experience fault data of the aviation comprehensive equipment and combining the special attribute of the debugging fault to form a debugging fault element table;
and converting the modulation fault data according to the modulation fault element table aiming at the existing modulation fault data to form a standardized modulation fault experience database.
Further, in step S2, the construction of the diagnostic knowledge model specifically includes: functional knowledge structure analysis and functional fault mapping analysis;
the functional knowledge structure analysis is performed by combing equipment working function sets, integrated extension sets and configuration modules according to the design principle of aviation comprehensive equipment to form a standardized functional knowledge structure tree;
the functional fault mapping analysis establishes a logical relation among equipment functional technical index items, fault root causes and fault phenomena based on existing debugging fault experience data.
Further, in step S3, the design of the similarity matching algorithm includes the following sub-steps:
s31, establishing a function set, an integrated extension set and a configuration module set according to the carded equipment function knowledge structure tree, and setting:
function set f= { F 1 ,f 2 ,f 3 ,…,f n1 }；
Integrated extension set i= { I 1 ,i 2 ,i 3 ,…,i n2 }；
Configuration module set m= { M 1 ,m 2 ,m 3 ,…,m n3 }；
wherein ,fn1 A certain function realized for the aviation comprehensive equipment, n1 is the number of functions, i n2 Integrated extension set for realizing certain function for aviation comprehensive equipment, n2 is the number of extension sets, m n3 Configuration modules required for realizing a certain function for the aviation comprehensive equipment are provided, wherein n3 is the number of modules;
s32, establishing a functional technical index set according to the carded equipment functional fault mapping analysis, and setting:
technical index set t= { T 1 ,t 2 ,t 3 ,…,t n4 }；
wherein ,tn4 A certain functional technical index item realized for the aviation comprehensive equipment, wherein n4 is the number of the functional technical index items;
s33, performing word segmentation processing on a text value of a target fault phenomenon description and a certain standard fault phenomenon description of a debugging fault knowledge base through a Chinese word segmentation algorithm of natural language processing to obtain two fault phenomenon word sets, and setting:
word set of target fault phenomena
Standard trouble phenomenon word set
wherein ,
describing word segmentation for the target fault phenomenon, wherein n5 is the number of word segmentation; />
Describing word segmentation for a certain standard fault phenomenon, wherein n6 is the number of word segmentation;
s34, setting:
C 1 ＝F∩W T ，ct 1 for set C 1 The number of elements;
C 2 ＝I∩W T ，ct 2 for set C 2 The number of elements;
C 3 ＝M∩W T ，ct 3 for set C 3 The number of elements;
C 4 ＝T∩W T ，ct 4 for set C 4 The number of elements;
C 5 ＝W S ∩W T ，ct 5 for set C 5 The number of elements;
s35, according to the comprehensive fault attribute adjustment and measurement, functional attribute, integrated extension, configuration module and technical index pair causeThe influence degree of the obstacle diagnosis is different, and the weight is set as P 1 、P 2 、P 3 、P 4 Target fault similarity:
s36, repeating the steps S33-S35 for standard fault records in the debugging fault knowledge base to obtain a group of target fault similarities
wherein ei And in order to test the number of standard fault records in the fault knowledge base, the standard fault record corresponding to the maximum value in the similarity array is the matching output result.
Further, in step S4, the fault knowledge base is perfect, specifically, for the situation that the result cannot be matched through the similarity matching algorithm or the matched standard fault knowledge cannot solve the target fault, the solution of the target fault is realized through a technician, and meanwhile, the fault knowledge data recording process is performed according to the debugging fault attribute and the debugging fault element.
Further, the product attribute is equipment composition entity, including equipment function, extension and module;
the environmental attribute refers to the environmental conditions including normal temperature, high temperature, low pressure and vibration when the fault is detected;
the logical attribute is the root cause and the solving measure of the debugging fault, and the root cause of the fault comprises components, software and process factors, which represent the causal relationship which leads to the occurrence of the fault phenomenon.
Further, the logical relationship among the fault phenomena comprises a belonging relationship, a leading relationship and an association relationship;
the belonging relation is that the fault phenomenon belongs to a certain product attribute of equipment;
the result relationship is that a certain device fault of equipment causes a certain attribute fault of a product, or a certain attribute fault of the product causes another product attribute fault, and the product attribute fault exists in an equipment function implementation flow;
the association relation is the correlation between the product fault phenomenon and the fault environment attribute of the debugging system.
A fault diagnosis system based on empirical knowledge of tuning, comprising:
the preprocessing module is used for preprocessing debugging fault data;
the diagnosis knowledge model construction module is used for constructing a diagnosis knowledge model;
the similarity matching module is used for designing a similarity matching algorithm and realizing the matching of the target fault phenomenon in a fault experience knowledge base;
and the fault knowledge base perfecting module is used for perfecting the fault knowledge base.
Further, the diagnosis knowledge model construction module comprises a functional knowledge structure analysis module and a functional fault mapping analysis module;
the functional knowledge structure analysis module is used for carding equipment working function sets, integration extensions and configuration modules through the design principle of aviation comprehensive equipment to form a standardized functional knowledge structure tree;
the functional fault mapping analysis module is used for establishing a logical relation among equipment functional technical index items, fault root causes and fault phenomena based on existing debugging fault experience data.
Further, the similarity matching module includes:
the first setting module is used for establishing a function set, an integrated extension set and a configuration module set according to the carded equipment function knowledge structure tree, and setting:
function set f= { F 1 ,f 2 ,f 3 ,…,f n1 }；
Integrated extension set i= { I 1 ,i 2 ,i 3 ,…,i n2 }；
Configuration module set m= { M 1 ,m 2 ,m 3 ,…,m n3 }；
wherein ,fn1 Is aeronautical comprehensiveSome function implemented by the equipment, n1 is the number of functions, i n2 Integrated extension set for realizing certain function for aviation comprehensive equipment, n2 is the number of extension sets, m n3 Configuration modules required for realizing a certain function for the aviation comprehensive equipment are provided, wherein n3 is the number of modules;
the second setting module is used for establishing a functional technical index set according to the carded equipment functional fault mapping analysis and setting:
technical index set t= { T 1 ,t 2 ,t 3 ,…,t n4 }；
wherein ,tn4 A certain functional technical index item realized for the aviation comprehensive equipment, wherein n4 is the number of the functional technical index items;
the third setting module is used for carrying out word segmentation processing on the text value of the target fault phenomenon description and a certain standard fault phenomenon description of the debugging fault knowledge base through a Chinese word segmentation algorithm of natural language processing to obtain two fault phenomenon word sets, and setting:
word set of target fault phenomena
Standard trouble phenomenon word set
wherein ,
describing word segmentation for the target fault phenomenon, wherein n5 is the number of word segmentation; />
Describing word segmentation for a certain standard fault phenomenon, wherein n6 is the number of word segmentation;
a fourth setting module, configured to set:
C 1 ＝F∩W T ，ct 1 for set C 1 The number of elements;
C 2 ＝I∩W T ，ct 2 for set C 2 The number of elements;
C 3 ＝M∩W T ，ct 3 for set C 3 The number of elements;
C 4 ＝T∩W T ，ct 4 for set C 4 The number of elements;
C 5 ＝W S ∩W T ，ct 5 for set C 5 The number of elements;
the target fault similarity calculation module is used for setting the weight as P according to different influence degrees of the functional attribute, the integrated extension, the configuration module and the technical index on fault diagnosis according to comprehensive debugging fault attribute 1 、P 2 、P 3 、P 4 Target fault similarity:
the maximum value searching module is used for repeating the flow from the third setting module to the target fault similarity calculating module aiming at the standard fault record in the adjustment fault knowledge base to obtain a group of target fault similarity
wherein ei And in order to test the number of standard fault records in the fault knowledge base, the standard fault record corresponding to the maximum value in the similarity array is the matching output result.
The beneficial effects of the invention include:
(1) And the aviation comprehensive equipment regulates and tests the experience data to realize knowledge reserve and inheritance. The invention fully utilizes the discrete fault record data of the current aviation comprehensive equipment through the establishment of the debugging fault knowledge base, realizes the accumulation and storage of debugging fault knowledge, and simultaneously provides a debugging fault knowledge inheritance method and a path in the form of the knowledge base.
(2) The requirements of the aviation comprehensive equipment debugging and testing fault diagnosis technicians are reduced. According to the invention, through the fault diagnosis design of the debugging experience knowledge, the automatic analysis and positioning of the debugging faults are realized, the current fault diagnosis modes aiming at technicians of different models and different projects are broken, and the cost investment of the technicians is reduced.
(3) The fault diagnosis design method based on the adjustment and measurement experience knowledge is convenient to popularize and apply. The design method adopted by the invention is suitable for the adjustment and measurement process of aviation comprehensive equipment, and the current aviation comprehensive product accumulates a large amount of fault experience data in the production process due to higher integration level and complexity, has abundant fault sample size, and meanwhile, the data processing and similarity matching algorithm has uniformity, is suitable for different models and different projects, has stronger portability and replicability, and is convenient for popularization and application.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a flow chart of a fault diagnosis design method of a certain type of aviation synthesis equipment based on adjustment experience knowledge according to an embodiment of the invention.
Detailed Description
All of the features disclosed in all of the embodiments of this specification, or all of the steps in any method or process disclosed implicitly, except for the mutually exclusive features and/or steps, may be combined and/or expanded and substituted in any way.
In view of the technical problems in the background, the inventor of the invention considers that after creative thinking, fault diagnosis based on debugging experience knowledge can be realized by carrying out standardized processing on fault data accumulated in the debugging process of aviation comprehensive equipment, extracting fault elements to form structured and standardized experience knowledge, establishing a diagnosis knowledge model and realizing automatic analysis and positioning of faults in the debugging process according to a similar matching algorithm.
In a further conception, the fault diagnosis design based on the test experience knowledge mainly comprises several stages of test fault data preprocessing, diagnosis knowledge model construction, similarity matching algorithm design and fault knowledge base improvement.
The data preprocessing is used for carrying out structuring and standardization processing on fault data accumulated in the adjustment and measurement process of the aviation comprehensive equipment, and mainly comprises the steps of classifying adjustment and measurement fault attributes through analysis of the adjustment and measurement attribute, extracting adjustment and measurement fault elements and realizing conversion of adjustment and measurement experience fault data.
And constructing a diagnosis knowledge model, namely converting loose diagnosis information which is stored in a database or a table file and is described by natural language into a standardized structured diagnosis knowledge graph model according to the functional principle of the aviation comprehensive equipment and the mapping relation between the debugging fault elements and the functional knowledge structure.
The matching calculation design process of the standard record attribute in the debugging fault knowledge base aiming at the target fault attribute by the design similarity matching algorithm is realized by carrying out word segmentation processing on the target fault text language, then carrying out matching with the standard fault phenomenon record one by one through the constraint of the functional attribute, the integrated extension, the configuration module and the functional technical index, and calculating the similarity of the target fault and the standard record fault in the debugging fault knowledge base according to the similarity matching result.
The fault knowledge base perfects the process of recording the fault information through the debugging fault knowledge base maintenance function aiming at the information which is not matched with the knowledge data suitable for the target or matched knowledge data and fails to solve the target fault in the debugging fault knowledge base.
In a further embodiment, the present embodiment provides a fault diagnosis design method based on tuning experience knowledge, including the following steps:
s1, preprocessing debugging fault data;
s2, constructing a diagnosis knowledge model;
s3, designing a similarity matching algorithm;
s4, perfecting a fault knowledge base;
through completing the stages, the fault diagnosis design method based on the adjustment experience knowledge is realized.
In a further embodiment, the debug fault data preprocessing includes the following sub-steps:
the related content comprises debugging fault attribute analysis, debugging fault element extraction and debugging fault data conversion. The debugging fault attribute analysis refers to the realization of classifying equipment debugging fault product attributes, environment attributes and logic attributes based on experience fault data through the analysis of an aviation comprehensive equipment debugging procedure, and attribute classes are formed, wherein the product attributes refer to equipment forming entities and mainly comprise equipment functions, extensions and modules; the environmental attribute refers to the environmental condition when the fault is detected, and mainly comprises normal temperature, high temperature, low pressure and vibration; the logical attribute is the root cause and solving measure of the debugging fault, and the root cause of the fault comprises factors such as components, software, technology and the like, and represents the causal relationship which leads to the occurrence of the fault phenomenon. The extraction of the debugging fault elements refers to the formation of a debugging fault element list by analyzing the existing experience fault data of the aviation comprehensive equipment and combining the special attribute of the debugging fault. The conversion of the debugging fault data is to convert the existing debugging fault data according to a debugging fault element table by a pointer to form a standardized debugging fault experience database.
In a further embodiment, the constructing a diagnostic knowledge model includes the following sub-steps:
the related content comprises functional knowledge structure analysis and functional fault mapping analysis. The functional knowledge structure analysis mainly comprises the steps of carding equipment working function collection, integration extension and configuration module through the design principle of aviation comprehensive equipment, and forming a standardized functional knowledge structure tree. The function fault mapping analysis is mainly to establish logical relations between equipment function technical index items, fault root causes and fault phenomena, including belonging relations, leading relations and incidence relations, based on existing debugging fault experience data. The term "belonging to a relationship" means that the failure phenomenon belongs to a certain product attribute of the equipment, such as that the UV voice call quality is poor and the failure phenomenon belongs to the UV function. The cause relation means that a certain device failure of equipment causes a certain attribute failure of a product, or causes another attribute failure of a product after a certain attribute failure of the product, and the cause relation mainly exists in the equipment function implementation flow. The association relation refers to the correlation between the product fault phenomenon and the environment attribute of the debugging fault, such as low-temperature fault, high-temperature fault and the like.
In a further embodiment, the design of the similarity matching algorithm comprises the following sub-steps:
the related content mainly realizes the matching of the target fault phenomenon in the fault experience knowledge base, and the realization steps are as follows.
Step 1, establishing a function set, an integrated extension set and a configuration module set according to a carded equipment function knowledge structure tree, and setting
Function set f= { F 1 ,f 2 ,f 3 ,…,f n1 }；
Integrated extension set i= { I 1 ,i 2 ,i 3 ,…,i n2 }；
Configuration module set m= { M 1 ,m 2 ,m 3 ,…,m n3 }；
wherein fn1 A certain function realized for the aviation comprehensive equipment, wherein n1 is the number of functions; i.e n2 The integrated extension set for realizing a certain function for the aviation comprehensive equipment, wherein n2 is the number of extension sets; m is m n3 Configuration modules required for realizing a certain function for the aviation comprehensive equipment are provided, wherein n3 is the number of modules;
step 2, establishing a functional technical index set according to the carded equipment functional fault mapping analysis, and setting a technical index set T= { T 1 ,t 2 ,t 3 ,…,t n4 }；
wherein tn4 A certain functional technical index item realized for the aviation comprehensive equipment, wherein n4 is the number of the functional technical index items;
step 3, word segmentation processing is carried out on text values of the target fault phenomenon description and a certain standard fault phenomenon description of the debugging fault knowledge base through a Chinese word segmentation algorithm of natural language processing, two fault phenomenon word sets are obtained, and setting is carried out
Word set of target fault phenomena
Standard trouble phenomenon word set
wherein 
Describing word segmentation for the target fault phenomenon, wherein n5 is the number of word segmentation; />
Describing word segmentation for a certain standard fault phenomenon, wherein n6 is the number of word segmentation;
step 4, setting
C 1 ＝F∩W T ，ct 1 For set C 1 The number of elements;
C 2 ＝I∩W T ，ct 2 for set C 2 The number of elements;
C 3 ＝M∩W T ，ct 3 for set C 3 The number of elements;
C 4 ＝T∩W T ，ct 4 for set C 4 The number of elements;
C 5 ＝W S ∩W T ，ct 5 for set C 5 The number of elements;
step 5, according to the comprehensive debugging fault attribute, the degree of influence of the functional attribute, the integrated extension, the configuration module and the technical index on fault diagnosis is different, and the weight is set as P 1 、P 2 、P 3 、P 4 Similarity of target faults
Step 6, repeating the steps 3 to 5 aiming at standard fault records in the debugging fault knowledge base to obtain a group of target fault similarity
wherein ei And in order to test the number of standard fault records in the fault knowledge base, the standard fault record corresponding to the maximum value in the similarity array is the matching output result.
In a further embodiment, the fault knowledge base is complete and comprises the following sub-steps:
the related content is mainly aimed at the situation that the result cannot be matched through a similarity matching algorithm or the matched standard fault knowledge cannot solve the target fault, and the technical personnel can solve the target fault and simultaneously record fault knowledge data according to the debugging fault attribute and the debugging fault element.
With the increasing demand of aviation comprehensive equipment, the rapid diagnosis of production and after-sale process faults is more strictly required, and the implementation of the fault diagnosis design method based on the adjustment experience knowledge can greatly shorten the fault diagnosis and maintenance period, simultaneously save the investment demands of manpower and material resources in the process, and is a method with twice the effort.
The realization and application of the fault diagnosis design method based on the debugging experience knowledge improve the debugging production efficiency of the equipment to a certain extent. The fault diagnosis design based on the test experience knowledge establishes a test fault knowledge base which can be continuously accumulated and perfected by utilizing fault experience data accumulated in the test process of the aviation comprehensive equipment, and realizes automatic analysis and matching of test faults in the knowledge base according to an similarity matching algorithm, thereby achieving the purpose of quick test fault diagnosis.
In other embodiments of the present invention, as shown in fig. 1, the embodiment of the present invention first performs preprocessing on the past test failure experience number of a certain type of aviation synthesis equipment to form a standardized test failure database corresponding to the type of aviation synthesis equipment; then, carrying out functional knowledge structure analysis and functional fault mapping analysis on the model equipment, and constructing a debugging fault diagnosis model suitable for the model equipment; finally, an input set of the similar matching algorithm is established to form an algorithm suitable for the type of debugging fault matching, and the debugging fault diagnosis efficiency is higher and higher along with the continuous enrichment of the sample size.
Taking a certain aviation comprehensive device as an example, the following implementation steps are adopted:
step 1: the equipment debugging fault product attribute, the environment attribute and the logic attribute are classified through debugging fault attribute analysis, so that attribute classes are formed, as shown in table 1.
TABLE 1 some type of equipment attribute class table
According to the existing experimental fault data of the equipment, a fault element table is constructed according to the existing experimental fault data of the equipment in advance, as shown in table 2, the existing debugging fault data conversion is realized according to the fault element table, and a standardized debugging fault experimental database is formed, as shown in table 3.
Table 2 fault element table
Table 3 debug failure database
Step 2: the model equipment was modeled for function and fault mapping as shown in table 4.
Table 4 functional and fault map modeling
Step 3: setting an input set of an similarity matching algorithm corresponding to the model, and setting
Function set f= { UV function, HF function, ILS function, MLS function, TACAN function ATC function … };
integration extension set i= { low frequency rack, communication control box, HF power amplifier, HF antenna tuning … };
configuring a module set M= { UVRT module, a UV antenna interface module, a UV receiving module, a UV exciting module, an MLSM module, an HFM module, an LB receiving module and an LB exciting module … };
technical index set t= { UV voice receiving sensitivity, UV voice receiving distortion degree, UV voice receiving dynamic range, radio frequency input frequency, gain control range … };
step 4: when the low-frequency rack is debugged at normal temperature by using the model target fault, the UV voice receives noise, when the standard fault records the normal temperature debugging, the power-on is firstly performed, the UV function is abnormal, the scanning signal and the excitation signal of the transmitting channel are measured to be normal, and the receiving channel cannot receive; a low frequency chassis; UVRT; debugging at normal temperature; normal temperature; first power-up; a UV function; receiving; a reception sensitivity; stabilization "for example, gives
Target fault phenomenon word set W T = { low frequency rack, normal temperature debug, UV voice, low frequency, rack, normal temperature, debug, UV, voice, receive, noise };
standard fault phenomenon word set W S = { normal temperature debug, first power-up, UV function, scan signal, excitation signal, low frequency rack, first power-up, normal temperature, debug, first power-up, test, UV, function, anomaly, emission, channel, scan, signal, excitation, normal, receive, unable, low frequency, rack, UVRT, power-up, sensitivity, stability };
step 5: setting up
C 1 ＝F∩W T Then intersection element number ct 1 ＝0；
C 2 ＝I∩W T Then intersection element number ct 2 ＝1；
C 3 ＝M∩W T Then intersection element number ct 3 ＝1；
C 4 ＝T∩W T Then intersection element number ct 4 ＝0；
C 5 ＝W S ∩W T ThenNumber of intersection elements ct 5 ＝8；
Step 6, setting the influence weights of the functional attribute, the integration extension, the configuration module and the technical index on fault diagnosis to be 1, 0.8, 0.5 and 0.3, and then obtaining the target fault similarity
And (3) repeating the steps 3-5 aiming at the standard fault records in the test fault knowledge base to obtain a group of target fault similarities, wherein the standard fault record corresponding to the maximum value in the similarities is the matching output result.
It should be noted that, within the scope of protection defined in the claims of the present invention, the following embodiments may be combined and/or expanded, and replaced in any manner that is logical from the above specific embodiments, such as the disclosed technical principles, the disclosed technical features or the implicitly disclosed technical features, etc.
Example 1
A fault diagnosis design method based on tuning experience knowledge comprises the following steps:
s1, preprocessing debugging fault data;
s2, constructing a diagnosis knowledge model;
s3, designing a similar matching algorithm for matching the target fault phenomenon in a fault experience knowledge base;
s4, perfecting a fault knowledge base.
Example 2
On the basis of embodiment 1, in step S1, the debug fault data preprocessing specifically includes: analyzing the debugging fault attribute, extracting debugging fault elements and converting debugging fault data;
the debugging fault attribute analysis realizes the classification of equipment debugging fault product attributes, environment attributes and logic attributes based on experience fault data through the analysis of the debugging process of the aviation comprehensive equipment, and forms attribute classes;
the debugging fault element extraction is carried out by analyzing the existing experience fault data of the aviation comprehensive equipment and combining the special attribute of the debugging fault to form a debugging fault element table;
and converting the modulation fault data according to the modulation fault element table aiming at the existing modulation fault data to form a standardized modulation fault experience database.
Example 3
On the basis of embodiment 1, in step S2, the construction of a diagnostic knowledge model is specifically: functional knowledge structure analysis and functional fault mapping analysis;
the functional knowledge structure analysis is performed by combing equipment working function sets, integrated extension sets and configuration modules according to the design principle of aviation comprehensive equipment to form a standardized functional knowledge structure tree;
the functional fault mapping analysis establishes a logical relation among equipment functional technical index items, fault root causes and fault phenomena based on existing debugging fault experience data.
Example 4
On the basis of embodiment 1, in step S3, the design of the similarity matching algorithm includes the following sub-steps:
s31, establishing a function set, an integrated extension set and a configuration module set according to the carded equipment function knowledge structure tree, and setting:
function set f= { F 1 ,f 2 ,f 3 ,…,f n1 }；
Integrated extension set i= { I 1 ,i 2 ,i 3 ,…,i n2 }；
Configuration module set m= { M 1 ,m 2 ,m 3 ,…,m n3 }；
wherein ,fn1 A certain function realized for the aviation comprehensive equipment, n1 is the number of functions, i n2 Integrated extension set for realizing certain function for aviation comprehensive equipment, n2 is the number of extension sets, m n3 Configuration modules required for realizing a certain function for the aviation comprehensive equipment are provided, wherein n3 is the number of modules;
s32, establishing a functional technical index set according to the carded equipment functional fault mapping analysis, and setting:
technical index set t= { T 1 ,t 2 ,t 3 ,…,t n4 }；
wherein ,tn4 A certain functional technical index item realized for the aviation comprehensive equipment, wherein n4 is the number of the functional technical index items;
s33, performing word segmentation processing on a text value of a target fault phenomenon description and a certain standard fault phenomenon description of a debugging fault knowledge base through a Chinese word segmentation algorithm of natural language processing to obtain two fault phenomenon word sets, and setting:
word set of target fault phenomena
Standard trouble phenomenon word set
wherein ,
describing word segmentation for the target fault phenomenon, wherein n5 is the number of word segmentation; />
Describing word segmentation for a certain standard fault phenomenon, wherein n6 is the number of word segmentation;
s34, setting:
C 1 ＝F∩W T ，ct 1 for set C 1 The number of elements;
C 2 ＝I∩W T ，ct 2 for set C 2 The number of elements;
C 3 ＝M∩W T ，ct 3 for set C 3 The number of elements;
C 4 ＝T∩W T ，ct 4 for set C 4 The number of elements;
C 5 ＝W S ∩W T ，ct 5 for set C 5 The number of elements;
s35, according to the comprehensive debugging fault attribute, the influence degree of the functional attribute, the integrated extension, the configuration module and the technical index on fault diagnosis is different, and the weight is set as P 1 、P 2 、P 3 、P 4 Target fault similarity:
s36, repeating the steps S33-S35 for standard fault records in the debugging fault knowledge base to obtain a group of target fault similarities
wherein ei And in order to test the number of standard fault records in the fault knowledge base, the standard fault record corresponding to the maximum value in the similarity array is the matching output result.
Example 5
Based on embodiment 1, in step S4, the fault knowledge base is perfect, specifically, for the case that the result cannot be matched through the similarity matching algorithm or the matched standard fault knowledge cannot solve the target fault, the solution of the target fault is realized through a technician, and meanwhile, the fault knowledge data recording process is performed according to the tuning fault attribute and the tuning fault element.
Example 6
On the basis of the embodiment 2, the product attribute is equipment composition entity, including equipment function, extension and module;
the environmental attribute refers to the environmental conditions including normal temperature, high temperature, low pressure and vibration when the fault is detected;
the logical attribute is the root cause and the solving measure of the debugging fault, and the root cause of the fault comprises components, software and process factors, which represent the causal relationship which leads to the occurrence of the fault phenomenon.
Example 7
On the basis of embodiment 3, the logical relationships among the fault phenomena include belonging relationships, leading relationships and incidence relationships;
the belonging relation is that the fault phenomenon belongs to a certain product attribute of equipment;
the result relationship is that a certain device fault of equipment causes a certain attribute fault of a product, or a certain attribute fault of the product causes another product attribute fault, and the product attribute fault exists in an equipment function implementation flow;
the association relation is the correlation between the product fault phenomenon and the fault environment attribute of the debugging system.
Example 8
A fault diagnosis system based on empirical knowledge of tuning, comprising:
the preprocessing module is used for preprocessing debugging fault data;
the diagnosis knowledge model construction module is used for constructing a diagnosis knowledge model;
the similarity matching module is used for designing a similarity matching algorithm and realizing the matching of the target fault phenomenon in a fault experience knowledge base;
and the fault knowledge base perfecting module is used for perfecting the fault knowledge base.
Example 9
On the basis of embodiment 8, the diagnostic knowledge model construction module comprises a functional knowledge structure analysis module and a functional fault mapping analysis module;
the functional knowledge structure analysis module is used for carding equipment working function sets, integration extensions and configuration modules through the design principle of aviation comprehensive equipment to form a standardized functional knowledge structure tree;
the functional fault mapping analysis module is used for establishing a logical relation among equipment functional technical index items, fault root causes and fault phenomena based on existing debugging fault experience data.
Example 10
On the basis of embodiment 8, the similarity matching module includes:
the first setting module is used for establishing a function set, an integrated extension set and a configuration module set according to the carded equipment function knowledge structure tree, and setting:
function set f= { F 1 ,f 2 ,f 3 ,…,f n1 }；
Integrated extension set i= { I 1 ,i 2 ,i 3 ,…,i n2 }；
Configuration module set m= { M 1 ,m 2 ,m 3 ,…,m n3 }；
wherein ,fn1 A certain function realized for the aviation comprehensive equipment, n1 is the number of functions, i n2 Integrated extension set for realizing certain function for aviation comprehensive equipment, n2 is the number of extension sets, m n3 Configuration modules required for realizing a certain function for the aviation comprehensive equipment are provided, wherein n3 is the number of modules;
the second setting module is used for establishing a functional technical index set according to the carded equipment functional fault mapping analysis and setting:
technical index set t= { T 1 ,t 2 ,t 3 ,…,t n4 }；
wherein ,tn4 A certain functional technical index item realized for the aviation comprehensive equipment, wherein n4 is the number of the functional technical index items;
the third setting module is used for carrying out word segmentation processing on the text value of the target fault phenomenon description and a certain standard fault phenomenon description of the debugging fault knowledge base through a Chinese word segmentation algorithm of natural language processing to obtain two fault phenomenon word sets, and setting:
word set of target fault phenomena
Standard trouble phenomenon word set
wherein ,
tracing for target fault phenomenaThe word segmentation, n5 is the number of the word segmentation; />
Describing word segmentation for a certain standard fault phenomenon, wherein n6 is the number of word segmentation;
a fourth setting module, configured to set:
C 1 ＝F∩W T ，ct 1 for set C 1 The number of elements;
C 2 ＝I∩W T ，ct 2 for set C 2 The number of elements;
C 3 ＝M∩W T ，ct 3 for set C 3 The number of elements;
C 4 ＝T∩W T ，ct 4 for set C 4 The number of elements;
C 5 ＝W S ∩W T ，ct 5 for set C 5 The number of elements;
the target fault similarity calculation module is used for setting the weight as P according to different influence degrees of the functional attribute, the integrated extension, the configuration module and the technical index on fault diagnosis according to comprehensive debugging fault attribute 1 、P 2 、P 3 、P 4 Target fault similarity:
the maximum value searching module is used for repeating the flow from the third setting module to the target fault similarity calculating module aiming at the standard fault record in the adjustment fault knowledge base to obtain a group of target fault similarity
wherein ei And in order to test the number of standard fault records in the fault knowledge base, the standard fault record corresponding to the maximum value in the similarity array is the matching output result.
The units involved in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
According to an aspect of embodiments of the present invention, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from the computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the methods provided in the various alternative implementations described above.
As another aspect, the embodiment of the present invention also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the methods described in the above embodiments.
The invention is not related in part to the same as or can be practiced with the prior art.
The foregoing technical solution is only one embodiment of the present invention, and various modifications and variations can be easily made by those skilled in the art based on the application methods and principles disclosed in the present invention, not limited to the methods described in the foregoing specific embodiments of the present invention, so that the foregoing description is only preferred and not in a limiting sense.
In addition to the foregoing examples, those skilled in the art will recognize from the foregoing disclosure that other embodiments can be made and in which various features of the embodiments can be interchanged or substituted, and that such modifications and changes can be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. The fault diagnosis design method based on the adjustment experience knowledge is characterized by comprising the following steps of:
s1, preprocessing debugging fault data;
s2, constructing a diagnosis knowledge model;
s3, designing a similar matching algorithm for matching the target fault phenomenon in a fault experience knowledge base;
s4, perfecting a fault knowledge base.
2. The fault diagnosis design method based on the empirical knowledge of debugging according to claim 1, wherein in step S1, the debugging fault data preprocessing is specifically: analyzing the debugging fault attribute, extracting debugging fault elements and converting debugging fault data;
the debugging fault attribute analysis realizes the classification of equipment debugging fault product attributes, environment attributes and logic attributes based on experience fault data through the analysis of the debugging process of the aviation comprehensive equipment, and forms attribute classes;
the debugging fault element extraction is carried out by analyzing the existing experience fault data of the aviation comprehensive equipment and combining the special attribute of the debugging fault to form a debugging fault element table;
and converting the modulation fault data according to the modulation fault element table aiming at the existing modulation fault data to form a standardized modulation fault experience database.
3. The fault diagnosis design method based on the empirical knowledge of tuning as claimed in claim 1, wherein in step S2, the construction of the diagnosis knowledge model is specifically: functional knowledge structure analysis and functional fault mapping analysis;
the functional knowledge structure analysis is performed by combing equipment working function sets, integrated extension sets and configuration modules according to the design principle of aviation comprehensive equipment to form a standardized functional knowledge structure tree;
the functional fault mapping analysis establishes a logical relation among equipment functional technical index items, fault root causes and fault phenomena based on existing debugging fault experience data.
4. The fault diagnosis design method based on tuning experience knowledge according to claim 1, wherein in step S3, the design of the similarity matching algorithm includes the following sub-steps:
s31, establishing a function set, an integrated extension set and a configuration module set according to the carded equipment function knowledge structure tree, and setting:
function set f= { F 1 ,f 2 ,f 3 ,…,f n1 }；
Integrated extension set i= { I 1 ,i 2 ,i 3 ,…,i n2 }；
Configuration module set m= { M 1 ,m 2 ,m 3 ,…,m n3 }；
wherein ,fn1 A certain function realized for the aviation comprehensive equipment, n1 is the number of functions, i n2 Integrated extension set for realizing certain function for aviation comprehensive equipment, n2 is the number of extension sets, m n3 Configuration modules required for realizing a certain function for the aviation comprehensive equipment are provided, wherein n3 is the number of modules;
s32, establishing a functional technical index set according to the carded equipment functional fault mapping analysis, and setting:
technical index set t= { T 1 ,t 2 ,t 3 ,…,t n4 }；
wherein ,tn4 A certain functional technical index item realized for the aviation comprehensive equipment, wherein n4 is the number of the functional technical index items;
s33, performing word segmentation processing on a text value of a target fault phenomenon description and a certain standard fault phenomenon description of a debugging fault knowledge base through a Chinese word segmentation algorithm of natural language processing to obtain two fault phenomenon word sets, and setting:
word set of target fault phenomena
Standard trouble phenomenon word set
/>
wherein ,
describing word segmentation for the target fault phenomenon, wherein n5 is the number of word segmentation; />
Describing word segmentation for a certain standard fault phenomenon, wherein n6 is the number of word segmentation;
s34, setting:
C 1 ＝F∩W T ，ct 1 for set C 1 The number of elements;
C 2 ＝I∩W T ，ct 2 for set C 2 The number of elements;
C 3 ＝M∩W T ，ct 3 for set C 3 The number of elements;
C 4 ＝T∩W T ，ct 4 for set C 4 The number of elements;
C 5 ＝W S ∩W T ，ct 5 for set C 5 The number of elements;
s35, according to the comprehensive debugging fault attribute, the influence degree of the functional attribute, the integrated extension, the configuration module and the technical index on fault diagnosis is different, and the weight is set as P 1 、P 2 、P 3 、P 4 Target fault similarity:
s36, repeating the steps S33-S35 for standard fault records in the debugging fault knowledge base to obtain a group of target fault similarities
wherein ei And in order to test the number of standard fault records in the fault knowledge base, the standard fault record corresponding to the maximum value in the similarity array is the matching output result.
5. The fault diagnosis design method based on the test experience knowledge according to claim 1, wherein in step S4, the fault knowledge base is perfect, specifically, for the case that the result cannot be matched through the similarity matching algorithm or the matched standard fault knowledge cannot solve the target fault, the solution of the target fault is realized through a technician, and meanwhile, the process of fault knowledge data recording is performed according to the test fault attribute and the test fault element.
6. The fault diagnosis design method based on the tuning experience knowledge according to claim 2, wherein,
the product attribute is equipment composition entity, including equipment function, extension and module;
the environmental attribute refers to the environmental conditions including normal temperature, high temperature, low pressure and vibration when the fault is detected;
the logical attribute is the root cause and the solving measure of the debugging fault, and the root cause of the fault comprises components, software and process factors, which represent the causal relationship which leads to the occurrence of the fault phenomenon.
7. The fault diagnosis design method based on the tuning experience knowledge according to claim 3, wherein,
the logical relationship among the fault phenomena comprises a belonging relationship, a leading relationship and a correlation relationship;
the belonging relation is that the fault phenomenon belongs to a certain product attribute of equipment;
the result relationship is that a certain device fault of equipment causes a certain attribute fault of a product, or a certain attribute fault of the product causes another product attribute fault, and the product attribute fault exists in an equipment function implementation flow;
the association relation is the correlation between the product fault phenomenon and the fault environment attribute of the debugging system.
8. A fault diagnosis system based on empirical knowledge of tuning, comprising:
the preprocessing module is used for preprocessing debugging fault data;
the diagnosis knowledge model construction module is used for constructing a diagnosis knowledge model;
the similarity matching module is used for designing a similarity matching algorithm and realizing the matching of the target fault phenomenon in a fault experience knowledge base;
and the fault knowledge base perfecting module is used for perfecting the fault knowledge base.
9. The fault diagnosis system based on the empirical knowledge of tuning of claim 8, wherein the diagnosis knowledge model construction module comprises a functional knowledge structure analysis module and a functional fault mapping analysis module;
the functional knowledge structure analysis module is used for carding equipment working function sets, integration extensions and configuration modules through the design principle of aviation comprehensive equipment to form a standardized functional knowledge structure tree;
the functional fault mapping analysis module is used for establishing a logical relation among equipment functional technical index items, fault root causes and fault phenomena based on existing debugging fault experience data.
10. The tuning experience knowledge based fault diagnosis system according to claim 8, wherein the similarity matching module comprises:
the first setting module is used for establishing a function set, an integrated extension set and a configuration module set according to the carded equipment function knowledge structure tree, and setting:
function set f= { F 1 ,f 2 ,f 3 ,…,f n1 }；
Integrated extension set i= { I 1 ,i 2 ,i 3 ,…,i n2 }；
Configuration module set m= { M 1 ,m 2 ,m 3 ,…,m n3 }；
wherein ,fn1 A certain function realized for the aviation comprehensive equipment, n1 is the number of functions, i n2 Integrated extension set for realizing certain function for aviation comprehensive equipment, n2 is the number of extension sets, m n3 Configuration modules required for realizing a certain function for the aviation comprehensive equipment are provided, wherein n3 is the number of modules;
the second setting module is used for establishing a functional technical index set according to the carded equipment functional fault mapping analysis and setting:
technical index set t= { T 1 ,t 2 ,t 3 ,…,t n4 }；
wherein ,tn4 A certain functional technical index item realized for the aviation comprehensive equipment, wherein n4 is the number of the functional technical index items;
the third setting module is used for carrying out word segmentation processing on the text value of the target fault phenomenon description and a certain standard fault phenomenon description of the debugging fault knowledge base through a Chinese word segmentation algorithm of natural language processing to obtain two fault phenomenon word sets, and setting:
word set of target fault phenomena
Standard trouble phenomenon word set
wherein ,
describing word segmentation for the target fault phenomenon, wherein n5 is the number of word segmentation; />
Is a certain standard fault phenomenonDescribing word segmentation, wherein n6 is the number of word segmentation;
a fourth setting module, configured to set:
C 1 ＝F∩W T ，ct 1 for set C 1 The number of elements;
C 2 ＝I∩W T ，ct 2 for set C 2 The number of elements;
C 3 ＝M∩W T ，ct 3 for set C 3 The number of elements;
C 4 ＝T∩W T ，ct 4 for set C 4 The number of elements;
C 5 ＝W S ∩W T ，ct 5 for set C 5 The number of elements;
the target fault similarity calculation module is used for setting the weight as P according to different influence degrees of the functional attribute, the integrated extension, the configuration module and the technical index on fault diagnosis according to comprehensive debugging fault attribute 1 、P 2 、P 3 、P 4 Target fault similarity:
the maximum value searching module is used for repeating the flow from the third setting module to the target fault similarity calculating module aiming at the standard fault record in the adjustment fault knowledge base to obtain a group of target fault similarity
wherein ei And in order to test the number of standard fault records in the fault knowledge base, the standard fault record corresponding to the maximum value in the similarity array is the matching output result. />
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