CN115291589A - D matrix fault diagnosis method and system for ground measurement and control equipment - Google Patents
D matrix fault diagnosis method and system for ground measurement and control equipment Download PDFInfo
- Publication number
- CN115291589A CN115291589A CN202210888791.0A CN202210888791A CN115291589A CN 115291589 A CN115291589 A CN 115291589A CN 202210888791 A CN202210888791 A CN 202210888791A CN 115291589 A CN115291589 A CN 115291589A
- Authority
- CN
- China
- Prior art keywords
- matrix
- fault
- diagnosis
- data
- reasoning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000011159 matrix material Substances 0.000 title claims abstract description 131
- 238000003745 diagnosis Methods 0.000 title claims abstract description 93
- 238000005259 measurement Methods 0.000 title claims abstract description 59
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000000605 extraction Methods 0.000 claims abstract description 21
- 238000012360 testing method Methods 0.000 claims description 58
- 238000012544 monitoring process Methods 0.000 claims description 11
- 238000012216 screening Methods 0.000 claims description 6
- 230000009191 jumping Effects 0.000 claims description 5
- 230000003993 interaction Effects 0.000 claims description 4
- 238000005520 cutting process Methods 0.000 claims description 3
- 230000002950 deficient Effects 0.000 abstract description 5
- 230000007246 mechanism Effects 0.000 description 8
- 230000006870 function Effects 0.000 description 6
- 230000036541 health Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000012423 maintenance Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 238000012790 confirmation Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008439 repair process Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 235000019580 granularity Nutrition 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012774 diagnostic algorithm Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention discloses a D matrix fault diagnosis method and a system of ground measurement and control equipment. The invention realizes two-stage D matrix diagnosis of the ground measurement and control equipment by designing the D matrix aiming at the fault diagnosis of the ground measurement and control equipment, the parameters and the characteristic extraction algorithm of the D matrix can be dynamically configured, the updating operation of the diagnosis knowledge is simple and convenient, and the diagnosis method can be iteratively upgraded in the actual use, thereby solving the technical problems that the fault diagnosis software of the current ground measurement and control equipment is deficient, the fault diagnosis method is not suitable for increasingly tense ground measurement and control resource forms, and the urgent PHM requirements of users cannot be met.
Description
Technical Field
The invention relates to the technical field of fault diagnosis of aerospace ground measurement and control equipment, in particular to a D matrix fault diagnosis method and system of ground measurement and control equipment.
Background
In recent years, along with the development of national force and the enhancement of space consciousness, the in-orbit and planned launching spacecraft of China shows the well-jet type growth, and the ground measurement and control requirements are correspondingly and rapidly increased. In order to meet the huge measurement and control requirements, china is continuously expanding and building ground measurement and control equipment, which brings great challenges to the operation guarantee of the equipment, so that the establishment of an automatic and intelligent central Health Management (PHM) system is urgent. The PHM concept originates from the last century, and comprises a series of sub-concepts such as state monitoring, fault diagnosis (positioning), health assessment, fault prediction, maintenance guarantee and the like, which have been developed greatly in China, and abundant theoretical research results are obtained, but the application mainly focuses on the fields of standardization, systematization and scale equipment such as automobiles, aviation and the like.
In the past, research and development and production of ground measurement and control equipment are developed in the form of scientific research projects, so that equipment models are customized, standardization, systematization and scale are not formed, and health management research and application in related fields are slow to develop. In a strict sense, the existing measurement and control equipment does not introduce a complete PHM concept, only the state monitoring and health evaluation functions are implanted into a monitoring software subsystem of an equipment end, the adopted abnormal state monitoring algorithm and the adopted health evaluation algorithm are simpler, and important functions such as fault diagnosis, fault prediction, maintenance guarantee and the like are lacked, so that the problems of existence and nonexistence of PHM are actually solved, and the practicability is low.
The fault diagnosis is an important ring of the PHM system, and mainly aims to locate a bottom layer fault mode, analyze a fault mechanism, provide treatment suggestions and the like, and provide key input for functions such as false alarm removal, health assessment, fault prediction, maintenance guarantee and the like. Common fault diagnosis methods include an expert knowledge system, machine learning, deep learning and the like, and are successfully applied to PHM systems in different fields. However, the application of these methods in the fault diagnosis of ground measurement and control equipment has not been fully explored: on one hand, due to the lack of a data acquisition, transmission, storage and processing platform, a fault diagnosis model cannot be trained and used by using a machine learning and deep learning method; on the other hand, since the equipment is customized in the form of items, there is a lack of a condition for accumulating expert knowledge from historical data.
In summary, the existing fault diagnosis software of the ground measurement and control equipment is deficient, the fault diagnosis method is not enough to be researched, and the method cannot adapt to increasingly tense ground measurement and control resource forms and cannot meet urgent PHM requirements of users. Therefore, the establishment of a scientific and effective fault diagnosis method for ground measurement and control equipment has great application value.
Disclosure of Invention
The invention mainly aims to provide a D matrix fault diagnosis method and a D matrix fault diagnosis system for ground measurement and control equipment, and aims to solve the technical problems that the existing fault diagnosis software of the ground measurement and control equipment is deficient, the fault diagnosis method is not enough to be suitable for increasingly tense ground measurement and control resource forms, and urgent PHM requirements of users cannot be met.
In order to achieve the above object, the present invention provides a D matrix fault diagnosis method for ground measurement and control equipment, the method comprising the following steps:
s1: determining a subject to be diagnosed; wherein, the object to be diagnosed comprises a system level, a subsystem level, a complete machine level and a unit
S2: acquiring a D matrix of the object to be diagnosed;
s3: determining the starting time and the ending time of the required data, and acquiring the diagnostic data of all the D matrix related measuring points in a given time range;
s4: performing matrix reasoning related to the system and the subsystem D to obtain a complete machine level fault mode;
s5: the user confirms the complete machine level fault mode obtained by automatic reasoning, and manually adds or deletes the complete machine level fault mode on the basis of the reasoning result;
s6: the user judges whether the diagnosis is finished; if yes, skipping S10; if not, executing S7;
s7: screening a complete machine level D matrix associated with the failed complete machine according to the complete machine level failure mode;
s8: executing the whole machine level D matrix reasoning screened out to obtain a unit level fault mode;
s9: the user confirms the unit-level fault mode obtained by automatic reasoning, and manually adds or deletes the unit-level fault mode on the basis of a reasoning result;
s10: and generating a diagnosis conclusion according to the finally obtained fault mode.
Optionally, the D matrix includes a test group, a failure mode group, and a relationship matrix.
Optionally, the failure mode group includes a plurality of failure modes, and the test group includes a plurality of tests.
Optionally, each of the tests characterizes a condition for a certain failure mode in the set of failure modes to occur.
Optionally, values of elements in the relationship matrix are boolean features, where the boolean features include pass, fail, and don't care.
Optionally, each row of the relationship matrix encodes a correspondence between a test result and an occurrence of a failure mode: when each non-independent bit in a row of the dependency matrix is equal to the corresponding test result, the failure mode corresponding to that row occurs.
Optionally, the D matrix inference is executed, which specifically includes the following steps:
s11: inputting time sequence data and time slice length; wherein the time slice length is a diagnostic configuration and is associated with the D matrix;
s12: according to the time slice length, sequentially cutting the time sequence data into data segments, and regarding the tail residual data as a segment if the tail residual data is less than one segment;
s13: traversing the data segments;
s14: traversing and testing;
s15: according to the monitoring points of the test, taking out data corresponding to the testing monitoring points from the data segments;
s16: calling a feature extraction algorithm to extract Boolean features of the measuring points;
s17: calling a logic operation module, and obtaining a current test conclusion according to the Boolean characteristics and the logic expression;
s18: is all tests performed complete? If yes, executing the next step; if not, jumping to S14.
S19: matching the test result with the row of the relation matrix, and if the passing Boolean eigenvalue and the failing Boolean eigenvalue of the relation matrix are equal to the output of the corresponding test, generating a fault mode corresponding to the row;
s20: putting the reasoning result into a fault mode set; the fault mode in the fault mode set is output of matrix reasoning;
s21: is all pieces of data done reasoning? If yes, executing the next step; if not, skipping S13;
s22: and outputting all the failure modes obtained by inference.
In addition, in order to achieve the above object, the present invention further provides a D-matrix fault diagnosis system for ground measurement and control equipment, which is applied to the D-matrix fault diagnosis method for ground measurement and control equipment, and the system includes:
the data interaction module is used for inquiring the D matrix configuration, the diagnosis data and the fault mode;
the characteristic extraction module is used for analyzing a characteristic extraction algorithm and extracting data characteristics;
the logic operation module is used for analyzing the logic expression and executing logic operation;
the logical reasoning module is used for feature extraction, logical operation and fault mode acquisition;
and the diagnosis flow module is used for selecting a diagnosis object, acquiring matrix configuration, acquiring diagnosis data, executing matrix reasoning, confirming a fault mode and generating a diagnosis conclusion.
The method comprises the steps of selecting a diagnosis object, obtaining a D matrix, obtaining diagnosis data, executing all systems and a system level D matrix, confirming a whole machine level fault mode, screening a whole machine level D matrix, executing the screened whole machine level D matrix, confirming a unit level fault mode and generating a diagnosis conclusion. The invention realizes two-stage D matrix diagnosis of the ground measurement and control equipment by designing the D matrix aiming at the fault diagnosis of the ground measurement and control equipment, the parameters and the characteristic extraction algorithm of the D matrix can be dynamically configured, the updating operation of the diagnosis knowledge is simple and convenient, and the diagnosis method can be iteratively upgraded in the actual use, thereby solving the technical problems that the fault diagnosis software of the current ground measurement and control equipment is deficient, the fault diagnosis method is not suitable for increasingly tense ground measurement and control resource forms, and the urgent PHM requirements of users cannot be met.
Drawings
FIG. 1 is a schematic diagram of implementation steps of a D matrix diagnosis method for ground measurement and control equipment;
FIG. 2 is a schematic diagram of a D matrix reasoning process for ground measurement and control equipment diagnosis;
FIG. 3 is a schematic diagram of a D matrix diagnosis process of the ground measurement and control equipment;
FIG. 4 is a schematic diagram of a diagnostic D matrix structure of the ground measurement and control equipment;
FIG. 5 is a schematic diagram of a functional module of a D matrix diagnostic system of the ground measurement and control equipment.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
At present, in the related technical field, the existing fault diagnosis software of the ground measurement and control equipment has defects, the fault diagnosis method is not enough to be researched, and the method cannot adapt to increasingly tense ground measurement and control resource forms and cannot meet the urgent PHM requirements of users.
In order to solve the problem, the invention provides various embodiments of the D matrix fault diagnosis method and system of the ground measurement and control equipment. The D matrix fault diagnosis method and the system of the ground measurement and control equipment provided by the invention realize two-stage D matrix diagnosis of the ground measurement and control equipment by designing the D matrix aiming at the fault diagnosis of the ground measurement and control equipment, the parameters and the feature extraction algorithm of the D matrix can be dynamically configured, the updating operation of the diagnosis knowledge is simple and convenient, and the diagnosis method can be iteratively upgraded in the actual use, thereby solving the technical problems that the fault diagnosis software of the current ground measurement and control equipment is deficient, the fault diagnosis method is insufficient to be researched, the increasingly tense ground measurement and control resource form cannot be adapted, and the urgent PHM requirements of users cannot be met.
The embodiment of the invention provides a D matrix fault diagnosis method of ground measurement and control equipment, which comprises the following steps: selecting a diagnosis object, obtaining a D matrix, obtaining diagnosis data, executing all systems and a system-level D matrix, confirming a whole machine level fault mode, screening the whole machine level D matrix, executing the screened whole machine level D matrix, confirming a unit level fault mode and generating a diagnosis conclusion.
It should be noted that the embodiment provides a D-matrix fault diagnosis method for ground measurement and control equipment, which is suitable for unit-level fault location and mechanism analysis, and provides a disposal suggestion according to a fault mode.
The D matrix for ground measurement and control equipment fault diagnosis comprises a test group, a fault mode group and a relation matrix. The single test in the test group should have definite physical meaning and good interpretability, and the table indicates a condition of some faults in the fault mode group, and the test indicates the event occurrence; the faults obtained by the D matrix reasoning can be selected only in the fault mode group or have no fault; the relationship matrix encodes a correspondence between test results of the test group and faults in the fault group.
The D matrix test consists of input, feature extraction method and test logic. The input of the D matrix test is monitoring point time sequence data which is directly reported by an equipment end; the feature extraction method converts time sequence data into a Boolean value as the input of test logic; the test logic is a logical operation of different input characteristic values, and the operation result is 1 (test passed) or 0 (test failed).
The failure mode needs to contain fields such as failure mode name, equipment node, mechanism analysis, influence domain, treatment suggestion, etc.: the failure mode name briefly describes the nature of the failure; the equipment node indicates the equipment node to which the fault belongs; the mechanism analysis explains the mechanism of the fault occurrence; the influence domain explains the influence of the fault on equipment functions, task capacity and the like; the handling recommendation gives a recommendation for subsequent failure handling. The failure mode name and the equipment node are necessary fields, mechanism analysis, influence fields and treatment suggestions should be filled as much as possible, and after the failure is located, effective suggestions can be provided for non-professionals according to information preset in the failure mode.
The elements in the D matrix relation matrix are all three-valued, and the values can be ' pass ', ' fail ' and ' don't care '. The independence in the relational matrix means that a test does not affect the occurrence of a certain failure mode, whether it passes or not. Each row of the relationship matrix encodes the correspondence of the test result to the occurrence of one failure mode: when each non-null bit in a row of the relationship matrix is equal to the corresponding test result, the failure mode corresponding to that row occurs.
The ground measurement and control equipment is abstracted into four layers which are respectively a system, a subsystem, a complete machine and a unit, wherein the unit is the minimum granularity of fault location. The two-stage D-matrix diagnostic protocol defines D-matrices of two granularities: the fault mode of the system and subsystem correlation D matrix is positioned to the whole machine; and the fault mode of the complete machine correlation D matrix is positioned to the unit. During diagnosis, firstly, the system and subsystem D matrix is used for positioning the fault to the whole machine, and then the whole machine D matrix is used for positioning the fault to the unit. The two-phase D matrix diagnostic scheme has three advantages: firstly, the D matrix structure is simplified, so that the matrix scale is not too large; secondly, the information density of the matrix is increased so as to avoid more irrelevant relation matrixes; thirdly, the matrix parameters are convenient to design, and the accuracy of the matrix is improved.
In order to explain the present application more clearly, specific examples of the present application are provided below.
Fig. 1 shows a principle process of D-matrix diagnosis of the ground measurement and control device.
Step 1: a diagnostic object is obtained. The diagnostic object is a set of ground measurement and control equipment system, and the fault location is started from the system and gradually goes to the unit location.
And 2, step: and acquiring a D matrix. And inquiring and acquiring the D matrix associated with each level of equipment node according to the diagnosis object.
And step 3: diagnostic data is acquired. Firstly, determining the starting time and the ending time of the needed data, and then acquiring the data of all the relevant measuring points of the D matrix in a given time range.
And 4, step 4: and executing the system and subsystem level D matrix. And executing all D matrix reasoning related to the system and the subsystems to acquire a complete machine level fault mode.
And 5: and confirming a complete machine level fault mode. And the user confirms the complete machine level fault mode obtained by automatic reasoning and allows the complete machine level fault mode to be manually added or deleted on the basis of the reasoning result.
And 6: the user determines whether the diagnosis is finished. If yes, jumping to step 10; if not, executing the next step.
And 7: and screening the complete machine level D matrix. And screening out the complete machine level D matrix associated with the failed complete machine according to the complete machine level failure mode.
And 8: and executing the screened complete machine level D matrix. And executing all screened complete machine level D matrixes to obtain unit level fault modes.
And step 9: cell level failure modes are confirmed. The user confirms the unit-level failure modes obtained by automatic reasoning and allows the unit-level failure modes to be manually added or deleted on the basis of the reasoning results.
Step 10: a diagnostic decision is generated. And generating a diagnosis conclusion according to the finally obtained fault mode.
When D matrix inference is performed, as shown in fig. 2, the method includes the following steps:
step 1: time sequence data and time slice length are input. The time slice length is a diagnostic configuration and is associated with the D matrix.
And 2, step: the data is sliced into fragments. And according to the time slice length, sequentially cutting the time sequence data into data segments, and regarding the tail residual data as a segment if the tail residual data is less than one segment.
And step 3: the data segments are traversed.
And 4, step 4: and traversing the test.
And 5: and acquiring data of the monitoring point. And according to the monitoring points to be tested, taking out the data corresponding to the monitoring points to be tested from the data segments.
Step 6: and (5) extracting features. And calling a feature extraction algorithm to extract Boolean features of the measuring points.
And 7: the test logic is executed. And calling a logic operation module, and obtaining a current test conclusion according to the Boolean characteristics and the logic expression.
And 8: is all tests performed complete? If yes, executing the next step; if not, skipping to the step 4.
And step 9: matrix reasoning is performed. And matching the test result with the row of the relation matrix, and if the non-null values of the latter are equal to the output of the corresponding test, generating the fault mode corresponding to the row. All failure modes matching the test results are legal.
Step 10: and putting the reasoning result into a fault mode set. The failure modes in the set of failure modes here are the output of matrix reasoning.
Step 11: is all pieces of data done reasoning? If yes, executing the next step; if not, skipping to the step 3.
Step 12: and outputting the failure mode set. And outputting all the failure modes obtained by inference.
Fig. 3 shows the implementation steps of the D matrix fault diagnosis method of the ground measurement and control equipment of the present invention:
firstly, the D matrix fault diagnosis method provides auxiliary materials for configuring diagnosis parameters, and auxiliary equipment experts fill in the configuration required by the diagnosis method, wherein the configuration comprises a D matrix parameter template, a test parameter template, a fault mode parameter template and a diagnosis algorithm template.
Then, a plurality of experts are requested to independently complete the filling of the configuration parameters required by the diagnosis method according to the auxiliary materials, and the configuration parameters are arranged, induced and corrected by experts and then are submitted to each expert for confirmation and adoption.
Then, all configuration parameters are filled in the database, including D matrix parameters, test parameters, failure mode parameters, and diagnostic algorithms.
Secondly, after the diagnosis condition is started, a diagnosis process is initiated manually, and the fault of the ground measurement and control equipment is positioned and a diagnosis conclusion is generated.
And then, periodically evaluating configuration parameters of the current D matrix diagnosis method by a user according to the fault diagnosis effect, and proposing improvement suggestions.
Finally, experts discuss rationality of parameter improvement through meetings, if improvement is needed, modification opinions of the configuration parameters are given at the same time, and users or software maintenance personnel finish diagnosis of configuration parameter modification.
Note that, as shown in fig. 4, the D matrix structure constitutes a component test, a failure mode, and a relationship matrix.
The test comprises two steps of feature extraction and logic operation. The feature extraction algorithm is a configuration of test correlation in the present invention, and is used for extracting boolean features in time series data. Logical operations refer to mixed operations of basic operations such as and, or, not, exclusive or, and voting, expressed in character strings in configuration, and supporting priorities expressed in small brackets. The test results are indicated by "1" for "pass" and "0" for "fail".
Mechanistic analysis, impact domains and treatment suggestions in the failure mode are not mandatory fields, but key information such as failure occurrence principles, failure consequences and treatment methods can be provided for laymen. The mechanism analysis is that the expert analyzes the root cause of the fault phenomenon from the physical and chemical mechanisms of the bottom layer, so that the non-professional can comprehensively know the fault. The influence domain is the influence range of the fault given by the expert and the user from the equipment function angle and the task capability angle respectively, and specifically comprises equipment function loss, index performance reduction, task coverage range reduction and the like. The treatment proposal mainly comprises two parts of a failure site confirmation method and a maintenance proposal: the fault site confirmation method is a test or observation means for finally confirming the fault by a user on an equipment site; the repair recommendations are based on the type of failure, severity, and user equipment repair capabilities, giving specifications for repair or disposal.
The values of the relationship matrix are three: 1 (pass), 0 (fail), and null (don't care). And when the non-null bit of the row where the fault mode is located in the relation matrix is equal to the corresponding test output after all tests are finished, the fault mode occurs. Note that null exists so that one matrix inference can output multiple failure modes, and all are valid.
As shown in fig. 5, the present application further provides a D matrix diagnostic system for ground measurement and control equipment, which includes five modules, namely a data interaction module, a feature extraction module, a logic operation module, a matrix inference module, and a diagnostic flow module. The data interaction module is mainly used for inquiring D matrix configuration, diagnosis data and fault modes; the feature extraction module mainly analyzes a feature extraction algorithm and extracts data features; the logic operation module mainly analyzes the logic expression and executes logic operation; the matrix reasoning module completes the processes of feature extraction, logic operation, failure mode acquisition and the like; the diagnosis flow module mainly comprises flows of selecting a diagnosis object, obtaining matrix configuration, obtaining diagnosis data, executing matrix reasoning, confirming a fault mode, generating a diagnosis conclusion and the like.
In the embodiment, a two-stage D matrix diagnosis of the ground measurement and control equipment is realized by designing a D matrix for the ground measurement and control equipment fault diagnosis, the parameters and the feature extraction algorithm of the D matrix can be dynamically configured, the updating operation of the diagnosis knowledge is simple and convenient, and the diagnosis method can be iteratively upgraded in practical use, so that the technical problems that the existing fault diagnosis software of the ground measurement and control equipment is lacked, the fault diagnosis method is insufficient in research, the existing increasingly tense ground measurement and control resource form cannot be adapted, and the urgent PHM requirements of users cannot be met are solved.
The foregoing is a detailed description of the invention with reference to specific preferred embodiments, and no attempt is made to limit the invention to the particular embodiments disclosed, or modifications and equivalents thereof, since those skilled in the art will recognize that various changes may be made without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A D matrix fault diagnosis method of ground measurement and control equipment is characterized by comprising the following steps:
s1: determining a subject to be diagnosed; wherein, the object to be diagnosed comprises a system level, a subsystem level, a complete machine level and a unit
S2: acquiring a D matrix of the object to be diagnosed;
s3: determining the starting time and the ending time of the required data, and acquiring the diagnostic data of all the D matrix related measuring points in a given time range;
s4: performing matrix reasoning related to the system and the subsystem D to obtain a complete machine level fault mode;
s5: the user confirms the complete machine level fault mode obtained by automatic reasoning, and manually adds or deletes the complete machine level fault mode on the basis of the reasoning result;
s6: the user judges whether the diagnosis is finished; if yes, jumping to S10; if not, executing S7;
s7: screening a complete machine level D matrix associated with the complete machine with the fault according to the complete machine level fault mode;
s8: executing the whole machine level D matrix reasoning screened out to obtain a unit level fault mode;
s9: the user confirms the unit-level fault mode obtained by automatic reasoning, and manually adds or deletes the unit-level fault mode on the basis of a reasoning result;
s10: and generating a diagnosis conclusion according to the finally obtained fault mode.
2. The method for diagnosing the fault of the D matrix of the ground measurement and control equipment as recited in claim 1, wherein the D matrix comprises a test group, a fault mode group and a relationship matrix.
3. The method of claim 2, wherein the set of failure modes includes a plurality of failure modes, and the set of tests includes a plurality of tests.
4. The method of claim 3, wherein each of the tests characterizes a condition of a failure mode of the set of failure modes.
5. The method according to claim 2, wherein values of elements in the relationship matrix are boolean features, and the boolean features include pass, fail and don't care.
6. The method according to claim 5, wherein each row of the relationship matrix encodes a correspondence between the test result and a failure mode occurrence: when each non-independent bit in a row of the dependency matrix is equal to the corresponding test result, the failure mode corresponding to that row occurs.
7. The method for diagnosing the D matrix fault of the ground measurement and control equipment as claimed in claim 4, wherein the D matrix reasoning is executed, and the method specifically comprises the following steps:
s11: inputting time sequence data and time slice length; wherein the time slice length is a diagnostic configuration and is associated with the D matrix;
s12: according to the time slice length, sequentially cutting the time sequence data into data segments, and regarding the tail residual data as a segment if the tail residual data is less than one segment;
s13: traversing the data segments;
s14: traversing and testing;
s15: according to the monitoring points of the test, taking out data corresponding to the testing monitoring points from the data segments;
s16: calling a feature extraction algorithm to extract Boolean features of the measuring points;
s17: calling a logic operation module, and obtaining a current test conclusion according to the Boolean characteristics and the logic expression;
s18: is all tests performed complete? If yes, executing the next step; if not, jumping to S14.
S19: matching the test result with the row of the relation matrix, and if the passing Boolean eigenvalue and the failing Boolean eigenvalue of the relation matrix are equal to the output of the corresponding test, generating a fault mode corresponding to the row;
s20: putting the reasoning result into a fault mode set; the fault mode in the fault mode set is the output of matrix reasoning;
s21: is all pieces of data done reasoning? If yes, executing the next step; if not, jumping to S13;
s22: and outputting all failure modes obtained by inference.
8. A D-matrix fault diagnosis system of ground measurement and control equipment, which is applied to the D-matrix fault diagnosis method of the ground measurement and control equipment according to any one of claims 1 to 7, and comprises:
the data interaction module is used for inquiring the D matrix configuration, the diagnosis data and the fault mode;
the characteristic extraction module is used for analyzing a characteristic extraction algorithm and extracting data characteristics;
the logic operation module is used for analyzing the logic expression and executing logic operation;
the logical reasoning module is used for feature extraction, logical operation and fault mode acquisition;
and the diagnosis flow module is used for selecting a diagnosis object, acquiring matrix configuration, acquiring diagnosis data, executing matrix reasoning, confirming a fault mode and generating a diagnosis conclusion.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210888791.0A CN115291589A (en) | 2022-07-26 | 2022-07-26 | D matrix fault diagnosis method and system for ground measurement and control equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210888791.0A CN115291589A (en) | 2022-07-26 | 2022-07-26 | D matrix fault diagnosis method and system for ground measurement and control equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115291589A true CN115291589A (en) | 2022-11-04 |
Family
ID=83823946
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210888791.0A Pending CN115291589A (en) | 2022-07-26 | 2022-07-26 | D matrix fault diagnosis method and system for ground measurement and control equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115291589A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104504248A (en) * | 2014-12-05 | 2015-04-08 | 中国航空工业集团公司第六三一研究所 | Failure diagnosis modeling method based on designing data analysis |
| CN104597892A (en) * | 2014-12-16 | 2015-05-06 | 北京航天测控技术有限公司 | Hierarchical fault diagnosis method for electronic information equipment |
| CN107918358A (en) * | 2017-11-17 | 2018-04-17 | 山东师范大学 | Numerical control equipment failure analysis methods and device |
| CN110162811A (en) * | 2018-04-16 | 2019-08-23 | 北京机电工程研究所 | Testability modeling and analysis method based on fault diagnosis |
| CN110309612A (en) * | 2019-07-08 | 2019-10-08 | 西安交通大学 | Dynamical system fault handling method based on fuzzy fault Petri network |
| CN111784207A (en) * | 2020-07-30 | 2020-10-16 | 中国电子科技集团公司第十四研究所 | Open type radar health management system |
| CN113626267A (en) * | 2021-07-31 | 2021-11-09 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Method for evaluating uncertainty fault diagnosis efficiency of complex electronic system |
-
2022
- 2022-07-26 CN CN202210888791.0A patent/CN115291589A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104504248A (en) * | 2014-12-05 | 2015-04-08 | 中国航空工业集团公司第六三一研究所 | Failure diagnosis modeling method based on designing data analysis |
| CN104597892A (en) * | 2014-12-16 | 2015-05-06 | 北京航天测控技术有限公司 | Hierarchical fault diagnosis method for electronic information equipment |
| CN107918358A (en) * | 2017-11-17 | 2018-04-17 | 山东师范大学 | Numerical control equipment failure analysis methods and device |
| CN110162811A (en) * | 2018-04-16 | 2019-08-23 | 北京机电工程研究所 | Testability modeling and analysis method based on fault diagnosis |
| CN110309612A (en) * | 2019-07-08 | 2019-10-08 | 西安交通大学 | Dynamical system fault handling method based on fuzzy fault Petri network |
| CN111784207A (en) * | 2020-07-30 | 2020-10-16 | 中国电子科技集团公司第十四研究所 | Open type radar health management system |
| CN113626267A (en) * | 2021-07-31 | 2021-11-09 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Method for evaluating uncertainty fault diagnosis efficiency of complex electronic system |
Non-Patent Citations (3)
| Title |
|---|
| 张贤志;杨红梅;盛文;张磊;: "基于CAN总线的雷达装备BIT故障诊断专家系统研究", 微型机与应用, no. 03, 10 February 2011 (2011-02-10), pages 58 - 60 * |
| 王晗中;杨江平;张爱元;: "现代雷达装备综合智能故障诊断系统设计", 现代雷达, no. 11, 15 November 2008 (2008-11-15), pages 18 - 21 * |
| 田恒;段富海;樊亮;桑勇;史萌;: "基于测试性D矩阵的多故障诊断与维修策略", 北京航空航天大学学报, no. 04, 30 June 2017 (2017-06-30), pages 802 - 809 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021179574A1 (en) | Root cause localization method, device, computer apparatus, and storage medium | |
| US5500941A (en) | Optimum functional test method to determine the quality of a software system embedded in a large electronic system | |
| CN110174883B (en) | System health state assessment method and device | |
| CN112487592B (en) | Bayesian network-based task reliability modeling analysis method | |
| CN111061293B (en) | Multi-parameter coupled aircraft fault positioning method, aircraft and storage medium | |
| CN102945204B (en) | Operating system standard conformance test adequacy evaluation method based on interface model | |
| CN116771576B (en) | Comprehensive fault diagnosis method for hydroelectric generating set | |
| CN104268375A (en) | Ship electric power station fault diagnosing method based on knowledge petri network | |
| CN116467674B (en) | Intelligent fault processing fusion updating system and method for power distribution network | |
| CN112084220B (en) | Abnormality diagnosis method and device for electric energy metering device and readable storage medium | |
| CN113626267A (en) | Method for evaluating uncertainty fault diagnosis efficiency of complex electronic system | |
| CN108825482B (en) | Fault detection method and system for axial plunger pump of airplane | |
| CN115291589A (en) | D matrix fault diagnosis method and system for ground measurement and control equipment | |
| ITTO20090562A1 (en) | PROCEDURE FOR THE GENERATION OF A SET OF CONFLICTS FOR THE MODEL-BASED DIAGNOSIS OF SYSTEMS, AND CORRESPONDING DIAGNOSIS PROCEDURE | |
| CN115600695A (en) | Fault diagnosis method of metering equipment | |
| CN112633745B (en) | Reliability attribute expression method of product | |
| Amati et al. | An incremental approach to functional diagnosis | |
| Strasser et al. | An empirical evaluation of Bayesian networks derived from fault trees | |
| CN115309576A (en) | Knowledge rule fault diagnosis method and system for ground measurement and control equipment | |
| CN112631238A (en) | Embedded diagnosis method for complex electronic system | |
| Hu et al. | Research on application of equipment fault diagnosis technology based on FTA | |
| Przytula et al. | Development of Bayesian diagnostic models using troubleshooting flow diagrams | |
| Sheppard et al. | Managing conflict in system diagnosis | |
| Musa et al. | Application of basic and logarithmic Poisson execution time models in software reliability measurement | |
| US20240273412A1 (en) | Unlearnable tasks in machine learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |