CN115994292A - Health evaluation device for electronic equipment - Google Patents
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Abstract
The invention relates to the technical field of aircraft health management, and discloses an electronic equipment health assessment device which comprises a component-level health assessment module and a system-level health assessment module, wherein the component-level health assessment module comprises a fuzzy severity level calculation module and a component health calculation module, and the system-level health assessment module comprises a component weight self-adaptive construction module and a system health calculation module; according to the invention, the health assessment of the electronic equipment is realized under the condition of lack of state monitoring parameters, the health state of the component is dynamically assessed based on the component fault risk and the built-in self-test information, the health state of the system is dynamically assessed based on the reliability block diagram of the system and the self-adaptive weight of the component, the health management capability of the aircraft is improved, and the autonomous health management of the aircraft is facilitated.
Description
Technical Field
The invention relates to the technical field of aircraft health management, in particular to a health assessment device for electronic equipment.
Background
In the field of aircraft health assessment, the health status of a subject is typically assessed based on real-time performance parameters. With the development of digitalization of aircrafts, electronic devices are increasingly applied to aircrafts, and the importance of health evaluation of electronic devices is increasingly highlighted. Currently, aircraft on-board electronics rely on-board self-test diagnostics, lacking real-time performance parameters for health assessment, making health assessment for aircraft-oriented electronics difficult to achieve. In addition, most aircraft health assessment methods do not distinguish between the logical structures of the systems, and there is little distinction between the system-level and component-level health assessment methods, in summary, the system-level health assessment does not embody the serial-parallel logical structures of the systems. In order to realize health assessment of electronic equipment, it is needed to assess the health state of a component in the absence of performance parameters, consider the serial-parallel logic structure of a system on the basis, and perform system-level health assessment on the electronic equipment, so as to improve the health assessment capability of the electronic equipment.
Disclosure of Invention
The invention aims at solving the problem of electronic system health assessment based on fault risks, thereby supporting the health management of an aircraft.
The invention aims at realizing the following technical scheme:
an electronic device health assessment apparatus includes a component-level health assessment module and a system-level health assessment module;
the component level health evaluation module comprises a fuzzy severity level calculation module and a component health level calculation module, wherein the fuzzy severity level calculation module is used for defuzzifying fuzzy membership of the component severity level and obtaining the fuzzy severity level of the component; the component health degree calculation module is used for analyzing the reliability degree, the fuzzy severity level and the built-in self test of the component and evaluating the health degree of the component based on the fault risk of the component;
the system-level health evaluation module comprises a component weight self-adaptive construction module and a system health degree calculation module, wherein the component weight self-adaptive construction module constructs a relative importance judgment matrix based on the fuzzy severity level of the component, and adopts a self-adaptive adjustment algorithm to adjust the relative importance judgment matrix so that the relative importance judgment matrix and the weight vector of the component pass consistency test; the system health degree calculation module firstly analyzes a reliability block diagram of the electronic equipment according to the component health degree and weight vectors of components passing consistency test in the system, so as to obtain logic results of the systems, and further selects a calculation mode of the system health degree according to the serial-parallel connection relationship to obtain system health assessment.
Preferably, the fuzzy severity level calculation module analyzes the component using fault type impact and deadline analysis, constructs a severity level fuzzy membership of the component: after determining the severity level set V, an evaluation group is established to evaluate the severity of each level of each component, obtain an evaluation of each severity level of each component, and count the number of supporters x of each severity level of each component ij From this, the degree of membership b is obtained ij ＝x ij /x i Wherein x is i Indicating the number of people evaluating the ith part; finally, calculating a fuzzy membership vector B of the i-th component severity level;
after the component severity level fuzzy membership vector B is constructed, calculating the component fuzzy severity level S by adopting a gravity center method according to the severity level set V, and carrying out normalization processing, as shown in a formula (1):
S＝VB T /maxV (1)。
preferably, the component health calculation module analyzes the failure rate h and the initial reliability R of the processing component 0 Based on the reliability analysis, the failure occurrence probability OPR of the component is calculated, and the component failure risk RNK is calculated by combining the fuzzy severity levels S and the adjustment coefficient K of S of the component calculated by the fuzzy severity level calculation module, as shown in the formula (2):
calculating the health degree of the component based on the fault risk of the component, selecting a calculation mode of the health degree of the component according to an built-in self-test diagnosis report of the component, and finally calculating the health degree of the component, wherein the calculation mode is shown in a formula (3):
wherein BIT of 0 indicates no component failure and BIT of 1 indicates component failure.
Preferably, the component weight self-adaptive construction module comprises a relative importance judgment matrix module, a component weight calculation module, a consistency index inspection module and a judgment matrix self-adaptive adjustment module;
the relative importance judging matrix module calculates a relative importance judging matrix R of the components according to the fuzzy severity grade S of each component in the system, adjusts the relative importance matrix according to the range of the nine-scale judging scale, and the adjusting coefficient is L, wherein the relative importance judging matrix of the system is shown in a formula (4):
R←[S T S] L (4)
the component weight calculation module calculates weight vectors of the components according to the relative importance judgment matrix R: first to oppositeThe importance judgment matrix R is summed in row and column directions to obtain a column sum vector CS R Further, the relative importance judgment matrix is normalized in the row direction, and the relative importance judgment matrix R, the row and the vector CS R Dividing the number to obtain a normalized judgment matrix R C ＝R/CS R Then, for the normalized judgment matrix R C The row summation processing is adopted to obtain a row summation vector RS R For normalized judgment matrix R C Summing all elements to obtain element and TS R Further, a weight vector W of each component in the system is calculated as shown in a formula (5):
W＝RS R /TS R (5)
the consistency index checking module performs consistency check on the relative importance judgment matrix calculated by the relative importance judgment matrix module and the component weight calculation module and the weight vector of the component: firstly, calculating the point multiplication of a relative importance judgment matrix R and a weight vector W, then dividing the point multiplication by the weight vector W to obtain a vector lambda, and averaging the vector lambda to obtain a maximum characteristic root lambda max Then, according to the number n of the components in the system, calculating a consistency index CI, according to the random consistency index RI corresponding to the number n of the components, calculating a consistency ratio CR, and finally, if CR<And 0.1, if the consistency test is passed, outputting a weight vector of the component to a system health degree calculation module, otherwise, if the consistency test is not passed, starting a judgment matrix self-adaptive adjustment module, and calculating the correlation as shown in a formula (6):
the judgment matrix self-adaptive adjustment module carries out self-adaptive adjustment on the relative importance judgment matrix, firstly initializes an adjustment coefficient d, adjusts the upper right corner elements of the relative importance judgment matrix one by one, and adjusts the elements r=r d Assigning r=1/R to the element symmetric along the main diagonal, and obtaining an adjusted relative importance judgment matrix R++R according to the adjusted element r The relative importance judgment matrix module and the component weight calculation module recalculate to obtain adjustmentAnd if the consistency test is passed, outputting the weight of the component to a system health degree calculation module, otherwise, judging that the consistency test is not passed, and continuing to adjust by a matrix self-adaptive adjustment module.
Preferably, the system health degree calculating module inputs the component health degree HI completed by the component health degree calculating module and the weight vector W of the components in the system completed by the component weight self-adaptive constructing module, calculates the health degree of the system according to the reliability block diagram of the system and the logic structure layering level of the reliability block diagram, as shown in a formula (7):
the invention has the beneficial effects that:
under the condition of no performance parameters, the health assessment of the electronic equipment is realized, a new component health assessment method is provided, namely, the component health assessment is realized based on the fault risk, meanwhile, the method is different from other system health assessment methods, a system health calculation mode is selected based on a reliability block diagram or a logic structure of the system, the weight dilution problem is avoided, the system-level health assessment is realized, and the analysis type calculation method is adopted for the health assessment based on the fault risk, so that the calculation speed is high, and the method can be applied to the health assessment of a ground system and the health assessment of an airborne system.
Drawings
FIG. 1 is a block diagram of a system health assessment device based on risk of failure;
FIG. 2 is a block diagram of the reliability of an avionics system;
FIG. 3 is a framework diagram of a system health assessment algorithm based on risk of failure;
FIG. 4 is a graph of component health versus probability of failure occurrence, and fuzzy severity level;
FIG. 5 is a statistical diagram of a health class of an avionics system;
fig. 6 is a health profile of an avionics system.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples.
Referring to fig. 1, an electronic device health assessment apparatus shown in this embodiment includes a component-level health assessment module and a system-level health assessment module;
the component level health evaluation module comprises a fuzzy severity level calculation module and a component health level calculation module, wherein the fuzzy severity level calculation module is used for defuzzifying fuzzy membership of the component severity level and obtaining the fuzzy severity level of the component; the component health degree calculation module is used for analyzing information such as reliability, fuzzy severity level, built-in self test and the like of the component and evaluating the health degree of the component based on the fault risk of the component;
the system-level health evaluation module comprises a component weight self-adaptive construction module and a system health degree calculation module, wherein the component weight self-adaptive construction module constructs a relative importance judgment matrix based on the fuzzy severity level of the component, and adopts a self-adaptive adjustment algorithm to adjust the relative importance judgment matrix so that the relative importance judgment matrix and the weight vector of the component pass consistency test; the system health degree calculation module firstly analyzes a reliability block diagram of the electronic equipment according to the component health degree and weight vectors of components passing consistency test in the system, so as to obtain logic results of each system, and further selects a calculation mode of the system health degree according to a serial-parallel connection relation, and performs index calculation processing on the health degree of the components to avoid weight dilution, thereby realizing system health assessment based on the reliability block diagram.
According to the embodiment, under the condition that the electronic equipment lacks performance parameters, the health assessment of the component level can be realized based on the fault risk, and the health assessment of the system level can be realized based on the reliability block diagram, so that the health assessment capability of the electronic equipment is improved. An example of some electronic device will be described below.
The system structure of some electronic equipment is abstracted, and the reliability analysis is performed on the system structure, so as to obtain a reliability block diagram of the electronic equipment, and as shown in fig. 2, the whole electronic equipment is formed by connecting 4 subsystems in series and parallel, and the total number of the electronic equipment is 20. Wherein, the severity level fuzzy membership of part of the components, the built-in self-test diagnosis report, the failure rate and the initial reliability are shown in tables 1, 2 and 3. In the table, PRODUCTION_NUMBER represents the unique identification of the component, LEVEL represents the LEVEL of severity (1-5), RUNNING_TIME represents the runtime of the component, BIT represents the current built-in self-test results of the component (0 and 1 correspond to no fault and faulty, respectively), INITIAL_RELIABILITY represents the INITIAL RELIABILITY, and FAILURE_RATE represents the FAILURE RATE (NUMBER of FAILUREs per unit TIME). The severity rating, failure rate, and the like for the remaining components (components 6-20) were the same as for the initial reliability and components 1-5, and assuming the equipment was the new system that was just put into service, the run times of components 6-20 were consistent with those of components 1-5, and no failure had occurred so far, i.e., BIT results were all 0.
TABLE 1 part severity level fuzzy membership
Table 2 in-flight self-test
TABLE 3 failure rate and initial reliability of the parts
In the fuzzy severity level calculation module, a component is constructed using fault type impact and deadline analysis (FMECA) analysis components, the severity level fuzzy membership of the component.
In this embodiment, the severity level is classified into 1 to 5 levels, and a higher severity level fuzzy membership meansThe greater the degree of membership to the severity level. After determining the severity level set V, an evaluation group is established to evaluate the severity of each level of each component to obtain an evaluation of each severity level of each component, as shown in table 1, and the fuzzy severity level calculation module counts the number of supporters x for each severity level according to table 1 ij (x ij A number of persons indicating that the severity level of the ith component is the jth level), from which the membership level b is obtained ij ＝x ij /x i (degree of membership b) ij A degree to which a rating indicating the severity level of the ith component is the jth level is subordinate to the jth level), wherein x is i Indicating the number of people evaluating the ith part; finally, a fuzzy membership vector B of the i-th component severity level is calculated, wherein b= [ B ] i1 ,b i2 ,b i3 ,b i4 ,b i5 ]。
After the component severity membership vector B is constructed, calculating the component fuzzy severity level S by adopting a gravity center method according to the severity level set V, and carrying out normalization processing, as shown in formula (1):
S＝VB T /maxV (1)
the component health calculation module analyzes and processes the failure rate h and the initial reliability R of the components in Table 3 0 The failure occurrence probability OPR of the component is calculated from the reliability analysis, and the failure risk RNK of the component is calculated by combining the fuzzy severity levels S of the component calculated by the fuzzy severity level calculation module and the adjustment coefficient k=0.5 of S, as shown in the formula (2):
calculating the health degree of the component based on the failure risk of the component, selecting a calculation mode of the health degree of the component according to a built-in self test (BIT) diagnosis report of the component in table 2 (BIT is 0 to indicate no failure of the component and 1 to indicate failure of the component), and finally calculating the health degree of the component as shown in a formula (3):
the component weight self-adaptive construction module comprises a relative importance judgment matrix module, a component weight calculation module, a consistency index inspection module and a judgment matrix self-adaptive adjustment module.
The relative importance judging matrix module calculates a relative importance judging matrix R of the components according to the fuzzy severity level S of each component in the system, adjusts the relative importance matrix according to the range of the nine-scale judging scale, wherein the adjusting coefficient is L=1.365, and the relative importance judging matrix of the system is shown in a formula (4):
R←[S T S] L (4)
the component weight calculation module calculates the weight vector of the component according to the relative importance judgment matrix R, and firstly sums the row direction and the column direction of the relative importance judgment matrix R to obtain a column sum vector CS R Further, the relative importance judgment matrix is normalized in the row direction, and the relative importance judgment matrix R, the row and the vector CS R Dividing the number to obtain a normalized judgment matrix R C ＝R/CS R Then, for the normalized judgment matrix R C The row summation processing is adopted to obtain a row summation vector RS R For normalized judgment matrix R C Summing all elements to obtain element and TS R Further, a weight vector W of each component in the system is calculated as shown in a formula (5):
W＝RS R /TS R (5)
the consistency index checking module performs consistency check on the relative importance judgment matrix calculated by the relative importance judgment matrix module and the component weight calculation module and the weight vector of the component, firstly calculates the point multiplication of the relative importance judgment matrix R and the weight vector W, then performs number division on the point multiplication and the weight vector W to obtain a vector lambda, and averages the vector lambda to obtain the maximum characteristic root lambda max Then, according to the number n of the components in the system, calculating a consistency index CI, according to the random consistency index RI corresponding to the number n of the components, calculating a consistency ratio CR, and finally, if CR<0.1, if the consistency check is passed, the weight vector of the component is output to the system health meterAnd the calculation module is used for starting the judgment matrix self-adaptive adjustment module if consistency test is not passed, and the related calculation is shown in a formula (6):
the judgment matrix self-adaptive adjustment module carries out self-adaptive adjustment on the relative importance judgment matrix, firstly, an adjustment coefficient d=0.5 is initialized, the upper right corner elements of the relative importance judgment matrix are adjusted one by one, and an adjustment element r=r d Assigning r=1/R to the element symmetric along the main diagonal, and obtaining an adjusted relative importance judgment matrix R++R according to the adjusted element r The relative importance judging matrix module and the component weight calculating module recalculate to obtain an adjusted weight vector W, if the consistency test is passed, the weight of the component is output to the system health degree calculating module, otherwise, if the consistency test is not passed, the judging matrix self-adaptive adjusting module continues to adjust.
The system health degree calculating module inputs the component health degree HI completed by the component health degree calculating module and the weight vector W of the components in the system completed by the component weight self-adaptive construction module, calculates the health degree of the system according to the reliability block diagram of the system and the logic structure layering level of the reliability block diagram, and is shown in a formula (7):
according to the electronic equipment health assessment device provided by the embodiment, as shown in fig. 3, the calculation pseudo codes of the component and system health assessment are as follows:
step S1: inputting a component severity level fuzzy membership file, an in-machine self-test file, a failure rate and initial reliability file of a component, as shown in tables 1-3;
step S2: inputting a component SEVERITY level fuzzy membership file, and calculating the component fuzzy SEVERITY level by a fuzzy SEVERITY level algorithm (SEVERITY) module, wherein the SEVERITY algorithm is as follows:
input: fuzzy membership vector B of component severity level
The algorithm flow is as follows:
1: acquiring a component severity level set V;
2: calculating the degree of ambiguity of the component based on the gravity center method, S+.VB T ；
3: normalizing the fuzzy severity level of the component, then S+.S/maxV;
and (3) outputting: fuzzy severity level S of component
Step S3: the in-machine self-test file, the failure rate and initial reliability of the component, and the fuzzy severity level of the component are input, and the HEALTH of the component is calculated by a component HEALTH algorithm (HEALTH) module, which is shown as follows:
input: fuzzy severity level S, failure rate h, initial reliability R of component 0 Built-in self-test result BIT
The algorithm flow is as follows:
1: calculating failure occurrence probability of component, OPR+.1-R 0 exp[-hT]；
2: acquiring a severity level adjustment coefficient K++0.5;
3: calculating failure risk RNK +_opr of component 1-KS ；
4: calculating the health degree HI of the components;
5: adjusting the health degree of the component based on the built-in self-test result, HI≡HI (1-BIT);
and (3) outputting: health HI of component
The relationship between the component health HI and the failure occurrence probability OPR and the blur severity level S is as shown in fig. 4, and the greater the failure occurrence probability OPR and the blur severity level S, the lower the component health.
Step S4: the fuzzy severity level of the input component is calculated by a relative importance judgment matrix construction algorithm (JUDGEMENT) module, wherein the JUDGEMENT algorithm is as follows:
input: fuzzy severity level vector S for each component in the system
The algorithm flow is as follows:
1: acquiring a judgment matrix adjustment coefficient L≡1.365;
2: based on fuzzy severity level construction judgment matrix, R≡S T S；
3: adjusting the judgment matrix, R≡R L ；
And (3) outputting: relative importance judgment matrix R of each component in system
Step S5: the relative importance judgment matrix of the components is input, and the WEIGHT of each component in the system is calculated through a WEIGHT calculation algorithm (WEIGHT) module, wherein the WEIGHT calculation algorithm is as follows:
input: relative importance judgment matrix R of each component in system
The algorithm flow is as follows:
1: summing the column directions of the relative importance judgment matrix R to obtain a column sum vector CS R ；
2: normalizing the relative importance judgment matrix in the row direction to obtain a normalized judgment matrix R C ←R/CS R ；
3: for normalized judgment matrix R C Summing the row directions to obtain a row sum vector RS R ；
4: for normalized judgment matrix R C Summing all elements to obtain element and TS R ；
5: calculating weight vector of each component in system, W≡RS R /TS R ；
And (3) outputting: weight vector W for each component in system
Step S6: the weight and relative importance judgment matrix of the input component are subjected to consistency test through a consistency test algorithm (valid) module, and a consistency test result (pass/fail) is obtained, wherein the valid algorithm is as follows:
input: relative importance judgment matrix R and weight vector W of each component in system
The algorithm flow is as follows:
1: calculating the dot product of the relative importance judgment matrix R and the weight vector W, and then dividing the dot product by the weight vector W to obtain a vector lambda (RW/W);
2: calculating the average value of the vector lambda to obtain the maximum characteristic root lambda max ；
3: acquiring the number n of components in the system;
4: computing consistency index CI≡lambda max -n)/(n-1)；
5: acquiring a random consistency index RI corresponding to the number n of the components;
6: calculating a consistency ratio CR≡CI/RI;
7：If CR<0.1 then
consistency test result v≡true;
Else
V←False；
End if
and (3) outputting: consistency test result V (True/False)
Step S7: if not, fine-tuning the relative importance judgment matrix by a judgment matrix self-adaptive adjustment Algorithm (ADJUST) module, and repeating the steps S5 to S7; if so, go to step S8, the ADJUST algorithm is as follows:
input: relative importance judgment matrix R of each component in system
The algorithm flow is as follows:
1: initializing an adjustment coefficient d≡0.5;
2: acquiring the number n of all parts in the system;
3: for row index=1 to maximum row index n do
For column index=row index+1 to maximum column index ndo
Judging matrix element r according to the row-column index, and adjusting element r≡r d ；
Assigning 1/r to the element r symmetrical along the main diagonal;
obtaining an adjusted relative importance judgment matrix R+.R according to the adjustment elements r ；
Invoking a WEIGHT algorithm to obtain an adjusted WEIGHT vector W;
a valid algorithm obtains a consistency test result V;
if consistency test result V is True then
Outputting the adjusted judgment matrix R, and terminating the algorithm flow;
End if
End for
End for
4: judging a matrix R according to the updated relative importance, and calling the ADJUST algorithm again
And (3) outputting: adaptive adjustment relative importance judgment matrix R
Step S8: the HEALTH of the component and the weight of the component are input, and the HEALTH of the SYSTEM is calculated through a SYSTEM HEALTH algorithm (system_health) module, wherein the system_health algorithm is as follows:
input: relative importance judgment matrix R and weight vector W of each component in system and health degree vector HI of each component
The algorithm flow is as follows:
1: calling a valid algorithm to obtain a consistency check result V;
if consistency test result V is False then
An Adjust algorithm obtains a relative importance judgment matrix R after self-adaptive adjustment;
the presence element of the judging matrix R after If adjustment is changed by then
Invoking a WEIGHT algorithm to obtain an adjusted WEIGHT vector W;
End if
End if
2: acquiring the number n of each component in the component system and the health degree HI of each component;
3: if system is serial system then
The health of the series system is calculated,
End if
if system is parallel system then
The health of the parallel system is calculated,
End if
and (3) outputting: health HI of system s
By adopting the calculation steps, firstly, the component-level HEALTH degree is calculated according to the steps S1-S2, and secondly, the SYSTEM-level HEALTH degree is calculated according to the steps S3-S8, wherein the calculation result is shown in a table 4, and the system_number identifies the unique identifier of the SYSTEM or the subsystem and the HEALTH identifies the component and the HEALTH degree of the SYSTEM.
TABLE 4 calculation of health of avionics systems
According to the health degree of the components and the systems, the health state of a certain avionics system is classified and counted, and as shown in the classification and counting diagram of fig. 5, it can be seen that a large number of components or systems are in abnormal states, and it is necessary to exclude the certain avionics system.
According to the health degree distribution diagram of the components and the system, as shown in fig. 6, it can be seen that the health degree of the system is in a normal state, but the level 3 subsystem and the level 1 subsystem are abnormal, so that the health degree of the components is checked, and the health state of the serial component is found to be in an abnormal state, so that maintenance and repair treatment are necessary to the components in the abnormal state.
It will be understood that equivalents and modifications will occur to those skilled in the art in light of the present invention and their spirit, and all such modifications and substitutions are intended to be included within the scope of the present invention as defined in the following claims.
Claims (5)
1. An electronic equipment health evaluation device, includes part-level health evaluation module and system-level health evaluation module, its characterized in that:
the component level health evaluation module comprises a fuzzy severity level calculation module and a component health level calculation module, wherein the fuzzy severity level calculation module is used for defuzzifying fuzzy membership of the component severity level and obtaining the fuzzy severity level of the component; the component health degree calculation module is used for analyzing the reliability degree, the fuzzy severity level and the built-in self test of the component and evaluating the health degree of the component based on the fault risk of the component;
the system-level health evaluation module comprises a component weight self-adaptive construction module and a system health degree calculation module, wherein the component weight self-adaptive construction module constructs a relative importance judgment matrix based on the fuzzy severity level of the component, and adopts a self-adaptive adjustment algorithm to adjust the relative importance judgment matrix so that the relative importance judgment matrix and the weight vector of the component pass consistency test; the system health degree calculation module firstly analyzes a reliability block diagram of the electronic equipment according to the component health degree and weight vectors of components passing consistency test in the system, so as to obtain logic results of the systems, and further selects a calculation mode of the system health degree according to the serial-parallel connection relationship to obtain system health assessment.
2. The electronic device health assessment apparatus according to claim 1, wherein the fuzzy severity level calculation module analyzes the component using a fault type impact and mortality analysis method, constructs a severity level fuzzy membership of the component: after determining the severity level set V, an evaluation group is established to evaluate the severity of each level of each component, obtain an evaluation of each severity level of each component, and count the number of supporters x of each severity level of each component ij From this, the degree of membership b is obtained ij ＝x ij /x i Wherein x is i Indicating the number of people evaluating the ith part; finally, calculating a fuzzy membership vector B of the i-th component severity level;
after the component severity level fuzzy membership vector B is constructed, calculating the component fuzzy severity level S by adopting a gravity center method according to the severity level set V, and carrying out normalization processing, as shown in a formula (1):
S＝VB T /maxV (1)。
3. the electronic equipment health assessment apparatus according to claim 1, wherein the component health degree calculation module analyzes the failure rate h and the initial reliability R of the processing component 0 Based on the reliability analysis, the failure occurrence probability OPR of the component is calculated, and the component failure risk RNK is calculated by combining the fuzzy severity levels S and the adjustment coefficient K of S of the component calculated by the fuzzy severity level calculation module, as shown in the formula (2):
calculating the health degree of the component based on the fault risk of the component, selecting a calculation mode of the health degree of the component according to an built-in self-test diagnosis report of the component, and finally calculating the health degree of the component, wherein the calculation mode is shown in a formula (3):
wherein BIT of 0 indicates no component failure and BIT of 1 indicates component failure.
4. The electronic equipment health assessment device according to claim 1, wherein the component weight adaptive construction module comprises a relative importance judgment matrix module, a component weight calculation module, a consistency index inspection module and a judgment matrix adaptive adjustment module;
the relative importance judging matrix module calculates a relative importance judging matrix R of the components according to the fuzzy severity grade S of each component in the system, adjusts the relative importance matrix according to the range of the nine-scale judging scale, and the adjusting coefficient is L, wherein the relative importance judging matrix of the system is shown in a formula (4):
R←[S T S] L (4)
the component weight calculation module calculates weight vectors of the components according to the relative importance judgment matrix R: firstly, summing the directions of the rows and the columns of a relative importance judgment matrix R to obtain a row sum vector CS R Further, the relative importance judgment matrix is normalized in the row direction, and the relative importance judgment matrix R, the row and the vector CS R Dividing the number to obtain a normalized judgment matrix R C ＝R/CS R Then, for the normalized judgment matrix R C The row summation processing is adopted to obtain a row summation vector RS R For normalized judgment matrix R C Summing all elements to obtain element and TS R Further, a weight vector W of each component in the system is calculated as shown in a formula (5):
W＝RS R /TS R (5)
the consistency index checking module performs consistency check on the relative importance judgment matrix calculated by the relative importance judgment matrix module and the component weight calculation module and the weight vector of the component: firstly, calculating the point multiplication of a relative importance judgment matrix R and a weight vector W, then dividing the point multiplication by the weight vector W to obtain a vector lambda, and averaging the vector lambda to obtain a maximum characteristic root lambda max Then, according to the number n of the components in the system, calculating a consistency index CI, according to the random consistency index RI corresponding to the number n of the components, calculating a consistency ratio CR, and finally, if CR<And 0.1, if the consistency test is passed, outputting a weight vector of the component to a system health degree calculation module, otherwise, if the consistency test is not passed, starting a judgment matrix self-adaptive adjustment module, and calculating the correlation as shown in a formula (6):
the judgment matrix self-adaptive adjustment module carries out self-adaptive adjustment on the relative importance judgment matrix, firstly initializes an adjustment coefficient d, adjusts the upper right corner elements of the relative importance judgment matrix one by one, and adjusts the elements r=r d Assigning r=1/r to the element symmetric along the main diagonal, and obtaining the adjusted relative weight according to the adjustment elementImportance judgment matrix R≡R r The relative importance judging matrix module and the component weight calculating module recalculate to obtain an adjusted weight vector W, if the consistency test is passed, the weight of the component is output to the system health degree calculating module, otherwise, if the consistency test is not passed, the judging matrix self-adaptive adjusting module continues to adjust.
5. The electronic equipment health assessment apparatus according to claim 1, wherein the system health calculation module inputs the component health HI completed by the component health calculation module and the weight vector W of the components in the system completed by the component weight adaptive construction module, calculates the health of the system according to the reliability block diagram of the system and the logic structure hierarchy level of the reliability block diagram, as shown in formula (7):
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211679679.2A CN115994292A (en) | 2022-12-26 | 2022-12-26 | Health evaluation device for electronic equipment |
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| CN116861270A (en) * | 2023-09-04 | 2023-10-10 | 中南大学 | Unmanned aerial vehicle system-level health assessment method and system |
| CN116861270B (en) * | 2023-09-04 | 2023-11-28 | 中南大学 | Unmanned aerial vehicle system-level health assessment method and system |
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