CN115329553A - Reliability testability collaborative modeling and optimal design method based on availability model - Google Patents
Reliability testability collaborative modeling and optimal design method based on availability model Download PDFInfo
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Abstract
The invention belongs to the field of reliability design analysis. The method comprises the steps of taking key design indexes in a reliability and testability design scheme as decision attribute data for judging the reliability and the quality of the testability design scheme, determining the weight coefficient of each decision attribute data, realizing optimization analysis of various reliability and testability design schemes according to a reliability and testability design scheme optimization model based on multi-attribute decision, obtaining the sequencing results of the various design schemes, and finally selecting an optimal reliability and testability design scheme, thereby realizing the overall reliability and testability collaborative modeling and optimization technology.
Description
Technical Field
The invention belongs to the field of reliability design analysis.
Background
Reliability refers to the ability of a product to perform specified functions under specified conditions and within specified time, but a system, equipment or a product is high in reliability and cannot always work normally, and users and maintainers need to know the health condition of the product and to know whether or not a fault occurs or where the fault occurs, so that the product needs to be monitored and tested, and the testability is involved. Testability is a design feature by which a product can determine its state (workable, inoperable, or degraded) accurately in time and isolate its internal faults. Reliability and testability are important quality characteristics of equipment, and the testability of the equipment is closely related to the reliability in both technology and management. Therefore, it is necessary to establish an equipment performance optimization design method capable of coordinating the relation between the equipment reliability design and the testability design, so as to provide objective and scientific basis for the reliability testability comprehensive performance analysis of the equipment.
The traditional equipment reliability optimization design method mainly aims at optimizing a single universal characteristic level of equipment, and has the problem that the comprehensive requirements of various universal characteristics of the equipment are difficult to meet. Chinese patent No. CN105825274a discloses a method for reliability-based maintainability optimization design for engineering machinery products, which comprises the following steps: s1: performing reliability and maintainability optimization distribution on the engineering machinery product design scheme; s2: carrying out product structure design on the scheme subjected to optimized allocation in the step S1; s3: estimating the reliability and maintainability of the product structure design scheme in the step S2; s4: if the design target is met, performing product test or using; if the design target is not met, adjusting the structural parameters, and repeating the steps S2 and S3; s5: performing data statistical analysis, wherein the data statistical analysis comprises actual reliability calculation and actual maintenance calculation; s6: according to the actual reliability and the actual maintenance degree result obtained by calculation in the step S5, if the actual reliability and the actual maintenance degree result meet the design target, ending the process; and if the design is not satisfied, returning to the step S1 and repeating the subsequent steps.
The reliability maintainability optimization design method in the existing scheme is also an equipment reliability optimization design method, the reliability and maintainability design of a product is optimized by adjusting the structure parameters of the product, and whether the design meets the requirement is judged by comparing the predicted value and the required value of the reliability and maintainability of the product, so that the problem that the traditional equipment reliability optimization design method only optimizes the single general characteristic level of the equipment is solved, and a more objective and scientific basis is provided for the reliability and maintainability optimization design of the equipment. However, in order to enhance the fault diagnosis capability of the equipment and realize the testability design of the equipment, certain hardware and software are required to be used as the components of the equipment, but the hardware and the software also have faults, so that the reliability of the equipment is reduced, that is, close relation exists between the reliability and the testability of the equipment, and it is difficult to realize the cooperative optimization design of the reliability and the testability of the equipment by respectively designing single general performance. However, the existing equipment reliability optimization design method does not establish a collaborative optimization model capable of analyzing the comprehensive performance of the equipment in the reliability and testability in various task working modes, and does not introduce an equipment reliability and testability design scheme optimization model capable of realizing the sequencing of various equipment reliability and testability design schemes. Meanwhile, the applicant finds in practical research that the availability model of the equipment can be used for comprehensively evaluating the reliability parameters and the testability parameters of the equipment in various task working modes, and provides a basis for the cooperative optimization design work of the reliability and the testability of the equipment, so that the cooperative modeling and the optimization design of the reliability and the testability of the equipment facing the task can be realized.
Therefore, how to design a collaborative modeling and optimal design method capable of finding optimal equipment reliability and testability based on an equipment availability model and a scheme optimization model is a technical problem which needs to be solved urgently.
Disclosure of Invention
Aiming at the defects of the prior art, the technical problems to be solved by the invention are as follows: how to provide the equipment reliability testability collaborative modeling and optimization design method which can fully consider the characteristics of the multitask working mode of the equipment in the complex environment, the collaborative analysis and optimization work of the multitask-oriented equipment reliability and testability design scheme can be realized based on the availability model, and the equipment reliability and testability design scheme which best meets the requirements can be optimized based on the equipment reliability and testability design scheme optimization model, so that the accuracy and the comprehensiveness of the equipment reliability testability collaborative modeling and optimization design are improved.
In order to solve the technical problems, the invention adopts the following technical scheme:
s1: and combining the reliability modeling simulation work and the testability modeling simulation work to acquire the key design variable data of the reliability and testability design scheme of the equipment in each task working mode.
S2: sampling key design index data of a reliability and testability design scheme under each task working mode, substituting the sampled data into a collaborative optimization model
Obtaining a plurality of use availability data A under each task working mode of the equipment;
s3: determining the lowest requirement value A of the usability of the equipment in each task working mode min Judging whether a plurality of use availability data A under each task working mode of the equipment meet the system requirement, and when the plurality of use availability data A under each task working mode are not less than the lowest use availability requirement value A min The reliability and testability design meets the requirements;
s4: if the reliability and testability design scheme meets the requirements, the reliability and testability design scheme is reserved at this time, and then the cooperative modeling and optimization analysis of the reliability and testability design scheme are completed; if the reliability and testability design scheme does not meet the requirements, optimizing the reliability and testability parameters through the adjustment work of the reliability and testability design scheme, sampling the key design index data of the optimized reliability and testability design scheme again, and substituting the sampled data into the model
Until the output result of the model meets the requirement, the cycle is repeated for many times to form various reliability and testability design schemes and key design index data thereof;
s5: and combining key design index data of various reliability and testability design schemes, realizing optimization analysis of various reliability and testability design schemes according to the reliability and testability design scheme optimization model based on multi-attribute decision, obtaining the sequencing results of various design schemes, and finally selecting the optimal reliability and testability design scheme.
Preferably, in step S1, reliability and testingKey design variables for the design solution include: mean Time Between Failures (MTBF) of reliability parameters, mean time before repair (MTTR) of maintainability parameters, and sum of fault detection time and fault isolation time (T) of testability parameters D And guaranteed parameter average guaranteed delay time MLDT.
Preferably, under the condition that the faults of all the components of the equipment are subject to exponential distribution, the reliability parameter mean time between failures MTBF of the equipment in each task working mode is calculated by the following formula:
in the formula: lambda [ alpha ] S Indicating the failure rate of the equipment in each task working mode;
wherein, the task reliability block diagram of the equipment under each task working mode and the failure rate lambda of each component of the equipment are combined i Obtaining the failure rate lambda of the equipment under each task working mode S 。
Preferably, the maintenance parameter mean time to repair MTTR of the equipment in each task operation mode is calculated by the following formula:
in the formula: i represents the number of component parts of the equipment; lambda [ alpha ] i The failure rate of each component of the equipment in each task working mode is represented; MTTR i And the repair time of each component part of the equipment in each task working mode is shown.
Preferably, the sum of the fault detection time and the fault isolation time of each component of the equipment is obtained by combining the fault detection time of each test point, the fault isolation time of the fuzzy groups with different fuzziness and a diagnosis strategy, wherein the diagnosis strategy is obtained by testability modeling simulation work, and comprises the following steps: detecting a set of test points corresponding to each critical component failure and the ambiguity corresponding to the final detected ambiguity set byThe sum T of the fault detection time and the fault isolation time of the b-th component of the formula computing equipment in each task working mode Db ：
In the formula: a represents the number of test points for detecting whether the b-th component fails; t is t a Indicating the fault detection time of the a test point in a group of test points for detecting whether the b component is in fault; t is t b Indicating a fault isolation time for detecting whether the b-th component is faulty;
obtaining the sum T of the fault detection time and the fault isolation time of all the parts of the equipment Db Based on the time sequence of the fault detection, sampling the sum of the fault detection time and the fault isolation time of each component according to the randomness of the fault of each component of the equipment to obtain the sum T of the fault detection time and the fault isolation time of the equipment D 。
Preferably, the average guaranteed delay time MLDT of the guaranteed parameters of all the components of the equipment in all the task working modes is obtained b Comprises the following steps:
s101: specifying factors that influence the MLDT index: spare part delay, guarantee equipment delay, guarantee group occupation delay;
s102: defining a set of ratings evaluating the MLDT index and its influencing factors: { very short, medium, long, very long };
s103: obtaining measurement values of all influence factors of all components of the equipment under all task working modes, wherein the influence factors belong to all evaluation levels, and the indexes of the measurement values adopt a 0.1-0.9 scaling method to obtain a fuzzy evaluation matrix R of all the components of the equipment under all task working modes;
s104: obtaining a weight vector W of each influence factor of each component of the equipment in each task working mode;
s105: calculating the comprehensive evaluation result S of the MLDT indexes of all the components of the equipment in all the task working modes through the following formula:
s = W × R; in the formula: w represents the weight vector of each influence factor of each component of the equipment in each task working mode; r represents a fuzzy evaluation matrix of each component of the equipment in each task working mode;
s106: judging and obtaining the evaluation grade of the MLDT index of each component of the equipment in each task working mode and the corresponding maximum membership U according to the comprehensive evaluation result obtained in the step S105 and the maximum membership principle max (MLDT)；
S107: and (3) defining an MLDT index value section corresponding to the MLDT index evaluation grade:
s108: calculating the MLDT index values of all the components of the equipment in all the task working modes through the following formula:
MLDT＝U max (MLDT)*(b-a)+a；
in the formula: u shape max (MLDT) represents the maximum degree of membership of the MLDT index obtained in step S107; a represents the maximum degree of membership U max (MLDT) is located in the minimum value of the MLDT index value interval; b represents the maximum degree of membership U max The maximum value b of the MLDT index value interval where the (MLDT) is located;
obtaining the average guaranteed delay time MLDT of all parts of the equipment b On the basis, the average guarantee delay time MLDT of each component is obtained by sampling the average guarantee delay time of each component according to the randomness of the failure of each component of the equipment.
Preferably, in step S5, the mean time before repair MTTR and the mean time to failure diagnosis T of the design plan for reliability and testability of the equipment in the same task operating mode are determined D And determining the MLDT data as decision attribute data of the reliability and testability design scheme.
Preferably, the step of cooperatively modeling the equipment reliability testability based on the availability model and optimizing the design scheme optimization model comprises the following steps:
s501: defining the times n of single sequencing of the scheme, and determining the weight coefficient omega of the average time before repair 1 A weight coefficient omega of the sum of the fault detection time and the fault isolation time 2 Weight coefficient omega of average guaranteed delay time 3 ；
S502: sampling 1 set of decision attribute data for each design, including 3 types of data: mean time before repair MTTR, sum of fault detection time and fault isolation time T D Average guarantee delay time MLDT;
s503: the same type data of a plurality of design schemes obtained by sampling under the same task working mode is normalized through the following formula, and the normalized value of various types of data of each design scheme is obtained:
in the formula: i represents the number of design solutions; MTTR i Mean time to repair data representing the ith design; MTTR i "represents a normalized value of the average pre-repair time data of the ith design:
in the formula: i represents the number of design solutions; t is Di Data representing a sum of a fault detection time and a fault isolation time of the ith design; t is Di "represents a normalized value of sum data of the fault detection time and the fault isolation time of the ith design:
in the formula: i represents the number of design solutions; MLDT i Mean guaranteed delay time data representing the ith design; MLDT i "represents a normalized value of the average guaranteed delay time data of the ith design scheme;
s504: the comprehensive ranking value A of each scheme is calculated by the following formula i ：
A i ＝ω 1 *MTTR i `+ω 2 *T Di `+ω 3 *MLDT i `；
In the formula: a. The i A composite ranking value representing the ith design; omega 1 A weight coefficient representing an average time before repair; omega 2 A weight coefficient representing a sum of the fault detection time and the fault isolation time; omega 3 A weight coefficient representing an average guaranteed delay time; MTTR i "represents a normalized value of the average pre-repair time data of the ith design solution; t is Di "represents a normalized value of sum data of the failure detection time and the failure isolation time of the ith design solution; MLDT i "represents a normalized value of the average guaranteed delay time data of the ith design solution;
s505: finishing single sorting of multiple schemes according to the comprehensive sorting value, wherein the scheme with the small comprehensive sorting value is arranged in front, and recording the sorting result;
s506: judging whether the scheme sorting times reach the specified sorting times n, if not, returning to the step S502; if so, calculating the possible degree N of each scheme at each sequencing position by the following formula K ：
In the formula: a is K Representing the number of times the design is ranked at position K; n represents the total number of scheme orderings;
s507: comparing the likelihood N of each scheme at the ranking position K K And if the reliability degrees of the two schemes are equal, continuously comparing the possibility degree of the next position until a final reliability and testability design scheme ordering structure result is obtained.
Compared with the prior art, the equipment reliability testability collaborative modeling and optimization design method has the following beneficial effects:
in the invention, the availability model of the equipment is introduced, so that the constraint condition of the equipment availability can be fully considered in the process of developing the equipment reliability testability collaborative optimization work, and the accuracy of the equipment reliability testability collaborative modeling and the optimization design is improved. Meanwhile, the cooperative modeling and analysis process of the equipment reliability and the testability can be realized based on the equipment availability model and the key design variables of the reliability and testability design scheme, and the optimal equipment reliability and testability design scheme is selected by combining the reliability and testability design scheme optimization model based on the multi-attribute decision, so that the comprehensiveness of the cooperative optimization method of the equipment reliability and the testability is improved.
Drawings
FIG. 1 is a logic block diagram of a multitask-oriented equipment reliability testability collaborative modeling and optimization design method.
Detailed Description
The following is further detailed by the specific embodiments:
the embodiment discloses a reliability testability collaborative modeling and optimal design method based on an availability model.
As shown in fig. 1, the cooperative modeling and optimization design method for reliability testability of multi-task-oriented equipment includes the following steps:
s1: and combining the reliability modeling simulation work and the testability modeling simulation work to acquire the key design variable data of the reliability and testability design scheme of the equipment in each task working mode.
S2: sampling key design index data of a reliability and testability design scheme under each task working mode, substituting the sampled data into a collaborative optimization model
Obtaining a plurality of use availability data A under each task working mode of the equipment;
s3: determining the lowest requirement value A of the usability of the equipment in each task working mode min Judging whether a plurality of use availability data A under each task working mode of the equipment meet the system requirement, and when the plurality of use availability data A under each task working mode are not less than the lowest use availability requirement value A min The reliability and testability design meets the requirements;
s4: if the reliability and testability design scheme meets the requirements, the reliability and testability design scheme is reserved at this time, and then the cooperative modeling and optimization analysis of the reliability and testability design scheme are completed; if the reliability and testability design scheme does not meet the requirements, optimizing the reliability and testability parameters through the adjustment work of the reliability and testability design scheme, sampling the key design index data of the optimized reliability and testability design scheme again, and substituting the sampled data into the model
Until the output result of the model meets the requirement, the cycle is repeated for many times to form various reliability and testability design schemes and key design index data thereof;
s5: and combining key design index data of various reliability and testability design schemes, realizing optimization analysis of various reliability and testability design schemes according to the reliability and testability design scheme optimization model based on multi-attribute decision, obtaining the sequencing results of various design schemes, and finally selecting the optimal reliability and testability design scheme.
In the invention, the availability model of the equipment is introduced, so that the constraint condition of the equipment availability can be fully considered in the process of developing the equipment reliability testability collaborative optimization work, and the accuracy of the equipment reliability testability collaborative modeling and the optimization design is improved. Meanwhile, the cooperative modeling and analyzing process of the equipment reliability and the testability can be realized based on the equipment availability model and the key design variables of the reliability and testability design scheme, and the optimal equipment reliability and testability design scheme is selected by combining the reliability and testability design scheme optimal model based on the multi-attribute decision, so that the comprehensiveness of the equipment reliability and testability cooperative optimization method is improved.
In the specific implementation process, the key design variables of the reliability and testability design scheme comprise: mean Time Between Failures (MTBF) of reliability parameters, mean time before repair (MTTR) of maintainability parameters, and time when fault detection and fault isolation of testability parametersSum of between T D And guaranteed parameter average guaranteed delay time MLDT.
Specifically, under the condition that the faults of all components of the equipment are subject to exponential distribution, the reliability parameter Mean Time Between Failures (MTBF) of the equipment in each task working mode is calculated by the following formula:
in the formula: lambda [ alpha ] S The failure rate of the equipment in each task working mode is represented;
wherein, the task reliability block diagram of the equipment under each task working mode and the failure rate lambda of each component of the equipment are combined i Obtaining the failure rate lambda of the equipment under each task working mode S 。
Specifically, the maintenance parameter mean time before repair MTTR of the equipment in each task working mode is calculated by the following formula:
in the formula: i represents the number of component parts of the equipment; lambda [ alpha ] i The failure rate of each component of the equipment in each task working mode is represented; MTTR i And the repair time of each component part of the equipment in each task working mode is shown.
Specifically, the sum of the fault detection time and the fault isolation time of each component of the equipment is obtained by combining the fault detection time of each test point, the fault isolation time of the fuzzy groups with different fuzziness and a diagnosis strategy, wherein the diagnosis strategy is obtained by testability modeling simulation work, and comprises the following steps: detecting a group of test points corresponding to the faults of each key component and the fuzziness corresponding to the finally detected fuzziness group, and calculating the sum T of the fault detection time and the fault isolation time of the b-th component of the equipment in each task working mode through the following formula Db ：
In the formula: a represents the number of test points for detecting whether the b-th component fails; t is t a Indicating the fault detection time of the a test point in a group of test points for detecting whether the b component is in fault; t is t b Indicating a fault isolation time for detecting whether the b-th component is faulty;
obtaining the sum T of the fault detection time and the fault isolation time of all the parts of the equipment Db Based on the time sequence of the fault detection, sampling the sum of the fault detection time and the fault isolation time of each component according to the randomness of the fault of each component of the equipment to obtain the sum T of the fault detection time and the fault isolation time of the equipment D 。
Specifically, the step of obtaining the average guaranteed delay time MLDT of the guaranteed parameters of the components of the equipment in each task working mode comprises the following steps:
s101: specifying factors that influence the MLDT index: spare parts are delayed, equipment is ensured to be delayed, and occupation of a group is ensured to be delayed;
s102: defining a set of ratings evaluating the MLDT index and its influencing factors: { very short, medium, long, very long };
s103: obtaining measurement values of all influence factors of all components of the equipment under all task working modes, wherein the influence factors belong to all evaluation levels, and the indexes of the measurement values adopt a 0.1-0.9 scaling method to obtain a fuzzy evaluation matrix R of all the components of the equipment under all task working modes;
s104: obtaining a weight vector W of each influence factor of each component of the equipment in each task working mode;
s105: calculating the comprehensive evaluation result S of the MLDT indexes of all the components of the equipment in all the task working modes through the following formula:
s = W × R; in the formula: w represents the weight vector of each influence factor of each component of the equipment in each task working mode; r represents a fuzzy evaluation matrix of each component of the equipment in each task working mode;
s106: according to the stepsS105, judging and obtaining the evaluation grade of the MLDT index of each component of the equipment in each task working mode and the corresponding maximum membership U according to the maximum membership principle according to the comprehensive evaluation result obtained by the step max (MLDT)；
S107: and (3) defining an MLDT index value section corresponding to the MLDT index evaluation grade:
s108: calculating the MLDT index values of all the components of the equipment in all the task working modes through the following formula:
MLDT＝U max (MLDT)*(b-a)+a；
in the formula: u shape max (MLDT) represents the maximum degree of membership of the MLDT index obtained in step S107; a represents the maximum degree of membership U max The minimum value of the MLDT index value interval where (MLDT) is located; b represents the maximum degree of membership U max The maximum value b of the MLDT index value interval where the (MLDT) is located;
on the basis of obtaining the average guarantee delay time MLDT of all the components of the equipment, sampling the average guarantee delay time MLDT of each component according to the randomness of the faults of all the components of the equipment to obtain the average guarantee delay time MLDT of the equipment.
The reliability parameter mean fault interval time, the maintainability parameter mean repair time, the sum of the testability parameter fault detection time and the fault isolation time and the supportability parameter mean guarantee delay time which are arranged under each task working mode are used as key design variables of a reliability and testability design scheme, wherein the mean fault interval time and the mean repair time are deterministic data, the sum of the fault detection time and the fault isolation time is random data, and the mean guarantee delay time is fuzzy data. Meanwhile, the reliability modeling simulation work and the testability modeling simulation work are combined to obtain the key design variable data which are used as the input data of the subsequent cooperative modeling and optimization work of the reliability and testability design scheme and the optimization work of the reliability and testability design scheme.
On the basis of obtaining the key design variable data of the reliability and testability design scheme under each task working mode and the lowest requirement of the availability, the invention carries out collaborative modeling analysis on various reliability and testability design schemes according to the collaborative optimization model to obtain the analysis result of whether the design scheme meets the requirement of the availability, and uses the design scheme meeting the requirement and corresponding key design index data as the input data of the optimal work of the reliability and testability design scheme.
In the specific implementation process, the mean time before repair MTTR and the mean time before failure diagnosis T of the design scheme with reliability and testability of the equipment in the same task working mode D And determining the MLDT data as decision attribute data of the reliability and testability design scheme.
Specifically, the step of performing collaborative modeling and optimization design scheme optimization model based on equipment reliability testability of multi-attribute decision comprises the following steps:
s501: defining the times n of single sequencing of the scheme, and determining the weight coefficient omega of the average time before repair 1 A weight coefficient omega of the sum of the fault detection time and the fault isolation time 2 Weight coefficient omega of average guaranteed delay time 3 ；
S502: sampling 1 set of decision attribute data for each design, including 3 types of data: mean time before repair MTTR, sum of fault detection time and fault isolation time T D Average guarantee delay time MLDT;
s503: the same type data of a plurality of design schemes obtained by sampling under the same task working mode is normalized through the following formula, and the normalized value of various types of data of each design scheme is obtained:
in the formula: i represents the number of design solutions; MTTR i Mean time to repair data representing the ith design; MTTR i "normalized value representing the average pre-repair time data for the ith design:
in the formula: i represents the number of design solutions; t is Di Data representing the sum of the fault detection time and the fault isolation time of the ith design scheme; t is Di "normalized value representing sum data of failure detection time and failure isolation time of the ith design:
in the formula: i represents the number of design solutions; MLDT i Mean guaranteed delay time data representing the ith design; MLDT i "represents a normalized value of the average guaranteed delay time data of the ith design scheme;
s504: the comprehensive ranking value A of each scheme is calculated by the following formula i ：
A i ＝ω 1 *MTTR i `+ω 2 *T Di `+ω 3 *MLDT i `；
In the formula: a. The i A composite ranking value representing the ith design; omega 1 A weight coefficient representing an average time before repair; omega 2 A weight coefficient representing a sum of the fault detection time and the fault isolation time; omega 3 A weight coefficient representing an average guaranteed delay time; MTTR i "represents a normalized value of the average pre-repair time data of the ith design solution; t is Di "represents a normalized value of sum data of the failure detection time and the failure isolation time of the ith design solution; MLDT i "represents a normalized value of the average guaranteed delay time data of the ith design solution;
s505: finishing single sorting of multiple schemes according to the comprehensive sorting value, wherein the scheme with the small comprehensive sorting value is arranged in front, and recording the sorting result;
s506: judging whether the scheme sorting times reach the specified sorting times n, if not, returning to the step S502; if so, calculating the possible degree N of each scheme at each sequencing position by the following formula K ：
In the formula: a is K Representing the number of times the design is lined up at position K; n represents the total number of scheme orderings;
s507: comparing the probability N of each scheme at the ranking position K K And if the reliability degrees of the two schemes are equal, continuously comparing the possibility degree of the next position until a final reliability and testability design scheme ordering structure result is obtained.
According to the invention, key design indexes in the reliability and testability design scheme are used as decision attribute data for judging the reliability and the testability design scheme, the weight coefficient of each decision attribute data is determined, finally, the optimal analysis of various reliability and testability design schemes is realized according to the reliability and testability design scheme optimal model based on multi-attribute decision, the sequencing results of various design schemes are obtained, and the optimal reliability and testability design scheme is finally selected, so that the overall reliability and testability collaborative modeling and optimization technology is realized.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that, while the invention has been described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Meanwhile, the detailed structures, characteristics and the like of the common general knowledge in the embodiments are not described too much. Finally, the scope of the claims should be determined by the content of the claims, and the description of the embodiments and the like in the specification should be used for interpreting the content of the claims.
Claims (8)
1. The equipment reliability testability collaborative modeling and optimization design method based on the availability model is characterized in that:
s1: combining the reliability modeling simulation work and the testability modeling simulation work to obtain key design variable data of the reliability and testability design scheme of the equipment in each task working mode;
s2: sampling key design index data of a reliability and testability design scheme under each task working mode, substituting the sampled data into a collaborative optimization model
Obtaining a plurality of use availability data A under each task working mode of the equipment;
s3: determining the lowest requirement value A of the usability of the equipment in each task working mode min Judging whether a plurality of use availability data A under each task working mode of the equipment meet the system requirement, and when the plurality of use availability data A under each task working mode are not less than the lowest use availability requirement value A min The reliability and testability design meets the requirements;
s4: if the reliability and testability design scheme meets the requirements, the reliability and testability design scheme is reserved at this time, and then the cooperative modeling and optimization analysis of the reliability and testability design scheme are completed; if the reliability and testability design scheme does not meet the requirements, optimizing the reliability and testability parameters through the adjustment work of the reliability and testability design scheme, sampling the key design index data of the optimized reliability and testability design scheme again, and substituting the sampled data into the model
Until the output result of the model meets the requirement, the cycle is repeated for many times to form various reliability and testability design schemes and key design index data thereof;
s5: and combining key design index data of various reliability and testability design schemes, realizing optimization analysis of various reliability and testability design schemes according to the reliability and testability design scheme optimization model based on multi-attribute decision, obtaining the sequencing results of various design schemes, and finally selecting the optimal reliability and testability design scheme.
2. The availability model-based equipment reliability testability collaborative modeling and optimization design method of claim 1, wherein: the key design variable data in the step S1 includes: mean Time Between Failures (MTBF) of reliability parameter, mean time before repair (MTTR) of maintainability parameter, and sum of fault detection time and fault isolation time (T) of testability parameter D And guaranteed parameter average guaranteed delay time MLDT.
3. The method of claim 2, wherein the method comprises: in step S1, under the condition that the faults of the components of the equipment obey the exponential distribution, the reliability parameter mean time between failures MTBF of the equipment in each task operating mode is calculated by the following formula:
in the formula: lambda [ alpha ] S Indicating the failure rate of the equipment in each task working mode;
wherein, the task reliability block diagram of the equipment under each task working mode and the failure rate lambda of each component of the equipment are combined i Obtaining the failure rate lambda of the equipment under each task working mode S 。
4. The method of claim 2, wherein the method comprises: in the step S1, the maintenance parameter mean repair time MTTR of the equipment in each task working mode is calculated by the following formula:
in the formula: i represents the number of component parts of the equipment; lambda [ alpha ] i The failure rate of each component of the equipment in each task working mode is represented; MTTR i And the repair time of each component part of the equipment in each task working mode is shown.
5. The availability model-based equipment reliability testability collaborative modeling and optimization design method of claim 2, wherein: in the step S1, the sum of the fault detection time and the fault isolation time of each component of the equipment is obtained by combining the fault detection time of each test point, the fault isolation time of the fuzzy groups with different fuzziness, and a diagnosis strategy, wherein the diagnosis strategy is obtained by testability modeling simulation work, and the diagnosis strategy includes: detecting a group of test points corresponding to the faults of each key component and the fuzziness corresponding to the finally detected fuzziness group, and calculating the sum T of the fault detection time and the fault isolation time of the b-th component of the equipment in each task working mode through the following formula Db ：
In the formula: a represents the number of test points for detecting whether the b-th component fails; t is t a Representing the fault detection time of the a test point in a group of test points for detecting whether the b component is in fault; t is t b Indicating a fault isolation time for detecting whether the b-th component is faulty; obtaining the sum T of the fault detection time and the fault isolation time of all the parts of the equipment Db Based on the time sequence of the fault detection, sampling the sum of the fault detection time and the fault isolation time of each component according to the randomness of the fault of each component of the equipment to obtain the sum T of the fault detection time and the fault isolation time of the equipment D 。
6. Availability model-based equipment reliability testability collaborative modeling and optimization design according to claim 2The method is characterized by comprising the following steps: in the step S1, the guaranteed parameter average guarantee delay time MLDT of each component of the equipment in each task working mode is obtained b Comprises the following steps:
s101: specifying factors that influence the MLDT index: spare parts are delayed, equipment is ensured to be delayed, and occupation of a group is ensured to be delayed;
s102: defining a set of ratings evaluating the MLDT index and its influencing factors: { very short, medium, long, very long };
s103: obtaining measurement values of all influence factors of all components of the equipment under all task working modes, wherein the influence factors belong to all evaluation levels, and the indexes of the measurement values adopt a 0.1-0.9 scaling method to obtain a fuzzy evaluation matrix R of all the components of the equipment under all task working modes;
s104: obtaining a weight vector W of each influence factor of each component of the equipment in each task working mode;
s105: calculating the comprehensive evaluation result S of the MLDT indexes of all the components of the equipment in all the task working modes through the following formula: s = W × R;
in the formula: w represents the weight vector of each influence factor of each component of the equipment in each task working mode; r represents a fuzzy evaluation matrix of each component of the equipment in each task working mode;
s106: judging and obtaining the evaluation grade of the MLDT index of each component of the equipment in each task working mode and the corresponding maximum membership U according to the comprehensive evaluation result obtained in the step S105 and the maximum membership principle max (MLDT)；
S107: and (3) defining an MLDT index value section corresponding to the MLDT index evaluation grade:
s108: calculating the MLDT index values of all the components of the equipment in all the task working modes through the following formula:
MLDT＝U max (MLDT)*(b-a)+a；
in the formula: u shape max (MLDT) represents the maximum degree of membership of the MLDT index obtained in step S107; a represents the maximum degree of membership U max (MLDT) is located in the minimum value of the MLDT index value interval; b represents the maximum degree of membership U max (MLDT) is locatedThe maximum value b of the MLDT index value interval;
obtaining the average guaranteed delay time MLDT of all parts of the equipment b On the basis, the average guarantee delay time of each component is sampled according to the randomness of the faults of all the components of the equipment, and the average guarantee delay time MLDT of the equipment is obtained.
7. The availability model-based equipment reliability testability collaborative modeling and optimization design method of claim 1, wherein: in step S4, the adjusting work of the reliability design scheme and the testability design scheme includes: adjusting the hardware structure of the equipment, optimizing the reliability of the component parts of the equipment, adjusting the diagnosis strategy of the equipment, optimizing the fault detection time of each test point of the equipment and the fault isolation time of each fuzzy group of the equipment.
8. The method of claim 1 for collaborative modeling and optimization design for equipment reliability testability based on availability model, wherein: in step S5, the step of performing collaborative modeling of equipment reliability testability and optimizing a design solution optimization model based on multi-attribute decision includes:
s501: defining the times n of single sequencing of the scheme, and determining the weight coefficient omega of the average time before repair 1 A weight coefficient omega of the sum of the fault detection time and the fault isolation time 2 Weight coefficient omega of average guaranteed delay time 3 ；
S502: sampling 1 set of decision attribute data for each design, including 3 types of data: mean time before repair MTTR, sum of fault detection time and fault isolation time T D Average guarantee delay time MLDT;
s503: the same type data of a plurality of design schemes obtained by sampling under the same task working mode is normalized through the following formula, and the normalized value of various types of data of each design scheme is obtained:
in the formula: i represents the number of design solutions; MTTR i Mean time to repair data representing the ith design; MTTR i "represents a normalized value of the average pre-repair time data of the ith design solution;
in the formula: i represents the number of design solutions; t is Di Data representing a sum of a fault detection time and a fault isolation time of the ith design; t is Di "represents a normalized value of sum data of the failure detection time and the failure isolation time of the ith design solution;
in the formula: i represents the number of design solutions; MLDT i Mean guaranteed delay time data representing the ith design; MLDT i "represents a normalized value of the average guaranteed delay time data of the ith design solution;
s504: calculating the comprehensive ranking value A of each scheme by the following formula i ：
A i ＝ω 1 *MTTR i `+ω 2 *T Di `+ω 3 *MLDT i `；
In the formula: a. The i A composite ranking value representing the ith design; omega 1 A weight coefficient representing an average time before repair; omega 2 A weight coefficient representing a sum of the fault detection time and the fault isolation time; omega 3 A weight coefficient representing an average guaranteed delay time; MTTR i "represents a normalized value of the average pre-repair time data of the ith design solution; t is a unit of Di "represents a normalized value of sum data of the failure detection time and the failure isolation time of the ith design solution; MLDT i "represents the average guaranteed delay time of the ith designNormalizing the value according to;
s505: finishing single sorting of multiple schemes according to the comprehensive sorting value, wherein the scheme with the small comprehensive sorting value is arranged in front, and recording the sorting result;
s506: judging whether the scheme sorting times reach the specified sorting times n, if not, returning to the step S502; if so, calculating the possible degree N of each scheme at each sequencing position by the following formula K ：
In the formula: a is K Representing the number of times the design is lined up at position K; n represents the total number of scheme orderings;
s507: comparing the likelihood N of each scheme at the ranking position K K And if the reliability degrees of the two schemes are equal, continuously comparing the possibility degree of the next position until a final reliability and testability design scheme ordering structure result is obtained.
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