Equipment Test Design Methodology for Multi-Task Operating Mode

Abstract

This invention specifically relates to a testability design method for equipment operating in a multi-task mode, comprising: establishing a correlation diagram model and a D-matrix model of the unit under test; calculating the single-task risk weight coefficient vector of each component of the unit under test in a single-task operating mode; calculating the corresponding task weight coefficient vector; calculating the multi-task risk weight coefficient vector of each component in a multi-task operating mode; calculating the fault detection weight and fault isolation weight of each component at corresponding test points; selecting fault detection test points and fault isolation test points based on the correlation diagram model and D-matrix model of the unit under test and the fault detection weight and fault isolation weight of each component at corresponding test points to establish a diagnostic strategy; and calculating testability indicators. This equipment testability design method can improve the risk weight of faults in each component of equipment under a multi-task operating mode scenario and strengthen the focus on high-risk components.

Description

Technical Field

[0001] This invention relates to the field of testability design technology, and more specifically to a testability design method for equipment oriented towards multi-task operating modes. Background Technology

[0002] Testability is a design characteristic of equipment (products) that enables timely and accurate determination of its status (including whether it is operational, inoperable, or the degree of performance degradation) and isolation of internal faults. In the field of complex equipment design, testability design has gained widespread attention. Good testability design can not only effectively improve equipment availability but also reduce the total life-cycle cost of equipment.

[0003] To address the issues of testability design and diagnostic strategy generation for equipment, Chinese Patent Publication No. CN112035996A discloses "An Integrated Design and Evaluation System for Equipment Testability," which includes a hierarchical modeling module. The hierarchical modeling module performs FST modeling, and the integrated design and analysis module combines relevant information from the FST model to perform testability analysis and allocate testability indicators for the equipment. The results of the testability analysis are used to generate diagnostic strategies in the test diagnostic strategy generation module, and the results of the generated diagnostic strategies are used for simulation and physical testing in the simulation and physical testing module.

[0004] The existing integrated design and evaluation system for equipment testability, as described above, achieves testability design and diagnostic strategy generation by performing testability analysis and allocating testability indicators. However, existing testability design methods are inconsistent with the equipment design philosophy of concurrent engineering. They fail to consider the risk weights of faults in each component of the unit under test (UDT) under multi-task operating modes, ignore the connection between testability and important design characteristics such as reliability and maintainability, and provide insufficient attention to high-risk components in testability design, resulting in low reliability of testability indicators and poor design quality. Therefore, how to develop an equipment testability design method that can improve the risk weights of faults in each component of the UDT under multi-task operating modes and strengthen the focus on high-risk components is an urgent technical problem to be solved. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the technical problem to be solved by the present invention is: how to provide a test design method for equipment oriented to multi-task working mode, so as to improve the risk weight of failure of each component of the equipment in multi-task working mode scenario, and to strengthen the attention to high-risk components, thereby improving the accuracy and comprehensiveness of equipment test design work.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A testable design methodology for equipment oriented towards multi-task operating modes includes the following steps:

[0008] S1: Establish the correlation diagram model and D matrix model of the unit under test;

[0009] S2: Calculate the single-task risk weight coefficient vector of each component of the unit under test in the single-task working mode;

[0010] S3: Calculate the corresponding task weight coefficient vector based on the working time and fault frequency ratio of each task working mode of the unit under test;

[0011] S4: Based on the task weight coefficient vector of the unit under test and the single task risk weight coefficient vector of each component, calculate the multi-task risk weight coefficient vector of each component in the multi-task working mode.

[0012] S5: Based on the multi-task risk weight coefficient vector of each component of the unit under test, calculate the fault detection weight and fault isolation weight of each component at the corresponding test point;

[0013] S6: Based on the correlation diagram model and D matrix model of the unit under test, as well as the fault detection weights and fault isolation weights of the corresponding test points of each component, select the test points for fault detection and the test points for fault isolation to establish a diagnostic strategy.

[0014] S7: Calculate testability indicators based on diagnostic strategies and complete the testability design of the equipment.

[0015] Preferably, in step S2, the single-task risk weight coefficient vector is calculated through the following steps:

[0016] S201: Analysis of the risk priority coefficient vector of each component of the unit under test in task working mode j based on FMECA table.

[0017] S202: For the risk priority coefficient vector RPN j In and Perform a binary comparison and construct a binary comparison matrix E of indicator importance based on the ranking scale. j ;

[0018] S203: Based on the binary contrast matrix E j Calculate the risk priority vector RPN j Importance Ranking Index And sorted by importance index Calculate the corresponding fuzzy judgment matrix M j ;

[0019] S204: Based on fuzzy judgment matrix Mj Calculate the sum of the fuzzy membership degrees of each row. And sum the fuzzy membership degrees of each row After normalization, the risk weight coefficients of component a of the tested unit under task operating mode j are obtained. Then, the risk weight coefficients of each other component of the unit under test are calculated under task operating mode j, resulting in a single-task risk weight coefficient vector for each component of the unit under test under task operating mode j.

[0020] Preferably, in step S201, the risk priority coefficient vector

[0021] in,

[0022] In the formula: a = 1, 2, ..., A; A represents the total number of components of the unit being tested; This represents the risk priority coefficient of component a of the tested unit under task operating mode j. This indicates the ease or difficulty of testing component a after failure mode n occurs under task working mode j. This indicates the probability level of failure mode n occurring in component a under task operating mode j; This indicates the severity of the influence of component a on the unit under test under task working mode j, and the subscript n indicates the failure mode coefficient of the corresponding component. This indicates the working time of component a in task working mode j.

[0023] Preferably, in step S202, the binary contrast matrix is ​​represented as follows:

[0024] in, Greater than At that time, sorting scale equal At that time, sorting scale Less than At that time, sorting scale

[0025] In the formula: b = 1, 2, ..., A.

[0026] Preferably, in step S203, the importance ranking index is expressed as:

[0027] The fuzzy judgment matrix is ​​represented as follows

[0028] The relative importance of fuzzy membership between any two components is represented as follows:

[0029] Preferably, in step S204, the sum of the fuzzy membership degrees of each row is represented as:

[0030] The risk weight coefficient of component a under task working mode j is expressed as follows:

[0031] The single-task risk weight coefficient vector of each component of the tested unit under task working mode j is represented as follows:

[0032] Preferably, in step S3, the task weight coefficient vector is represented as H. j =(h1,h2,...,h J );

[0033] Wherein, the task weight coefficient of task working mode j is expressed as

[0034]

[0035] In the formula: J represents the total number of task working modes; t j σ represents the duration of operation of the tested unit in task mode j; j λ represents the failure frequency ratio of the tested unit in task operating mode j; j This represents the sum of the failure rates of each component of the unit under test in task working mode j. This indicates the failure rate of component a of the tested unit in task operating mode j.

[0036] Preferably, in step S4, the multi-task risk weight coefficient vector of each component of the tested unit in the multi-task working mode is represented as:

[0037]

[0038] Among them, the task weight coefficient vector H j =(h1,h2,...,h J ); The single-task risk weight coefficient vector of each component of the tested unit under task working mode j.

[0039] Preferably, in step S5, the fault detection weights of the test points corresponding to each component are represented as follows: Fault isolation weights are represented as

[0040] In the formula: d axThe element in the a-th row and x-th column of the D matrix model represents the element; Z represents the number of matrices to be analyzed during the calculation; k represents the k-th matrix; w a This represents the multi-task risk weighting coefficient of component a of the unit under test in the multi-task working mode.

[0041] Preferably, in step S7, the testability indicators include the fault detection rate and fault isolation rate of the unit under test.

[0042] The equipment testability design method for multi-task operating modes in this invention has the following beneficial effects:

[0043] The equipment testability design method of this invention calculates a multi-task risk weight coefficient vector, as well as fault detection weights and fault isolation weights, using a single-task risk weight coefficient vector and a task weight coefficient vector. This allows for the selection of test points for fault detection and fault isolation to establish a diagnostic strategy. Building upon existing testability design methods, this invention establishes a link between testability, reliability, and maintainability. It increases the risk weights of faults in each component of the unit under test under multi-task operating modes, enhances the fault detection and fault isolation weights of test points corresponding to high-risk components, and strengthens the focus on high-risk components. This results in higher reliability of testability indicators and better design quality, thereby improving the accuracy and comprehensiveness of equipment testability design. Attached Figure Description

[0044] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will now be described in further detail with reference to the accompanying drawings, wherein:

[0045] Figure 1 A logic block diagram for the design method for equipment testability;

[0046] Figure 2 This is the functional block diagram of the unit under test;

[0047] Figure 3 A graphical model illustrating the correlation of the unit under test;

[0048] Figure 4 This is a schematic diagram of the diagnostic tree for the unit under test. Detailed Implementation

[0049] The following detailed explanation illustrates the specific implementation methods:

[0050] Example:

[0051] This embodiment discloses a testability design method for equipment oriented towards multi-task working modes.

[0052] like Figure 1As shown, the equipment testability design method for multi-task operating modes includes the following steps:

[0053] S1: Establish the correlation diagram model and D matrix model of the unit under test;

[0054] S2: Calculate the single-task risk weight coefficient vector of each component of the unit under test in the single-task working mode;

[0055] S3: Calculate the corresponding task weight coefficient vector based on the working time and fault frequency ratio of each task working mode of the unit under test;

[0056] S4: Based on the task weight coefficient vector of the unit under test and the single task risk weight coefficient vector of each component, calculate the multi-task risk weight coefficient vector of each component in the multi-task working mode.

[0057] S5: Based on the multi-task risk weight coefficient vector of each component of the unit under test, calculate the fault detection weight and fault isolation weight of each component at the corresponding test point;

[0058] S6: Based on the correlation diagram model and D matrix model of the unit under test, as well as the fault detection weights and fault isolation weights of the corresponding test points of each component, select the test points for fault detection and the test points for fault isolation to establish a diagnostic strategy.

[0059] S7: Calculate testability indicators based on diagnostic strategies and complete the equipment testability design. Testability indicators include the fault detection rate and fault isolation rate of the unit under test.

[0060] The equipment testability design method of this invention calculates a multi-task risk weight coefficient vector, as well as fault detection weights and fault isolation weights, using a single-task risk weight coefficient vector and a task weight coefficient vector. This allows for the selection of test points for fault detection and fault isolation to establish a diagnostic strategy. Building upon existing testability design methods, this invention establishes a link between testability, reliability, and maintainability. It increases the risk weights of faults in each component of the unit under test under multi-task operating modes, enhances the fault detection and fault isolation weights of test points corresponding to high-risk components, and strengthens the focus on high-risk components. This results in higher reliability of testability indicators and better design quality, thereby improving the accuracy and comprehensiveness of equipment testability design.

[0061] Taking a specific unit under test in this embodiment as an example, its functional block diagram is as follows: Figure 2 As shown. The correlation diagram model established based on the functional block diagram of the unit under test and the available test points is as follows. Figure 3 As shown, where F a T represents the a-th component of the unit under test, where a = 1, 2, ..., A; xLet x represent the x-th test point, where x = 1, 2, ..., X. The D-matrix model established based on the functional block diagram of the unit under test and the available test points is shown in Table 1, where the a-th row matrix F... a =[d a1 ,d a2 ,...,d aX This indicates the relationship between the a-th component of the tested unit and each test point T. x (x = 1, 2, ..., X) correlation; matrix T in the x-th column x =[d 1x ,d 2x ,...,d Ax This indicates the relationship between the x-th test point and each component F of the unit under test. a (a = 1, 2, ..., A) correlation, where:

[0062]

[0063] Table 1 D-matrix model

[0064]

[0065] In the specific implementation process, the single-task risk weight coefficient vector is calculated through the following steps:

[0066] S201: Analyze the risk priority coefficient vector of each component of the unit under test in task operating mode j based on the FMECA table (obtained through existing means).

[0067] S202: For the risk priority coefficient vector RPN j In and Perform a binary comparison and construct a binary comparison matrix E of indicator importance based on the ranking scale. j ;

[0068] S203: Based on the binary contrast matrix E j Calculate the risk priority vector RPN j Importance Ranking Index And sorted by importance index Calculate the corresponding fuzzy judgment matrix M j ;

[0069] S204: Based on fuzzy judgment matrix M j Calculate the sum of the fuzzy membership degrees of each row. And sum the fuzzy membership degrees of each row After normalization, the risk weight coefficients of component a of the tested unit under task operating mode j are obtained. Then, the risk weight coefficients of each other component of the unit under test are calculated under task operating mode j, resulting in a single-task risk weight coefficient vector for each component of the unit under test under task operating mode j.

[0070] Specifically, risk priority coefficient vector

[0071] in,

[0072] In the formula: a = 1, 2, ..., A; A represents the total number of components of the unit being tested; This represents the risk priority coefficient of component a of the tested unit under task operating mode j. This indicates the ease or difficulty of testing component a after failure mode n occurs under task working mode j. This indicates the probability level of failure mode n occurring in component a under task operating mode j; This indicates the severity of the influence of component a on the unit under test under task working mode j, and the subscript n indicates the failure mode coefficient of the corresponding component. This indicates the working time of component a in task working mode j.

[0073] The binary contrast matrix is ​​represented as follows:

[0074] in, Greater than At that time, sorting scale equal At that time, sorting scale Less than At that time, sorting scale

[0075] In the formula: b = 1, 2, ..., A.

[0076] Importance ranking index is expressed as

[0077] The fuzzy judgment matrix is ​​represented as follows

[0078] The relative importance of fuzzy membership between any two components is represented as follows:

[0079] The sum of the fuzzy membership degrees of each row is represented as:

[0080] The risk weight coefficient of component a under task working mode j is expressed as follows:

[0081] The single-task risk weight coefficient vector of each component of the tested unit under task working mode j is represented as follows:

[0082] In practical implementation, the task weight coefficient vector is represented as follows:

[0083] Wherein, the task weight coefficient of task working mode j is expressed as

[0084]

[0085] In the formula: J represents the total number of task working modes; t j σ represents the duration of operation of the tested unit in task mode j; j λ represents the failure frequency ratio of the tested unit in task operating mode j; j This represents the sum of the failure rates of each component of the unit under test in task working mode j. This indicates the failure rate of component a of the tested unit in task operating mode j.

[0086] In specific implementation, the multi-task risk weight coefficient vector of each component of the tested unit in multi-task working mode is represented as follows:

[0087] Among them, the task weight coefficient vector H j =(h1,h2,...,h J ); The single-task risk weight coefficient vector of each component of the tested unit under task working mode j.

[0088] In the specific implementation process, the fault detection weights of the test points corresponding to each component are expressed as follows: Fault isolation weights are represented as

[0089] In the formula: d ax The element in the x-th column of the a-th row (i.e., the a-th component of the unit under test) in the D matrix model represents the element; Z represents the number of matrices to be analyzed during the calculation process; k represents the k-th matrix; w a This represents the multi-task risk weighting coefficient of component a of the unit under test in the multi-task working mode.

[0090] In the specific implementation process, firstly, a diagnostic matrix model is constructed based on the D matrix model and the fault detection weights and fault isolation weights of the corresponding test points of each component, as shown in Table 2. Then, test points for fault detection and test points for fault isolation are selected based on the diagnostic matrix model, and a diagnostic tree is drawn on this basis to form a diagnostic strategy.

[0091] Table 2 Diagnostic Matrix Model

[0092]

[0093] First, select the fault detection weight W based on the diagnostic matrix model in Table 2. FD The test point T8 with the largest value is used as the first test point for fault detection; after the matrix is ​​segmented, test point T7 is selected as the second test point for fault detection, at which point component F6 can be isolated;

[0094] Secondly, the fault isolation weight W is selected based on the diagnostic matrix model in Table 2. FI The largest test point T5 is used as the first test point for fault isolation; after dividing the matrix, test points T7 and T1 are selected as test points for fault isolation, so that the fault can be isolated to a single component.

[0095] Then, based on the first fault detection test point T8 and the second fault detection test point T7, as well as the first fault isolation test point T5, the fault isolation test points T7 and T1, a system is constructed. Figure 4 The diagnostic tree of the tested unit is shown to form a diagnostic strategy.

[0096] Finally, based on the established diagnostic strategy, the fault detection rate and fault isolation rate of the unit under test (UDT) are calculated to predict the testability of the UDT and achieve testability design. The calculations show that, considering reliability, the testability index of the UDT is: Fault Detection Rate λ FD =100%, fault isolation rate λ FI =75%, and the test design of the equipment for multi-task operation mode was finally completed.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described with reference to preferred embodiments, those skilled in the art should understand that various changes in form and detail can be made without departing from the spirit and scope of the invention as defined in the appended claims. Furthermore, common knowledge such as specific structures and characteristics known in the embodiments is not described in detail here. Finally, the scope of protection claimed by the present invention should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for testability design of equipment oriented to multi-task working mode, characterized in that, Includes the following steps: S1: Establish the correlation diagram model and D matrix model of the unit under test; S2: Calculate the single-task risk weight coefficient vector of each component of the unit under test in the single-task working mode; S3: Calculate the corresponding task weight coefficient vector based on the working time and fault frequency ratio of each task working mode of the unit under test; S4: Based on the task weight coefficient vector of the unit under test and the single task risk weight coefficient vector of each component, calculate the multi-task risk weight coefficient vector of each component in the multi-task working mode. S5: Based on the multi-task risk weight coefficient vector of each component of the unit under test, calculate the fault detection weight and fault isolation weight of each component at the corresponding test point; S6: Based on the correlation diagram model and D matrix model of the unit under test, as well as the fault detection weights and fault isolation weights of the corresponding test points of each component, select the test points for fault detection and the test points for fault isolation to establish a diagnostic strategy. S7: Calculate testability indicators based on diagnostic strategies and complete the testability design of the equipment.

2. The multi-task oriented working mode facing equipment testability design method of claim 1, wherein, In step S2, the single-task risk weight coefficient vector is calculated through the following steps: S201: based on the FMECA table, analyzing a risk priority vector of each component of the unit under test in the mission working mode ;​ S202: binary comparison is made on the risk priority coefficient vector in the vector and a binary comparison matrix is established on the importance of the indicators according to the ranking scale ; , , represents the total number of constituent components of the unit under test; represents the constituent components of the unit under test in the task working mode ;​ S203: based on the binary contrast matrix computing a risk priority coefficient vector importance ranking index and according to the importance ranking index computing a corresponding fuzzy judgment matrix ; S204: Based on fuzzy judgment matrix Calculate the sum of the fuzzy membership degrees of each row. And sum the fuzzy membership degrees of each row. Normalization is performed to obtain the constituent components of the unit under test. In task work mode Risk weighting coefficient ; Then, the other components of the unit under test are calculated in the task operation mode. The risk weight coefficients are used to obtain the risk weights of each component of the tested unit in the task operation mode. Single-task risk weight coefficient vector .

3. The equipment testability design method for multi-task operating modes as described in claim 2, characterized in that: In step S201, the risk priority coefficient vector ; in, ; In the formula: ; Indicates the total number of components in the unit being tested; Indicates the components of the unit under test In task work mode Risk priority coefficient below; Indicates the task working mode Lower components Failure modes The difficulty level of the test after the event; Indicates the task working mode Lower components Failure modes The probability level of occurrence; Indicates the task working mode Lower components Severity of impact on the unit under test, subscript This represents the failure mode coefficient of the corresponding component; Indicates the components In task work mode The following working hours.

4. The equipment testability design method for multi-task operating modes as described in claim 3, characterized in that: In step S202, the binary contrast matrix is ​​represented as follows: ; in, Greater than At that time, sorting scale ; equal At that time, sorting scale , ; Less than At that time, sorting scale , ; In the formula: .

5. The equipment testability design method for multi-task operating modes as described in claim 4, characterized in that: In step S203, the importance ranking index is expressed as: ; The fuzzy judgment matrix is ​​represented as follows ; The relative importance of fuzzy membership between any two components is represented as follows: .

6. The equipment testability design method for multi-task operating modes as described in claim 5, characterized in that: In step S204, the sum of the fuzzy membership degrees of each row is represented as: ; Components In task work mode The risk weight coefficient is expressed as follows: ; The components of the unit under test in the task operation mode The single-task risk weight coefficient vector is represented as follows: .

7. The equipment testability design method for multi-task operating modes as described in claim 6, characterized in that: In step S3, the task weight coefficient vector is represented as follows: ; Among them, task working mode The task weight coefficient is expressed as ; ； ； In the formula: Indicates the total number of task working modes; This indicates that the unit under test is in the task operation mode. Working hours within the period; This indicates that the unit under test is in the task operation mode. The failure frequency ratio; Indicates the task working mode The sum of the failure rates of each component in the unit under test; Indicates the components of the unit under test In task work mode The failure rate is as follows.

8. The equipment testability design method for multi-task operating modes as described in claim 7, characterized in that, In step S4, the multi-task risk weight coefficient vector of each component of the tested unit in the multi-task working mode is represented as follows: ； Among them, the task weight coefficient vector The components of the unit under test are in the task operation mode. Single-task risk weight coefficient vector .

9. The equipment testability design method for multi-task operating modes as described in claim 8, characterized in that: In step S5, the fault detection weights for the test points corresponding to each component are expressed as follows: The fault isolation weights are represented as ; In the formula: In the D matrix model, the first... Line number Column elements; This indicates the number of matrices to be analyzed during the calculation process; Indicates the first A matrix; Indicates the components of the unit under test Multi-task risk weighting coefficients for the unit under test in multi-task working mode.

10. The equipment testability design method for multi-task operating modes as described in claim 1, characterized in that: In step S7, the testability metrics include the fault detection rate and fault isolation rate of the unit under test.

Images