CN112596971A - Equipment testability prediction method based on simulation - Google Patents
Equipment testability prediction method based on simulation Download PDFInfo
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- CN112596971A CN112596971A CN202011526264.2A CN202011526264A CN112596971A CN 112596971 A CN112596971 A CN 112596971A CN 202011526264 A CN202011526264 A CN 202011526264A CN 112596971 A CN112596971 A CN 112596971A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
- G06F11/26—Functional testing
- G06F11/261—Functional testing by simulating additional hardware, e.g. fault simulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
- G06F11/2273—Test methods
Abstract
Disclosed is a simulation-based equipment testability prediction method, which includes: performing testability modeling on the equipment by analyzing the equipment structure to obtain a multi-signal flow diagram model and a correlation matrix of the equipment; analyzing the testability index of the equipment given by the correlation matrix by a Labview and Matlab mixed programming method, and verifying the testability index of the equipment, wherein the testability index comprises the allocation and the fault injection of a fault sample; sending a fault instruction to a simulator running Java through TCP communication during fault verification to a simulator module, and sending a fault injection instruction to an Agent in analog through TCP communication in Labview during testability verification; and (3) performing equipment component Agent simulation by using analog, selecting a corresponding verification scheme according to corresponding conditions, obtaining the fault sample distribution number of each module by inputting corresponding actual engineering data, and finishing corresponding fault simulation by the Agent. The method for predicting the testability of the equipment is more complete through testability modeling of the equipment, determination of a testability verification scheme and simulation of intelligent agent faults.
Description
Technical Field
The application belongs to the technical field of testability, and particularly relates to a simulation-based equipment testability prediction method.
Background
At present, testability refers to "a design characteristic that equipment can timely and accurately determine its status (workable, inoperable, or degraded) and effectively isolate its internal faults". The design method is an important design characteristic of the equipment which is convenient to test and diagnose, becomes an independent subject with the same importance as reliability and maintainability, and has important academic value and engineering guidance significance for developing testability design technology research. The method is one aspect of comprehensive guarantee capability of equipment and is important quality characteristic given to the equipment by design. At present, in the field of complex equipment design, testability design has attracted general attention of people. The good testability design not only can effectively improve the usability of the equipment, but also can greatly reduce the whole life cycle cost of the equipment. In the early stage of equipment design, the method for determining the system-level testability index is the basis for carrying out the system testability design. When the system has a fault, the built-in test BIT is used for detecting, the system detects the fault and isolates the fault to the LRU level under certain system level index fault detection rate FDR and fault isolation rate FIR, and the system works normally again after being repaired. However, the fault may be caused by BIT false alarm, and after being checked and repaired, the system returns to normal. And then judging whether the testability index meets the maximum availability under the condition of expense, if so, determining the testability index, and having great research value for both theory and practical application.
Disclosure of Invention
The application provides a simulation-based equipment testability prediction method, which can improve the test and guarantee capability of equipment.
According to an embodiment of the present application, there is provided a simulation-based equipment testability prediction method, including:
performing testability modeling of equipment, and acquiring a multi-signal flow diagram model of the equipment and a correlation matrix;
analyzing the correlation matrix to obtain testability indexes of the equipment, and analyzing implicit faults and false faults, wherein the testability indexes comprise fault detection rate, fault isolation rate, faults which cannot be detected, a redundancy test group and a fuzzy group;
sending a fault instruction to a simulator running Java during fault verification to a simulator module, and sending a fault injection instruction to an Agent in analog through TCP communication in Labview during testability verification;
and (3) performing equipment component agent simulation by using analog, and inputting corresponding actual engineering data to obtain the fault sample distribution number of each module of the equipment so as to complete corresponding fault simulation.
In the simulation-based equipment testability prediction method, a plurality of single signals and test points are superposed on the basis of an equipment structure functional block diagram to obtain the multi-signal flow diagram model according to the functions, the composition and the working principle of each part of the equipment, the connection relation among the parts and the signal flow direction.
In the simulation-based equipment testability prediction method, on the basis of the multi-signal flow graph model, the correlation relationship between all possible faults and all available tests in the multi-signal flow graph model is described in a Boolean matrix form, and the correlation matrix is obtained.
In the above method for predicting the testability of the simulation-based device, the method for determining the testability index is as follows: judging whether all zero rows exist in the correlation matrix, if so, determining that the fault modes corresponding to all zero rows are the faults which cannot be detected, and determining that the fault detection rate is the ratio of the residual detectable fault modes to all fault modes; deleting all zero rows to obtain a new matrix, judging whether the new matrix has column vectors with the same elements, and if so, taking the column vectors with the same elements as the redundancy test group; judging whether the row vectors with the same elements exist again on the basis of the new matrix, if so, determining that the fault modes corresponding to the row vectors with the same elements are fuzzy groups, and dividing all the row vectors with the same elements; and if not, calculating the fault isolation rate, wherein the fault isolation rate is the ratio of the fault modes corresponding to all the row vectors of the obtained final matrix to all the fault modes.
In the method for predicting the testability of the simulation-based equipment, two PCs are adopted, one PC is used as a testability platform machine, the other PC is used as the simulator, a fault instruction is sent to the simulator running Java through TCP communication during fault verification to the simulator module, and a fault injection instruction is sent to Agent in analog through TCP communication in Labview during testability verification.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings of the embodiments will be briefly described below.
Fig. 1 shows a multi-signal flow diagram according to an embodiment of the application.
FIG. 2 shows a schematic representation of Labview interfacing with analog using TCP communication according to an embodiment of the present application.
FIG. 3 illustrates a block diagram of an equipment testability prediction and verification system according to an embodiment of the present application.
Detailed Description
The application provides a simulation-based equipment testability prediction method, which comprises the following steps: firstly, obtaining a multi-signal flow graph model and a correlation matrix of equipment; secondly, analyzing a correlation relation by using Labview and Matlab mixed programming to obtain a testability index; then, the communication between the Labview and the analog is realized by utilizing TCP communication, so that a fault injection instruction can be sent to the analog from the Labview through the TCP communication; and finally, performing Agent simulation by using an analog simulator. And (4) simulating the intelligent agent to reflect whether the testability verification meets the requirements.
The simulation-based equipment testability prediction method of the present application can be applied to various electronic devices. The method of the present application will be described in detail below using a radar antenna as an example.
Step 1, determining functions, compositions and working principles of all parts of the radar antenna, connection relations among all parts and signal flow directions, and superposing a plurality of single signals and test points on the basis of a functional block diagram of the radar antenna structure to obtain a multi-signal model of the radar antenna, as shown in fig. 1. Wherein the relationship between the test points and the tests in FIG. 1 is shown in Table 1 below:
TABLE 1 relationship between test points and tests
On the basis of completing the multi-signal flow graph model, describing correlation relations between all possible faults and all available tests in the multi-signal flow graph model in a Boolean matrix form, and obtaining a correlation matrix.
And 2, analyzing the correlation matrix to obtain testability indexes of the equipment, and analyzing implicit faults and false faults, wherein the testability indexes comprise a fault detection rate, a fault isolation rate, faults which cannot be detected, a redundancy test group and a fuzzy group. The method for determining the testability index comprises the following steps:
step 2.1, judging whether all zero rows exist in the correlation matrix, if so, determining that the fault modes corresponding to all zero rows are the faults which cannot be detected, and determining that the fault detection rate is the ratio of the residual detectable fault modes to all fault modes;
step 2.2, deleting all zero rows to obtain a new matrix, judging whether the new matrix has column vectors with the same elements, and if so, taking the column vectors with the same elements as the redundancy test group;
step 2.3, on the basis of the new matrix, judging whether the row vectors with the same elements exist again, if so, determining the fault mode corresponding to the row vectors with the same elements as the fuzzy group, and dividing all the row vectors with the same elements; and if not, calculating the fault isolation rate, wherein the fault isolation rate is the ratio of the fault modes corresponding to all the row vectors of the obtained final matrix to all the fault modes. And after five testability indexes of the radar antenna are obtained, analyzing the hidden fault and analyzing the false fault.
And 3, adopting two PCs, wherein one PC is used as a testability platform machine, the other PC is used as a simulator, a txt document is used for storing data, in order to meet the communication between Labview and Java of the system during fault verification, a fault instruction is sent to the simulator running Java to the radar antenna simulator module through TCP communication as shown in figure 2, and a fault injection instruction is sent to Agent in analog through TCP communication in Labview during testability verification.
And 4, obtaining a fault model for testability simulation analysis and verification based on the multi-Agent system, normalizing the collected fault data through preset fault types and fault occurrence conditions, classifying the faults by using analog after simplification, and determining the types of the faults through color change. And simulating the real operation condition of the radar antenna component by calling a fault model, and generating a fault sample set.
Step 5, using analog to simulate the radar antenna component Agent, selecting a corresponding verification scheme according to corresponding conditions, adopting a proportional layered sampling method to distribute samples, firstly analyzing the structural hierarchy and fault rate of a test product, distributing the determined sample quantity to each component unit of the product according to the relative occurrence frequency of faults, then distributing the sample quantity of the component unit to the component unit by the same method, and obtaining the fault sample distribution number of each module by inputting corresponding actual engineering data, for example: considering the problem of actual engineering data accumulation of a certain radar antenna, the contract agreement between two parties is carried out to ensure that the risk alpha of a receiving party is 0.3, the risk beta of an ordering party is 0.3 and the design requirement value q is00.95 and the lowest acceptable value q10.85. The table look-up of the data yields the number of pass determinations C-1 and the number of samples n-20 in the testability verification scheme. Namely, 20 faults are injected into the virtual prototype, and the Agent completes corresponding fault simulation. And when the simulator operates normally, reading fault information and injecting faults, wherein whether the faults are injected successfully or not is used for reflecting whether the radar antenna testability design meets the requirements or not and determining a testability verification result.
Fig. 3 shows a system for implementing the simulation-based equipment testability prediction method, which includes a simulation machine, and a user management module, a testability prediction module, and a testability verification module on a testability platform machine, wherein functions to be implemented by the respective modules are referred to the above method section.
Claims (5)
1. A simulation-based equipment testability prediction method, comprising:
performing testability modeling of equipment, and acquiring a multi-signal flow diagram model of the equipment and a correlation matrix;
analyzing the correlation matrix to obtain testability indexes of the equipment, and analyzing implicit faults and false faults, wherein the testability indexes comprise fault detection rate, fault isolation rate, faults which cannot be detected, a redundancy test group and a fuzzy group;
sending a fault instruction to a simulator running Java during fault verification to a simulator module, and sending a fault injection instruction to an Agent in analog through TCP communication in Labview during testability verification;
and (3) performing equipment component agent simulation by using analog, and inputting corresponding actual engineering data to obtain the fault sample distribution number of each module of the equipment so as to complete corresponding fault simulation.
2. The method of claim 1, wherein the multi-signal flow graph model is obtained by superimposing a plurality of single signals and test points on the basis of a functional block diagram of an equipment structure according to functions, compositions and operating principles of components of the equipment, connection relations among the components, and signal flow directions.
3. The simulation-based equipment testability prediction method according to claim 2, characterized in that the correlation matrix is obtained by describing the correlation between all possible faults and all available tests in the multi-signal flow graph model in the form of a boolean matrix on the basis of the multi-signal flow graph model.
4. The simulation-based equipment testability prediction method of claim 2, wherein the testability indicator is determined by:
judging whether all zero rows exist in the correlation matrix, if so, determining that the fault modes corresponding to all zero rows are the faults which cannot be detected, and determining that the fault detection rate is the ratio of the residual detectable fault modes to all fault modes;
deleting all zero rows to obtain a new matrix, judging whether the new matrix has column vectors with the same elements, and if so, taking the column vectors with the same elements as the redundancy test group;
judging whether the row vectors with the same elements exist again on the basis of the new matrix, if so, determining that the fault modes corresponding to the row vectors with the same elements are fuzzy groups, and dividing all the row vectors with the same elements; and if not, calculating the fault isolation rate, wherein the fault isolation rate is the ratio of the fault modes corresponding to all the row vectors of the obtained final matrix to all the fault modes.
5. The simulation-based equipment testability prediction method according to claim 1, wherein two PCs are used, one as a testability platform and the other as the simulator, and wherein a fault instruction is sent to the simulator running Java through TCP communication to the simulator module during fault verification, and a fault injection instruction is sent to Agent in analog through TCP communication in Labview during testability verification.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103927259A (en) * | 2014-04-18 | 2014-07-16 | 北京航空航天大学 | Fault detection and isolation synthesis method based on testability modeling data |
| CN105786678A (en) * | 2014-12-25 | 2016-07-20 | 北京电子工程总体研究所 | Relevance model-based testability prediction method |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103927259A (en) * | 2014-04-18 | 2014-07-16 | 北京航空航天大学 | Fault detection and isolation synthesis method based on testability modeling data |
| CN105786678A (en) * | 2014-12-25 | 2016-07-20 | 北京电子工程总体研究所 | Relevance model-based testability prediction method |
Non-Patent Citations (1)
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| 陈丽影: "考虑不确定性的开关电源测试性设计研究", 《中国优秀硕士学位论文全文数据库》 * |
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