Aircraft airborne BIT system testability verification method based on correlation model

Abstract

The invention relates to an airplane airborne BIT system testability verification method based on a correlation model, and the method comprises the steps: carrying out the testability modeling of an object system, and obtaining a dependency relation model which describes the relation between a fault mode of the object system and a test method of the object system; based on the dependency relationship model, obtaining a BIT theoretical fault report set of each fault mode under all test methods by utilizing reachability analysis, and deducing an optimal diagnosis fault fuzzy group; constructing a fault sample library, verifying the BIT theoretical fault report set by using samples in the library, and adjusting the dependency relationship model according to a verification result; and performing fault simulation based on the verified dependency matrix, and calculating a total fault detection rate and a fault isolation rate of the object system according to a generated fault report. According to the method, the isolation rate index of the fault can be effectively verified in the incomplete and unformed stage of the fault diagnosis strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of testability computing, in particular to a method for verifying testability of an airborne BIT system based on a correlation model. BACKGROUND

[0002] With the wide application of digital systems composed of electronic hardware and software, the composition, structure and function of modern electronic systems such as airborne systems are becoming increasingly complex, which leads to great difficulties in the original maintenance scheme mainly using external separate detection and measurement equipment, and thus requires the functional system itself to have a self-test BIT capability, but also increases the complexity of system testability verification work.

[0003] Traditionally, testability verification work is performed in the product design finalization stage, a certain number of faults are injected into the developed product, fault detection and isolation are performed by using the test method specified by the testability design, the testability level of the product is estimated according to the results, and it is determined whether the specified requirements are met to decide whether to accept or reject.

[0004] However, in actual work, the traditional testability verification work faces the following problems

[0005] 1. The product design finalization stage often does not have complete, clear and reasonable fault diagnosis process, manual, document and equipment, which leads to the fact that this verification work can only verify the fault detection rate index and cannot verify the fault isolation rate index.

[0006] 2. During the product verification process, the actual fault isolation effect cannot be estimated when the product test conditions are close to the product use conditions, because the verification process is a black-box test process, which leads to the fact that the product is often tested in a hurry without meeting the verification pass conditions, resulting in repeated rework and prolonged test cycle. SUMMARY

[0007] The technical problem to be solved by the present application is to provide a method for verifying testability of an airborne BIT system based on a correlation model, which can effectively verify the fault isolation rate index in the stage when the fault diagnosis strategy is not complete and not formed.

[0008] The technical solution adopted by the present application to solve the technical problem is: a method for verifying testability of an airborne BIT system based on a correlation model, comprising:

[0009] testability modeling of the object system to obtain a dependency relationship model describing the relationship between fault modes of the object system and test methods thereof;

[0010] Based on the dependency relationship model, a BIT theoretical fault report set of each fault mode under all test methods is obtained by reachability analysis, and an optimal diagnosis fault fuzzy group is derived;

[0011] A fault sample library is constructed, the BIT theoretical fault report set is verified by using the samples in the library, and the dependency relationship model is adjusted according to the verification result;

[0012] Based on the verified dependency relationship model, fault simulation is performed, and the total fault detection rate and fault isolation rate of the object system are calculated according to the generated fault report.

[0013] Further, the testability modeling of the object system comprises:

[0014] Inputting multi-source monitoring data;

[0015] According to the fault transmission mode of the object system, a correlation matrix of fault modes and test methods is established;

[0016] The established correlation matrix is simplified to obtain a dependency relationship model.

[0017] Further, the correlation matrix of fault modes and test methods is established by taking fault modes as rows, test methods as columns, and the detection state of the test method to the fault as an element value.

[0018] Further, the simplification of the established correlation matrix comprises:

[0019] Merging the same columns in the correlation matrix;

[0020] Removing the corresponding columns of the test methods not selected in the correlation matrix.

[0021] Further, the BIT theoretical fault report set corresponding to each fault mode under all test methods is obtained by reachability analysis of the fault signatures in the dependency relationship model, and the fault diagnosis conclusion corresponding to each fault mode under all test methods is obtained as the BIT theoretical fault report set.

[0022] Further, after the step of fault simulation based on the verified dependency relationship model, the step of comparing the injected fault sample and its fault diagnosis conclusion to prompt whether the fault is successfully diagnosed and generating a corresponding diagnosis fault fuzzy group is further included.

[0023] Further, before the step of fault simulation based on the verified dependency relationship model, the step of determining the fault sample injection mode and the number of times to make the object system present the expected fault mode is further included.

[0024] Further, the construction of the failure sample library comprises:

[0025] According to the testability index and its confidence, the initial sample size is determined;

[0026] According to the occurrence rate of each failure mode and the inducing cause, the sample size is adjusted.

[0027] Advantages

[0028] Compared with the prior art, the present application has the following advantages and positive effects:

[0029] (1) The testability model is integrated in the testability verification work, so that the failure isolation rate index verification work can be carried out even when the failure diagnosis strategy is incomplete and not formed.

[0030] (2) The testability model is used to generate the verification criteria and conclusions of the failure verification work, so that the verification test can be pre-estimated, reviewed and modified before the formal verification work is carried out, and the rework of the verification test is reduced.

[0031] (3) The testability model is used to generate the verification criteria and conclusions of the failure verification work, so that the verification criteria can be predicted, the failure verification results can be automatically collected and judged by software, and the workload and error rate of manual result input are reduced. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 is a flowchart of an embodiment of the present application;

[0033] Figure 2 is an interaction schematic diagram of a flight control system and a diagnosis system of an embodiment of the present application;

[0034] Figure 3 is an example block diagram of a testability model of a flight control system of an embodiment of the present application;

[0035] Figure 4 is an example diagram of a correlation matrix of a flight control system of an embodiment of the present application;

[0036] Figure 5 is a failure injection schematic diagram of a flight control system of an embodiment of the present application. DETAILED DESCRIPTION

[0037] The present application will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present application and not used to limit the scope of the present application. In addition, it should be understood that after reading the content taught by the present application, those skilled in the art can make various modifications or changes to the present application, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0038] The embodiments of this invention relate to a testability verification method for an airborne BIT system based on a correlation model. By introducing a testability correlation model into the testability verification process of the airborne BIT system, it achieves the following characteristics: automatic generation of verification criteria and prediction of verification conclusions. For example... Figure 1 As shown, the steps are as follows:

[0039] Step 1: Input multi-source monitoring data, use testable modeling tools to perform testable modeling of the system to be verified, collect the fault propagation mode of the system, and analyze to obtain the correlation matrix. Merge identical columns in the correlation matrix and remove corresponding columns that were not selected for testing, thereby simplifying the correlation matrix and forming the dependency matrix of the system.

[0040] Step 2: Automatic generation of conclusion verification criteria: Traverse all failure modes of the testability model, use reachability analysis to analyze the corresponding dependency matrix failure propagation relationship, obtain the BIT failure report set corresponding to all failure modes, and the theoretically best diagnostic failure fuzzy group that these BIT failure report sets can achieve.

[0041] The method involves analyzing each fault signature in the dependency matrix to obtain the theoretically expected set of fault reports when injecting the fault sample. Based on the availability and accessibility of the fault report set, the method derives the fault isolation conclusion achievable under these circumstances. A fault signature refers to the feature vector of an aircraft fault, typically derived from offline data analysis by extracting state information to generate a static fault signature.

[0042] Step 3: Establish a fault sample library: Determine the initial sample size based on testability indicators, confidence levels, and other requirements; and allocate and supplement the sample size and establish the fault sample library based on the occurrence rate and triggering causes of each fault mode.

[0043] Step 4: Verification Conclusion Prediction / Verification Solution Review: Use the verification conclusion criteria from Step 2 to review the solution, that is, judge whether the diagnosis conclusion of each fault is correct one by one. If the judgment fails, the correlation model can be modified for adjustment until the diagnosis solution is approved.

[0044] Step 5: Fault Sample Injection: Based on the fault sample library, determine the fault sample injection method and number of times to simulate the fault and make the verification object exhibit the expected fault mode.

[0045] Step Six: The computer automatically records the fault report issued by the verification object, and automatically compares the fault diagnosis conclusion with the injection source, indicating whether the diagnosis was successful and the corresponding fuzzy group.

[0046] Step 7: The computer automatically summarizes the diagnostic results and calculates the relative failure probability of the system.

[0047] The invention will now be described in further detail in conjunction with the aircraft's flight control system.

[0048] Take an aircraft's flight control system as an example. For instance... Figure 2 As shown, the system contains multiple LRUs (Field Replaceable Units). The LRUs interact with each other and each has a built-in BITE (Browser In-System Test Equipment). Fault reports generated by each BITE are sent to the computer via a bus.

[0049] Utilize relevant technologies to establish its test model. Figure 3 This is a test block diagram example. FM represents the failure mode of the modeling object, TP represents the test method of the modeling object, and i and o represent the input and output of the modeling object, respectively.

[0050] The fault transmission patterns of the flight control system are collected, and a correlation matrix is ​​obtained through analysis. In the correlation matrix, if a logical relationship exists, it is represented by "1"; if no logical relationship exists, it is represented by "0". Figure 4 This is an example of a correlation matrix, where all test points equal to 0 represent no faults. Three modules are selected: the power supply module, the A-channel vertical rod command sensor, and the angular displacement sensor, to demonstrate the establishment of the correlation matrix.

[0051] By merging identical columns in the correlation matrix and removing corresponding columns of unselected tests, the correlation matrix is ​​simplified, forming the dependency matrix of the flight control system.

[0052] Each fault signature in the dependency matrix is ​​analyzed to obtain the set of fault reports that should theoretically be obtained when the fault is injected. Based on the availability and accessibility of the fault report set, the fault isolation conclusion that can be obtained in this case is deduced. For example, when the BIT reports a TP2 detection failure, it may be a single fault of FM1 and FM2 or a combination of the two. FM1 and FM2 are the fault fuzzy groups under TP2.

[0053] The obtained fault isolation conclusions are used as verification criteria to review the verification plan. That is, each fault diagnosis conclusion is judged to be correct. If the judgment fails, the correlation model can be modified to make adjustments until the diagnosis plan passes.

[0054] Typical faults in flight control systems include actuator faults, software faults, power supply faults, and interference faults. Based on the occurrence rate and triggering causes of each fault mode, the important events of system faults are ranked, and a fault sample library for flight control systems is established.

[0055] like Figure 5As shown, faults are injected into the flight control system's software and hardware through fault signal sources and fault excitation devices, thereby simulating the fault and causing the flight control system to exhibit the expected fault mode. BITE sends a fault report to the computer. The computer automatically compares the fault diagnosis conclusion with the injection source, indicating whether the fault diagnosis was successful and the corresponding fuzzy group.

[0056] The computer automatically summarizes the diagnostic results and calculates the relative failure probability of the system.

Claims

1. A test verification method for an aircraft airborne BIT system based on a correlation model, characterized in that, include: Testable modeling of the object system yields a dependency model describing the relationship between the failure modes of the object system and its test methods. Based on the dependency relationship model, reachability analysis is used to obtain the BIT theoretical fault report set for each fault mode under all test methods, and the optimal diagnostic fault fuzzy group is derived. A fault sample library is constructed, and the samples in the library are used to verify the BIT theoretical fault report set. The dependency relationship model is then adjusted based on the verification results. Fault simulation is performed based on the validated dependency relationship model, and the overall fault detection rate and fault isolation rate of the object system are calculated based on the generated fault reports.

2. The method according to claim 1, characterized in that, The test modeling of the object system includes: Input multi-source monitoring data; Based on the fault propagation mechanism of the object system, establish a correlation matrix between fault modes and testing methods; The dependency model is obtained by simplifying the established correlation matrix.

3. The method according to claim 2, characterized in that, The correlation matrix between fault modes and test methods is established by using fault modes as rows, test methods as columns, and the detection status of faults by the test methods as element values.

4. The method according to claim 2, characterized in that, The simplified correlation matrix includes: Merge identical columns in the correlation matrix; Remove the columns in the correlation matrix corresponding to test methods not selected.

5. The method according to claim 1, characterized in that, The method of obtaining the BIT theoretical fault report set corresponding to each fault mode under all test methods by utilizing reachability analysis is achieved by performing reachability analysis on each fault signature in the dependency relationship model to obtain the fault diagnosis conclusion corresponding to each fault mode under all test methods as the BIT theoretical fault report set.

6. The method according to claim 5, characterized in that, After the step of performing fault simulation based on the verified dependency relationship model, the method further includes comparing the injected fault samples and their fault diagnosis conclusions, indicating whether the fault was successfully diagnosed, and generating the corresponding diagnostic fault fuzzy group.

7. The method according to claim 1, characterized in that, Before the step of performing fault simulation based on the verified dependency model, the method and number of fault sample injections are determined so that the object system exhibits the expected fault mode.

8. The method according to claim 1, characterized in that, The construction of the fault sample library includes: The initial sample size is determined based on the testability metrics and their confidence levels; The sample size was adjusted based on the incidence rate of each failure mode and the triggering factors.

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