Fault detection modeling and detection rate calculation method based on SysML

Abstract

The invention discloses a fault detection modeling and detection rate calculation method based on SysML, and belongs to the technical field of testability. The method comprises the following steps: firstly, determining a failure mode needing to be tested of a system to be researched, product functions and performance parameters associated with the failure mode, a test point, a built-in test item and external test item information, and then establishing an initial function parameter cross-linking model consisting of product function composition and performance parameter composition of a structural unit by using a SysML module definition diagram; on the basis of the model, supplementing a fault mode and a cross-linking relation of a product, supplementing a relation between the fault mode and a test category and supplementing a test item to obtain a designed fault detection model, then calculating a fault detection rate of the fault detection model, judging whether the fault detection rate reaches the standard or not, outputting the fault detection model if the fault detection rate reaches the standard, and otherwise, continuing to adjust. According to the invention, the normalization and consistency of fault detection modeling are ensured, the obtained detection scheme can improve the efficiency and accuracy of fault detection, and the requirements of a high-complexity system are met.

Description

Technical Field

[0001] The present invention belongs to the technical field of testability, and in particular relates to a test fault detection modeling and detection rate calculation method based on SysML. Background Art

[0002] Fault refers to damage or disturbance of one or more links or components in the system, which causes the output parameters to exceed the acceptable range, thus causing the system performance to decline, the function to fail or even stop working, and the scheduled engineering task to be unable to be completed. Fault detection is a key link in system process monitoring and a key means to ensure the safe and efficient operation of the system. By accurately identifying and locating system faults, it can help staff to make timely judgments and take corresponding measures to avoid system out of control. This is crucial to ensure the stable operation of the system.

[0003] SysML (Systems Modeling Language) is a standard system modeling language that aims to eliminate the differences in expression and terminology between different modeling languages ​​and standardize symbols and semantics. SysML supports the description, analysis, design and verification of complex system integration architectures composed of software, hardware, data and personnel. As a graphical modeling language, SysML can visualize important aspects of system design such as structure, behavior, requirements and parameters, helping engineers deal with increasingly complex system problems and promoting interdisciplinary communication among system engineers.

[0004] As the speed of equipment replacement accelerates, the requirements for equipment are becoming more and more stringent, and the complexity of equipment is constantly increasing. Although traditional fault detection methods, such as those based on manual monitoring, expert knowledge, and analytical models, are relatively mature, their efficiency and accuracy are increasingly difficult to meet the needs of actual product equipment research and development. In response to increasingly complex and dynamically changing modern systems, a more systematic, dynamic, and efficient fault detection modeling and evaluation method is urgently needed to address the challenges and limitations of existing technologies. Summary of the invention

[0005] In order to meet the above needs and solve the problems existing in the existing fault detection technology, a fault detection modeling and detection rate calculation method based on SysML is proposed. The fault detection design modeling elements and their expression specifications are determined by analysis, and a functional parameter cross-linking relationship diagram is established. On this basis, a fault detection model is established, and the test category of each fault mode is determined. The threshold judgment of the software method is added, and the collected function / performance parameters are judged to determine whether they are out of limit or normal / faulty. According to the fault detection model, the fault detection rate is calculated.

[0006] A fault detection modeling and detection rate calculation method based on SysML of the present invention comprises the following steps:

[0007] Step 1: Determine the fault detection factor information of the system to be studied. The fault detection factors include the fault mode to be tested, the product functions and performance parameters associated with the fault mode, the test points, the internal test items, and the external test items;

[0008] Step 2: Use SysML module definition diagram to establish the initial functional parameter cross-linking model of product functional composition and performance parameter composition of each structural unit;

[0009] Step 3: Expand the initial functional parameter cross-linking model of each structural unit of the product based on the failure mode information obtained in step 1, and supplement the failure mode and cross-linking relationship of the product;

[0010] Step 4: Add test categories to the functional parameter cross-linking model obtained in step 3. The test categories include built-in test BIT and external test. According to the severity attribute and failure rate of the failure mode, establish the relationship between the failure mode and the test category.

[0011] Step 5: Based on the information of the internal test items and external test items obtained in step 1, add test items to the functional parameter cross-linking model obtained in step 4, establish the relationship between the test items and product functions, performance parameters, and failure modes, and ensure that each test item can cover the relevant product functions and performance parameters;

[0012] Step 6: Calculate the fault detection rate of the fault detection model obtained in step 5 to determine whether it meets the standard. If it does, output the fault detection model. Otherwise, continue to adjust the fault detection model by steps 3 to 5 until the fault detection rate meets the standard.

[0013] The method for constructing the initial functional parameter cross-linking model in step 2 includes:

[0014] Step 21, for each structural unit, a module definition diagram is established according to the cross-linking relationship between product functions and performance parameters;

[0015] Step 22, construct the expression of the function item: customize the "Function" block element, name the block after the function, and set the importance attribute, including important and general;

[0016] Step 23, construct interface parameters by definition or link dragging. The expression of interface parameters is: customize the "parameter" block element and name the block with the parameter name and identifier;

[0017] Step 24: establishing a distribution relationship between the function item and the interface parameter of the structural unit.

[0018] In the step 3, the expression of the failure mode is established: the "failure mode" block element is customized, the block is named with the failure mode number, and the attributes of the failure mode are set to include the failure rate and severity of the failure mode; and then the allocation relationship is used to connect the failure mode with the product function and performance parameters.

[0019] In the step 4, the expression of the test category is established: a "test category" block element is customized, and the block is named after the test category; and then the allocation relationship is used to connect the fault mode and the test category.

[0020] In the step 5, a test item is established: a "test item" block element is customized, the block is named with the name of the internal test item or the external test item, and the properties of the test item are set; and then the test item is supplemented using the allocation relationship.

[0021] Compared with the prior art, the advantages and positive effects of the present invention are:

[0022] (1) The method of the present invention performs fault detection modeling and evaluation based on SysML, providing a standardized and systematic modeling method. The application of SysML eliminates the differences in expression and terminology among different model languages, thus ensuring the standardization and consistency of fault detection modeling.

[0023] (2) The method of the present invention improves the efficiency and accuracy of fault detection by establishing a functional parameter cross-linking relationship diagram and a fault association diagram model, thereby meeting the requirements of high-complexity systems.

[0024] (3) The method of the present invention ensures comprehensive coverage of fault detection by designing fault test categories such as built-in self-test (BIT) and external test, and dividing specific test items; BIT and external test are reasonably allocated according to the severity attribute of the fault mode, thereby improving the coverage and reliability of fault detection.

[0025] (4) The method of the present invention realizes quantitative evaluation of fault detection capability through fault detection rate evaluation; by calculating BIT and the total fault detection rate, the effectiveness of the detection method can be intuitively evaluated, providing a basis for subsequent optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of the fault detection modeling and detection rate calculation method based on SysML of the present invention;

[0027] Figure 2 It is a schematic diagram of a fault detection scheme design according to an embodiment of the present invention;

[0028] Figure 3 is an initial functional parameter cross-linking model diagram constructed in an embodiment of the present invention;

[0029] Figure 4 This is a diagram of a supplementary fault association relationship model according to an embodiment of the present invention;

[0030] Figure 5 This is a model diagram of additional test categories according to an embodiment of the present invention;

[0031] Figure 6 This is a model diagram of additional test items in an embodiment of the present invention;

[0032] Figure 7 The figure is a flow chart of calculating the fault detection rate according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0034] like Figure 1 As shown, the fault detection modeling and detection rate calculation method based on SysML implemented in the embodiment of the present invention includes the following six steps. Figure 2 As shown, a fault association diagram is constructed based on the constructed functional parameter cross-linking relationship diagram, and fault modes and cross-linking relationships, fault test categories, BITs and external test items are added, and finally fault detection modeling and detection rate calculation based on SysML are realized.

[0035] Step 1: Determine the fault detection elements of the system to be studied. The fault detection elements mainly include: the fault mode to be tested, the function / performance parameters associated with the fault mode, the test point, the internal test, and the external test. The embodiment of the present invention designs these elements as follows.

[0036] (1) Statistical failure modes. Failure modes refer to the specific manifestations of failures and are the core goal of fault detection. Usually, in the preliminary design stage of a product, the failure modes are sorted out and analyzed through hardware FMECA (failure mode, effects and criticality analysis). The method of the present invention obtains relevant failure mode information from the FMECA report for modeling. Failure mode information includes failure mode number, failure mode description (i.e., failure mode name), impact level, possible causes, detection methods, failure rate, and severity.

[0037] An example of a specific failure mode obtained through a hardware FMECA report in an embodiment of the present invention is shown in Table 1. The acquired failure modes include but are not limited to component failure, loose connection, sensor failure, and signal interference.

[0038] Table 1 Failure mode related information

[0039] Serial number Failure Mode Number Failure Mode Description Impact Level Possible causes Fault Detection Methods Failure rate Severity 1 FM001 Spindle break high Material fatigue, overload Vibration detection, stress analysis 0.96％ Ⅰ 2 FM002 Gear crack high Wear and impact Gear mesh analysis, sound monitoring 0.72％ Ⅱ 3 FM003 Oil contamination middle Contaminants mixed in Oil analysis, viscosity testing 0.48％ Ⅲ 4 FM004 The pump body is not sealed tightly middle Seal failure Pressure testing, leak detection 1.14％ Ⅰ 5 FM005 Temperature is too high high Poor heat dissipation, overload operation Temperature monitoring, thermal imaging detection 0.62％ Ⅱ

[0040] (2) Determine the functional / performance parameters associated with the failure mode. After the failure mode occurs, it usually affects the functional or performance parameters of the product. Therefore, testing these functional or performance parameters is the basis for achieving fault detection.

[0041] Functional and performance parameters are key indicators that need to be monitored and analyzed during normal operation and fault conditions of the system. Obtaining these parameters can identify the specific manifestations of the fault when it occurs, and effectively detect and locate the fault. These parameters can directly reflect the health status of the system and provide an important basis for fault detection.

[0042] The present invention obtains the relevant function / performance parameter information of the product to be tested, and it is necessary to determine the relevant information table of the product function and the relevant information table of the product performance parameter. The relevant information of the product function of the present invention includes the function number, function name, function description, relevant performance parameters and remarks information, and some product function information is shown in Table 2. The relevant information of the product performance parameters of the present invention includes information such as parameter name, unit of measurement, relevant failure mode number (i.e., the failure mode number affecting Table 3), normal range of parameter value and parameter detection method, and some product performance parameters are shown in Table 3.

[0043] Table 2 Product function related information

[0044]

[0045] Table 3 Product performance parameters related information

[0046] Serial number Parameter name unit Impact Failure Mode Number Normal range Detection Methods 1 Operating temperature ℃ FM005 -20～70 Temperature Sensor 2 Speed km / h FM001,FM002 0～200 Vehicle speed sensor 3 Lubricating oil pressure Pa FM004 1.5～3.0 Oil pressure sensor 4 Transmission oil temperature ℃ 50～120 Oil temperature sensor 5 Control oil pressure Pa FM004 5.0～7.0 Oil pressure sensor 6 Fan speed rpm FM004 1000～3000 Speed ​​sensor 7 Oil quality ％ FM003 0~10(pollutant) Oil Analyzer

[0047] (3) Set test points. Test points are electrical connection points in the unit under test, including various connection points for signal measurement, input test stimulus, and control signals. The parameters to be measured and the measurement locations or circuit nodes are obtained based on the input and output signals and functional characteristics of the unit under test and its component units.

[0048] The test point is a specific location in the system for collecting data and monitoring signals. The selection of test points should cover all key functions and performance parameters to ensure that the failure mode can be fully detected. The layout of the test points should be scientific and reasonable so as to accurately and quickly obtain the system operation status information. The relevant information of some test points set in the embodiment of the present invention is shown in Table 4. The test point information includes information such as the test point number, test point description, test parameters, and test equipment.

[0049] Table 4 Test point related information

[0050] Serial number Test point number Test point description Test Parameters Test equipment 1 TP001 Temperature sensor connection point Operating temperature Temperature Sensor 2 TP002 Vehicle speed sensor connection point Speed Speed ​​Sensor 3 TP003 Oil pressure sensor connection point Lubricating oil pressure Oil pressure sensor 4 TP004 Transmission oil temperature sensor connection point Transmission oil temperature Oil temperature sensor 5 TP005 Control oil pressure sensor connection point Control oil pressure Oil pressure sensor 6 TP006 Fan speed sensor connection point Fan speed Speed ​​sensor

[0051] (4) Design built-in test. BIT refers to the testing capability provided within the system or equipment to automatically detect and isolate faults.

[0052] The related information of BIT includes BIT number, BIT description, test object (function / parameter), test point number and related test object name or number, etc. As shown in Table 5, it is the specific information of some BITs in the embodiment of the present invention.

[0053] Table 5 Related information of in-machine test

[0054] Serial number BIT Number BIT Description Test object (function / parameter) Test point number Test object name 1 BIT001 Operating temperature BIT parameter TP001 Operating temperature 2 BIT002 Speed ​​BIT parameter TP002 Speed 3 BIT003 Lubricating oil pressure BIT parameter TP003 Lubricating oil pressure 4 BIT004 Transmission oil temperature BIT parameter TP004 Transmission oil temperature 5 BIT005 Operating oil pressure BIT parameter TP005 Control oil pressure 6 BIT006 Fan speed BIT parameter TP006 Fan speed

[0055] (5) Set up external testing. External testing refers to testing for detecting and isolating faults outside the system through testing instruments, tools and equipment, including external automatic testing and manual testing. Relevant information of external testing includes external test number, test description, test type and test equipment. Test type refers to manual testing or external automatic testing. A specific external test item information of an embodiment of the present invention is shown in Table 6.

[0056] Table 6 External test related information

[0057] Serial number External test number Test Description Test Type Test equipment 1 ET001 Oil quality testing Manual testing Oil Analyzer

[0058] Step 2: Establish an initial functional parameter cross-linking model. In order to achieve accurate modeling of fault detection, it is necessary to establish an initial functional parameter cross-linking model of the functional composition and performance parameter composition of each layer of structural unit. The initial functional parameter cross-linking model can clearly show the relationship between each functional module and performance parameter of the system, and provide basic data support for subsequent fault detection modeling.

[0059] like Figure 3 As shown, the functional parameter cross-linking model of the integrated transmission device of the amphibious vehicle is shown. The initial functional parameter cross-linking model of the functional composition and performance parameter composition of each layer of structural unit is described by using the Block Definition Diagram (BDD) of SysML. Figure 3 The main functions of the transmission device and the corresponding performance parameters are described. The specific modeling rules of the initial functional parameter cross-linking model are as follows:

[0060] (1) According to the object design data, for each structural unit, a module definition diagram reflecting the cross-linking relationship between functions and performance parameters is established; in the embodiment of the present invention, the information in Table 2 and Table 3 is obtained, and then the module definition diagram is established according to the association relationship between functions and parameters.

[0061] (2) Function items are constructed by definition. The expression of function items is as follows: customize the "Function" block element, name the block after the function, and set the importance attributes including: important, general.

[0062] (3) Construct interface parameters by definition or link dragging. The expression of interface parameters is: customize the "parameter" block element and name the block with the parameter name and identifier.

[0063] (4) Establish the allocation relationship between the function item and the unit interface parameter.

[0064] In BDD, the relationship between functional items and performance parameters is represented by connectors. These relationships reflect the performance parameters that each functional item depends on for implementation.

[0065] By establishing the initial function parameter cross-linking model, the interdependence between various function items and performance parameters is systematically analyzed, providing basic data for subsequent fault detection modeling. In this way, it can ensure that the relevant function items and performance parameters can be quickly located when a fault occurs, thereby improving the efficiency of fault detection and repair.

[0066] Step 3: Add fault association relationship.

[0067] On the basis of the above initial functional parameter cross-linking model, the fault association relationship is supplemented to establish a comprehensive cross-linking relationship between the internal functional composition, performance parameters and fault modes of the structural unit. The specific modeling rules are as follows:

[0068] (1) Selected product layer: Expand the initial functional parameter cross-linking model of each structural unit at this level and supplement the failure mode and cross-linking relationship of the product.

[0069] (2) Establish failure mode information: The failure mode required for testability design is expressed as follows: customize the "failure mode" block element, name the block with the failure mode number or name, and set the block attributes to include the failure rate attribute (unit 10-6 / h) and severity attribute (I, II, III, IV).

[0070] (3) The extended fault cross-linking relationship is as follows:

[0071] The fault does not affect the function or the interface performance parameters: No connection.

[0072] The fault clearly affects the interface performance parameters: Use the allocation relationship to connect the fault mode and the interface performance parameters.

[0073] Fault impact function: Use the assignment relationship to connect the fault mode and the function block.

[0074] like Figure 4 As shown, it shows the Figure 3 The model is supplemented with the fault correlation relationship. The relevant fault mode information is obtained from Table 1. Figure 3The functional parameter cross-linking relationship diagram in the system is expanded, and the fault mode is connected with the functional and performance parameters using the allocation relationship to clarify the relationship between them. By adding fault association relationships, we can more clearly understand how faults affect different parts of the system, and systematically analyze the impact of fault modes on functional items and performance parameters, which helps to formulate effective fault detection and isolation strategies and improve system reliability and maintenance efficiency.

[0075] Step 4: Add test categories.

[0076] Based on the model in step 3 above, we further add fault test categories (BIT, external test). Common test categories include built-in test (BIT) and external test. The specific properties of each BIT and external test are clarified. By reasonably allocating these test categories, the comprehensiveness and reliability of fault detection can be improved. The modeling rules are as follows:

[0077] (1) Expand the model in step 3 and add test categories for failure modes.

[0078] (2) Establish the relationship between failure modes and test categories: Use the allocation relationship to connect failure modes and test categories.

[0079] (3) Test category expression: Customize the "Test Category" block element and name the block with the test category name.

[0080] (4) Establish the relationship between failure modes and test categories according to the following principles:

[0081] Failure modes with severity levels Ⅰ and Ⅱ should be assigned to BIT test categories;

[0082] Evaluate BIT fault test coverage. If the requirements are met, the remaining fault modes are assigned as external tests.

[0083] If the requirements are not met, the failure modes with severity level III are sorted from high to low in terms of failure rate, and added to the set of failure modes to be tested in sequence, and the BIT failure test coverage is calculated synchronously until the requirements are met;

[0084] If all the fault modes with severity III are added to the set of fault modes to be tested, but the BIT fault test coverage requirement is still not met, the fault modes with severity IV are sorted from high to low in terms of failure rate, and added to the set of fault modes to be tested in sequence, and the BIT fault test coverage is calculated synchronously until the requirement is met;

[0085] The remaining failure modes are classified as failure modes covered by external tests.

[0086] like Figure 5 As shown, it is Figure 4 The model after adding test categories on the basis of the above, through this figure, we can see the specific relationship between the fault mode and the test category. In the embodiment of the present invention, the characteristics of each fault mode are first analyzed to determine its test requirements, and then according to the severity attribute and failure rate of the fault mode, it is decided whether to use BIT test or external test; the fault mode is classified according to the test requirements, and BIT test and external test categories are designed respectively. BIT test is usually used to detect fault modes with higher severity and higher frequency of occurrence, while external test is used to detect other fault modes. These test categories need to design their specific test attributes respectively. Finally, the association relationship between the test category and the fault mode is established, and the fault mode is connected to the corresponding test category using the allocation relationship. By designing the fault test category and ensuring that each fault mode has a suitable test method, the comprehensiveness and efficiency of fault detection can be improved, and faults can also be effectively discovered and isolated to ensure the stable operation of the system.

[0087] Step 5: Add test items. After determining the fault test category, the next step is to further refine and determine the specific fault test items. The test items need to consider the specific requirements of each test category (such as BIT test and external test) to ensure that the test items can fully cover all fault modes and effectively detect and locate faults. After adding test items to the functional parameter cross-linking model, the fault detection model is obtained.

[0088] Based on the model in step 4 above, specific fault test items are further added, including specific BIT test items and external test items. The specific modeling rules are as follows:

[0089] (1) Expand on the model in step 4 and add specific test items.

[0090] (2) Test item definition: Customize the "Test Item" block element, name the block with the BIT item name or the external test item name, BIT category attributes: POBIT, CBIT, PBIT, etc., external test category attributes: ATE, manual, etc. The BIT item name or external test item name here is determined according to the test number or test description in Table 5 or Table 6.

[0091] (3) Use the allocation relationship to establish an association with the test item. The principles are as follows:

[0092] If the test item is for interface parameters, establish the allocation relationship between the corresponding performance parameters and the test item.

[0093] If the test item is for a function, establish the allocation relationship between the corresponding product function and the test item.

[0094] If the test item is for a fault, establish the allocation relationship between the corresponding fault mode and the test item.

[0095] like Figure 6 As shown, it is Figure 5 The model after adding test items to the model. Through this figure, you can see the specific test items under each test category, as well as their specific relationship with system functions, performance parameters and failure modes. The embodiment of the present invention first obtains the BIT test item information from Table 5 and the external test item information from Table 6, and then establishes the relationship between the test items and functions / parameters / failures, and uses the allocation relationship to connect the test items with related functional items, performance parameters and failure modes to ensure that each test item can effectively cover its related functions and parameters. By determining specific test items, it is ensured that the detection content under each test category is detailed and comprehensive, thereby improving the coverage and accuracy of fault detection. This helps to quickly and accurately detect and locate faults when they occur, and improve the reliability and maintenance efficiency of the system.

[0096] Step 6: Calculate the fault detection rate. The fault detection rate is defined as the ratio of the number of faults correctly detected by the specified method within a specified time to the total number of faults that occur in the unit under test. FD The calculation is as follows:

[0097]

[0098] where λ D is the total failure rate of the failure modes that can be detected, λ is the total failure rate of all failure modes; i is the failure rate of the ith failure mode; Di is the failure rate of the detected i-th fault mode. The failure rates of each fault model can be obtained from Table 1.

[0099] If the fault detection rate is required, first obtain the fault mode failure rate according to the fault detection model, then obtain the fault mode failure rate that can be detected by BIT according to the fault test category design results, and calculate the BIT fault detection rate through the ratio; obtain the fault mode failure rate that can be detected by external testing, add it to the fault mode failure rate that can be detected by BIT, and calculate the total fault detection rate through the ratio. The fault detection model can be further adjusted based on the total fault detection rate to obtain the desired fault detection rate.

[0100] The fault detection rate is used to evaluate the effectiveness of the fault detection method, that is, the ratio of the number of faults correctly detected by a specific method to the total number of faults that occurred in the unit under test within a specified time. Calculating the fault detection rate can help optimize the fault detection strategy and improve the reliability and maintenance efficiency of the system. Figure 7 As shown, the specific fault detection rate calculation process of the embodiment of the present invention is as follows:

[0101] (1) Obtaining the failure mode failure rate: According to Figure 6 Fault detection model to collect failure rate data of each failure mode;

[0102] (2) Calculate the BIT fault detection rate: Based on the design results of the fault detection model, determine the fault rate of the fault mode that can be detected by the BIT test, and calculate the BIT fault detection rate. Figure 7 As shown, the test item is taken as the starting node and the fault mode is taken as the target node. In the designed fault detection model, the paths from each starting node to each target node are traversed, and the failure rates of the target nodes of the paths whose starting nodes are BIT test items are accumulated. Let the sum of the failure rates of all fault modes based on Table 1 be C, the sum of the failure rates of the fault modes that can be detected by the statistical BIT items be A, and calculate the BIT fault detection rate = A / C.

[0103] (3) Calculate the external test fault detection rate: Determine the failure rate of the failure mode that can be detected by the external test.

[0104] (4) Calculate the total fault detection rate: add up the fault detection rates of BIT and external tests to calculate the total fault detection rate. The embodiment of the present invention directly calculates the external test fault detection rate and the BIT fault detection rate to obtain the total fault detection rate, such as Figure 7 As shown. Assume that the sum of the failure rates of all detectable failure modes is B, and calculate the total failure detection rate = B / C.

[0105] (5) Output evaluation results: Summarize and output the calculation results to evaluate the coverage and effectiveness of fault detection. The following is an example of calculating the fault detection rate.

[0106] The following data can be obtained according to the designed fault detection model in the embodiment of the present invention:

[0107] All failure modes and their failure rates are as follows:

[0108] Serial number Failure Mode Number Failure Mode Description Failure rate 1 FM001 Spindle break 0.96％ 2 FM002 Gear crack 0.72％ 3 FM003 Oil contamination 0.48％ 4 FM004 The pump body is not sealed tightly 1.14％ 5 FM005 Temperature is too high 0.62％

[0109] Among them, the failure modes and failure rates that can be detected by BIT testing are as follows:

[0110] Serial number Failure Mode Number Failure Mode Description Failure rate 1 FM001 Spindle break 0.96％ 2 FM002 Gear crack 0.72％ 4 FM004 The pump body is not sealed tightly 1.14％ 5 FM005 Temperature is too high 0.62％

[0111] The failure modes and their failure rates that can be detected by external testing are as follows:

[0112] Serial number Failure Mode Number Failure Mode Description Failure rate 3 FM003 Oil contamination 0.48％

[0113] The BIT fault detection rate is calculated as follows:

[0114]

[0115] The total fault detection rate is calculated as follows:

[0116]

[0117] Finally, the calculation results are output: BIT fault detection rate = 85.45%; total fault detection rate = 100%.

[0118] Through the above calculations, the effectiveness of the fault detection strategy can be evaluated, providing data support for optimizing the fault detection method. This helps to improve the reliability and maintenance efficiency of the system in practical applications.

[0119] Except for the technical features described in the specification, all of them are known technologies to those skilled in the art. The present invention omits the description of known components and known technologies to avoid redundancy and unnecessary limitation of the present invention. The implementation methods described in the above embodiments do not represent all implementation methods consistent with the present application. On the basis of the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative labor are still within the protection scope of the present invention.

Claims

1. A fault detection modeling and detection rate calculation method based on SysML, characterized in that: The steps include: Step 1: Determine the fault detection factor information of the system to be studied. The fault detection factors include the fault mode to be tested, the product functions and performance parameters associated with the fault mode, the test points, the internal test items, and the external test items; Step 2: Use SysML module definition diagram to establish the initial functional parameter cross-linking model of product functional composition and performance parameter composition of each structural unit; Step 3: Expand the initial functional parameter cross-linking model of each structural unit of the product based on the failure mode information obtained in step 1, and supplement the failure mode and cross-linking relationship of the product; Step 4: Add test categories to the functional parameter cross-linking model obtained in step 3. The test categories include built-in test BIT and external test. According to the severity attribute and failure rate of the failure mode, establish the relationship between the failure mode and the test category. Step 5: Based on the information of the internal test items and external test items obtained in step 1, add test items to the functional parameter cross-linking model obtained in step 4, establish the relationship between the test items and product functions, performance parameters, and failure modes, and ensure that each test item can cover the relevant product functions and performance parameters; Step 6: Calculate the fault detection rate of the fault detection model obtained in step 5 to determine whether it meets the standard. If it does, output the fault detection model for fault detection of the system to be studied. Otherwise, continue to adjust the fault detection model from steps 3 to 5 until the fault detection rate meets the standard.

2. The method according to claim 1, characterized in that The step 1 comprises: Step 11, statistically analyze the failure modes through the hardware FMECA report, and record the failure mode information, including failure mode number, failure mode description, impact level, possible cause, failure detection method, failure rate and severity; FMECA stands for failure mode, impact and criticality analysis; Step 12, determining product function information associated with the failure mode, including function number, function name, function description and related performance parameters; determining performance parameter information associated with the failure mode, including parameter name, measurement unit, related failure mode number, normal range of parameter value and parameter detection method; Step 13, setting the test points in the system for collecting data and monitoring signals, the test point information includes the test point number, test point description, test parameters and test equipment; Step 14, design BIT items. BIT information includes BIT number, BIT description, test object, test point number and related test objects. Test object refers to product function or performance parameters. Step 15, designing external test items. The information of the external test includes the external test number, test description, test type and test equipment. The test type refers to manual test or external automatic test.

3. The method according to claim 1 or 2, characterized in that: The method for constructing the initial functional parameter cross-linking model in step 2 includes: Step 21, for each structural unit, a module definition diagram is established according to the cross-linking relationship between product functions and performance parameters; Step 22, construct the expression of the function item: customize the "Function" block element, name the block after the function, and set the importance attribute, including important and general; Step 23, construct interface parameters by definition or link dragging. The expression of interface parameters is: customize the "parameter" block element and name the block with the parameter name and identifier; Step 24: establishing a distribution relationship between the function item and the interface parameter of the structural unit.

4. The method according to claim 1 or 2, characterized in that: In step 3, the method of supplementing the failure modes and cross-linking relationships of the products includes: Step 31, establish the expression of the failure mode: customize the "failure mode" block element, name the block with the failure mode number, and set the attributes of the failure mode including the failure rate and severity of the failure mode; Step 32, using the allocation relationship to connect the failure mode with the product function and performance parameters according to the following principles: (1) If the fault does not affect the product function or the interface performance parameters, the connection will not be established; (2) If the fault clearly affects the performance parameters, the corresponding fault mode and interface parameters are connected using the allocation relationship; (3) If the fault affects the product function, the corresponding fault mode and function block are connected by the allocation relationship.

5. The method according to claim 1 or 2, characterized in that: The step 4 comprises: Step 41, establish the expression of the test category: customize the "test category" block element and name the block with the test category name; Step 42, according to the following principles, use the allocation relationship to connect the failure mode and the test category: (1) Allocate the failure modes with severity levels I and II to the test categories of BIT; (2) If the fault test coverage requirement of BIT is currently met, set the remaining fault modes to external tests; (2) If the BIT fault test coverage requirement is not met at present, the fault modes with severity level III are sorted from high to low according to the failure rate, and added to the set of fault modes to be tested in sequence, and the fault test coverage of the BIT is calculated synchronously until the requirement is met; (3) If all the fault modes with severity level III are added to the set of fault modes to be tested, but the BIT fault test coverage requirement is still not met, the fault modes with severity level IV are sorted from high to low according to the failure rate, and added to the set of fault modes to be tested in turn, and the fault test coverage is calculated synchronously until the requirement is met.

6. The method according to claim 1 or 2, characterized in that: The step 5 comprises: Step 51, establish a test item: customize the "test item" block element, name the block with the name of the internal test item or the external test item, and set the properties of the test item; Step 52, according to the following principles, use the allocation relationship to supplement the test items: (1) If the test item is for interface parameters, establish the allocation relationship between the corresponding performance parameters and the test items; (2) If the test item is for a function, establish the allocation relationship between the corresponding product function and the test item; (3) If the test item is for a fault, establish an allocation relationship between the corresponding fault mode and the test item.

7. The method according to claim 1 or 2, characterized in that: In step 6, for the obtained fault detection model, the failure rate of each fault mode is obtained according to the fault mode information of the system to be studied, and then the test item is used as the starting node and the fault mode is used as the target node. The path from each starting node to the target node is traversed in the fault detection model, and the sum A of the failure rates of the fault modes that can be detected by the BIT items and the sum B of the failure rates of the fault modes that can be detected by all test items are counted. Then, based on the sum C of the failure rates of all fault modes of the system to be studied, the BIT fault detection rate = A / C is calculated, and the total fault detection rate = B / C is calculated.

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