CN117539753A - Test scheme evaluation method for complex system in discrete industry - Google Patents
Test scheme evaluation method for complex system in discrete industry Download PDFInfo
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- CN117539753A CN117539753A CN202311466621.4A CN202311466621A CN117539753A CN 117539753 A CN117539753 A CN 117539753A CN 202311466621 A CN202311466621 A CN 202311466621A CN 117539753 A CN117539753 A CN 117539753A
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- 238000012360 testing method Methods 0.000 title claims abstract description 78
- 238000011156 evaluation Methods 0.000 title claims abstract description 26
- 238000000034 method Methods 0.000 claims abstract description 27
- 238000004088 simulation Methods 0.000 claims abstract description 25
- 238000004458 analytical method Methods 0.000 claims abstract description 18
- 238000002347 injection Methods 0.000 claims abstract description 15
- 239000007924 injection Substances 0.000 claims abstract description 15
- 238000010586 diagram Methods 0.000 claims abstract description 12
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- 230000004048 modification Effects 0.000 claims description 2
- 230000005284 excitation Effects 0.000 claims 1
- 238000005065 mining Methods 0.000 claims 1
- 238000013461 design Methods 0.000 abstract description 10
- 238000005259 measurement Methods 0.000 abstract description 5
- 238000004519 manufacturing process Methods 0.000 abstract description 4
- 239000000243 solution Substances 0.000 abstract description 4
- 230000006872 improvement Effects 0.000 abstract description 3
- 238000011160 research Methods 0.000 abstract description 3
- 238000005457 optimization Methods 0.000 abstract description 2
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- 238000011058 failure modes and effects analysis Methods 0.000 description 2
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- 238000004806 packaging method and process Methods 0.000 description 2
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- 238000012544 monitoring process Methods 0.000 description 1
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3684—Test management for test design, e.g. generating new test cases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3688—Test management for test execution, e.g. scheduling of test suites
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention discloses a test scheme evaluation method for a complex system in discrete industry, which is based on a digital twin idea and a multi-signal flow diagram analysis method and provides a series of generalized, standardized and automatic solutions for fault injection simulation, test point layout analysis and test scheme evaluation, thereby realizing optimization of the test scheme and improving the reliability and design efficiency of the test scheme. The method can be divided into three steps: firstly, a data interface is established for a research object, namely a model of a complex system product, then automatic fault injection and simulation work are carried out according to a fault dictionary, existing product information and a test scheme, finally, measurement point information is analyzed according to a simulation result, index data of the test scheme is given, and a prompt is given for a possible improvement direction. The method can be used for the design, evaluation and improvement links of test schemes such as assembly test and the like, and provides help and guidance for actual production.
Description
Technical Field
The invention relates to a test scheme evaluation method, in particular to a test scheme evaluation method for a complex system in discrete industry.
Background
For complex systems, especially complex system products in discrete industries, the design of a test scheme is important to the reliability and safety control of the products. Meanwhile, due to the iteration and progress of the technology, the more complex the complex system is, the design and evaluation of the test scheme become important issues to be concerned. However, there is no solution to this problem that is systematic, has high versatility, and has high automation degree.
In the field of fault detection, conventional analysis methods include:
(1) Failure mode and impact analysis (Failure Mode and Effect Analysis, FMEA) method. The method collates possible failure mode information and generalizes its impact on other parts of the system as well as on overall functionality. The method focuses on qualitative description of fault attributes, and direct guidance cannot be given to layout design of measuring points or test schemes.
(2) Fault tree analysis (Fault Tree Analysis, FTA) method. The method emphasizes the influence and propagation of faults, focuses on the maintenance and troubleshooting analysis of the system rather than the test design stage, and pays less attention to the test point layout scheme.
(3) A multiple signal flow graph (Multi-Signal Flow Graphs) method. The method is less focused on physical properties of the faults, but gives consideration to the relation of fault propagation and the relation of fault-measuring points, and is suitable for the layout design and evaluation work of the measuring points.
It can be seen that the failure mode and impact analysis method and the fault tree analysis method are more suitable for maintenance and diagnosis links in the life cycle of the product, and the multi-signal flow diagram method is more suitable for test scheme design links. However, it is necessary to first establish a link between the faults and the fault-to-test points for further analysis.
However, in the current trend of the manufacturing industry towards multiple types and mass production modes, the system structure of the discrete industry is huge, the types of faults are gradually various, and sufficient fault diagnosis experience and information cannot be provided in the design link of the test flow. At the same time, even if there is a certain knowledge about the partial failure mode (e.g. there is data about similar models), experience knowledge often cannot fully cover the characterization possibility of the failure risk, and it is difficult to meet the high requirements for reliability. In view of the complex features of the current complex systems, the traditional knowledge driving methods are not fully applicable any more, and a new device testing method which gets rid of the limitations of expert experience is needed to be provided. The automatic test scheme evaluation performed by the digital twin model not only can realize complete fault analysis and test simulation, but also can realize the transformation from qualitative to quantitative analysis, thereby supporting the technical foundation of stable development of the production mode of the discrete industry on the product reliability level.
Disclosure of Invention
The invention provides a test scheme evaluation method for a complex system in discrete industry, which aims to solve the problem of missing solutions of test scheme evaluation and analysis of the complex system in discrete industry. The method is based on a digital twin idea and a multi-signal flow diagram analysis method, and aims to establish a complete set of model for a test scheme of a complex system, and realize a series of generalized, standardized and automatic solutions of fault injection simulation, test point layout analysis and test scheme evaluation, so that the optimization of the test scheme is realized, and the reliability and design efficiency of the test scheme are improved.
The test scheme evaluation method is oriented to complex systems in discrete industries and can be used in test processes such as assembly test and the like. Firstly, a digital twin model is established for a complex system under study, and standardized interfaces are reserved for system parameters, test item inputs, measuring point outputs and the like. Outside the model, a systematic fault dictionary is written with reference to a list of possible fault modes and parameters. In the simulation process, fault injection is carried out on each test item, and the state of the measuring point is recorded for analysis. Therefore, a set of complete multi-signal flow diagram model can be generated, the evaluation index of the test scheme can be quantitatively obtained through the model, and the direction of designing and improving the test scheme can be obtained according to the model.
The invention is characterized in that:
(1) The universality is good: the simulation parameters, test input, measurement point information and the like of the model are all in an interface form, so that the simulation parameters, the test input, the measurement point information and the like are applicable to different test objects and test schemes, and the adjustment and iteration of the test schemes are convenient;
(2) Test-fault relationship is fully mined: based on a digital twin model, the relation between the fault state and the detection state of the measuring point is obtained through an automatic fault injection mode, so that the effect of full excavation is achieved;
(3) The evaluation index can be quantified: and further analyzing and obtaining fault detection rate, isolation rate and measuring point redundancy index of the test scheme according to the fault injection result, and realizing quantitative evaluation of the test scheme.
Drawings
FIG. 1 is a schematic diagram of a test scheme evaluation flow for a complex system in the discrete industry.
FIG. 2 is a schematic diagram of a digital twin model based on standard packaging.
FIG. 3 is a schematic diagram of a parallel-serial combined simulation process.
Detailed Description
The test scheme evaluation method for the complex system in the discrete industry provided by the invention is described in detail below with reference to the accompanying drawings.
The invention provides a test scheme evaluation method for a complex system in discrete industry. The test scheme evaluation method is based on a multi-signal flow diagram method and a digital twin idea, establishes and automates fault injection, measuring point state and fault propagation mode analysis through a standardized model interface, digs out the perfection index of the test scheme, and provides possible operation suggestions for modification and perfection of the test scheme. The method can be divided into three steps: firstly, a data interface is established for a research object, namely a model of a complex system product, then automatic fault injection and simulation work are carried out according to a fault dictionary, existing product information and a test scheme, finally, measurement point information is analyzed according to a simulation result, index data of the test scheme is given, and a prompt is given for a possible improvement direction. The schematic diagram of the test scheme evaluation flow for the complex system in the discrete industry is shown in fig. 1.
Firstly, the method performs model interface standardized packaging on a digital twin model of a research object. Three types of programmable data interfaces are set aside in the object system model, considering that failure modes are embodied as changes in model parameters: parameter interface, input interface, output interface.
1. The parameter interface corresponds to the function and performance parameters of the model, which can generate faults. The parameter interface in the model is named as a parameter, a subsystem name, a parameter name, and a transmitted data structure is a floating point number;
2. the input interface corresponds to the stimulus and input signals provided during the model simulation test. The input interface is named as input signal name, and the transmitted data structure is an array;
3. the output interface corresponds to the output of each measuring point generated in the simulation test process of the model. The output interface is named as the output signal name, and the transmitted data structure is an array.
The relationship of the model to each data interface is shown in fig. 2.
The method then performs automatic fault injection and simulation. In order to do this, the following data are needed:
1. failure modes that may occur. The data format is a key-value pair structure, the keys of each key-value pair are fault names, and the values are list structures. Each item in the list is a key-value pair structure, including the subsystem affected by the fault, the changed parameters, and the possible value range information of the parameters after the fault is generated.
2. And testing configuration information. The data format is a key-value pair structure, the keys of each key-value pair are simulated test item names, and the values are nested key-value pair structures, and the data format comprises designated input data, parameters of health states, injected fault modes and focused measuring point information.
For each fault mode, the fault mode is injected into system parameters in a parameter offset injection mode, and simulation of a test process is carried out. Let the original health parameter be θ, the fault parameter be θ', the fault injection degree be r, then there are:
θ′＝(1+r)θ
according to the value range of the fault parameter corresponding to the fault mode, theta can be obtained min ≤θ′≤θ max I.e.
In the simulation process, different test items of the same fault mode are subjected to parallel simulation, and serial simulation is performed among different fault modes, so that simulation efficiency is improved. The execution of the simulation is shown in fig. 3. The simulation result of each measuring point of each test item of each fault mode comprises two groups of information, namely a signal value acquired by the measuring point and a corresponding time value. The data obtained by simulation are processed in the next step.
And finally, analyzing the obtained data by the method, and quantitatively evaluating the test scheme. The anomalies of the analog signal are divided into four basic modes:
TABLE 1 basic anomaly pattern and behavior of analog signals
| Abnormal pattern name | Expression form |
| Steady state is absent | The signal is larger as a set of data standard deviations over a time period of interest |
| Abnormal steady state value | The signal has a steady state value, but this value is related to healthy pre-treatmentThere is a large deviation in the period value |
| Excessive overshoot | The signal has a steady state value, but has an excessive overshoot in the adjustment process |
| The adjustment time is too long | The signal has steady state value, but the adjustment time is too long |
The anomalies of the digital signal are divided into six basic modes:
TABLE 2 digital Signal basic anomaly Pattern and manifestation
| Abnormal pattern name | Expression form |
| Interrupt | Signal zero |
| Seizing up | The signal staying at a certain value and no longer changing |
| Saturation | The signal is kept at saturation value |
| Delay of | There is a delay in the output of the signal |
| Deviation of | The value of the signal and the expected value of healthWith deviations |
| Noise adding | The value of the signal is noisy |
The monitored signal failure mode corresponding to each measurement point may include one or more of the above. And analyzing the time sequence signals according to the monitoring mode, the threshold value and other information set by the measuring point information, so as to obtain whether the measuring point has an abnormal state. By examining the corresponding relation between the fault and the abnormal state of the measuring point, the corresponding relation between the fault propagation mode and the detecting performance of the measuring point and the value of the fault injection rate and the detected fault can be mined. The resulting information is relied upon to further generate a multiple signal flow graph fault analysis model. The evaluation of the test protocol may be embodied as the following three quantitative indicators:
by examining the indexes and the multi-signal flow diagram model, the testing performance condition of the testing scheme can be obtained, and a reliable specific basis is provided for further designing and improving the testing scheme.
Claims (4)
1. A test scheme evaluation method for a complex system in discrete industry is characterized by comprising the following steps: based on a multi-signal flow diagram method and a digital twin idea, establishing and automating fault injection, measuring point state and fault propagation mode analysis through a standardized model interface, mining the perfection degree index of a test scheme, and providing possible operation suggestions for modification and perfection of the test scheme.
2. The discrete industry complex system oriented test scheme evaluation method of claim 1, characterized by: the digital twin model for the study object encapsulates three types of standardized programmable data interfaces: parameter interface, input interface, output interface. The three types of interfaces respectively correspond to the function and performance parameters of the model, which can generate faults, simulate excitation and input signals provided in the test process and simulate the output of each measuring point generated in the test process.
3. The discrete industry complex system oriented test scheme evaluation method of claim 1, characterized by: and performing automatic fault injection and simulation, wherein different test items of the same fault mode are subjected to parallel simulation, serial simulation is performed among different fault modes, and corresponding signal values are obtained for each measuring point of each test item of each fault mode.
4. The discrete industry complex system oriented test scheme evaluation method of claim 1, characterized by: dividing abnormal modes of the measuring point signals, examining the corresponding relation between the fault injection rate and the abnormal state of the measuring point, establishing a multi-signal flow diagram model, excavating the fault propagation mode and the detection performance of the measuring point, quantitatively evaluating the test scheme, and providing a reliable concrete basis for further designing and improving the test scheme.
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