A device fault diagnosability design method based on structural model

Abstract

The present invention relates to a device diagnosability design method based on a structural model, and belongs to the field of system fault diagnosis design. In order to solve the shortcomings of the structural analysis method that relies on the construction, analysis and derivation of a precise analytical model of the system, such as the complexity and high cost of the design method, a device fault diagnosability design method based on a structural model is proposed. This method does not require the establishment of an accurate mathematical model, and has advantages in the diagnosability design of large and complex systems; it avoids complex numerical calculations and has higher efficiency than the analytical method. The method includes step 1: establishing a diagnosability structural model of the device system: step 2: performing structural analysis on the diagnosability structural model established in step 1 to obtain the minimum consistency relationship set of the device system: step 3: establishing a diagnosability optimization model based on the minimum consistency relationship set established in step 2: step 4: solving the diagnosability optimization model established in step 3.

Description

Technical Field

[0001] The invention relates to a device diagnosability design method based on a structural model, and belongs to the field of system fault diagnosis design. Background Art

[0002] With the rapid development of science and technology, the complexity of the structure and function of large-scale equipment has gradually increased. Therefore, the full life cycle cost of the system design, development, testing, operation, maintenance, etc. will increase greatly. The complexity of the system structure and function will greatly increase the probability of its failure, performance degradation and functional failure, and also bring great challenges to the fault diagnosis of the system. On the one hand, due to the lack of advanced fault diagnosis equipment, some fault information cannot be obtained or the acquisition process is complicated, and the analysis and processing of fault information is not comprehensive enough; on the other hand, it is impossible to perform fault diagnosis in a dynamic and changeable environment. Secondly, the difficulty and process of fault diagnosis are cumbersome, and the improvement of fault diagnosis performance can only be achieved through the optimization design of fault diagnosis algorithms, which cannot fundamentally improve the fault diagnosis performance. Finally, the fault diagnosis cycle is long, the cost is high, and the resource utilization rate is low. Therefore, in order to ensure the reliability of the system, it is necessary to accelerate the improvement of the fault diagnosis performance of the system.

[0003] Traditionally, increasing the time between failures and ensuring system reliability is mainly achieved by increasing the reliability of key components of the system and improving the fault diagnosis capability of the system. The reliability of key components is the basis of system reliability. However, selecting components with high reliability cannot assemble a highly reliable system. On the one hand, increasing the reliability of key components is often limited by objective conditions such as the design, production, and installation of key components, and will also increase the design cost of the system; on the other hand, improving the reliability of key components cannot avoid the occurrence of system failures. At present, the main way to improve the fault diagnosis capability of the system is through the optimization design of the fault diagnosis method, but this cannot fundamentally change the fault diagnosis performance of the system. As the structural and functional complexity of the system gradually increases, it is difficult to meet the fault diagnosis needs of the system simply through the optimization design of the fault diagnosis method. Since the design of the fault diagnosis method is limited by the fault information provided by the system, if the fault information provided by the system itself is insufficient or missing, even the most accurate fault diagnosis algorithm will be of no help. Therefore, it is necessary to start from the source of fault diagnosis and improve the fault diagnosis performance of the system.

[0004] Diagnosability is a design feature that characterizes the fault diagnosis performance of a system. It can not only qualitatively determine whether a fault can be diagnosed, but also quantitatively describe the difficulty of fault diagnosis. Diagnosability is an important design feature to improve the fault diagnosis performance of a system. On the one hand, diagnosability ensures that the structure of the system provides sufficient fault information, and on the other hand, it ensures that the system completes the fault diagnosis task at the lowest cost and with the highest efficiency, thus ensuring the fault diagnosis performance of the system throughout its life cycle.

[0005] The structural analysis method is different from the previous model-based fault diagnosis method, which relies on the construction, analysis and derivation of the precise analytical model of the system. The structural analysis method focuses on obtaining relevant information for fault diagnosis from the structural model of the system. Structural analysis conducts diagnosability analysis by identifying sub-models that contain redundant relationships. Redundant sub-models are also called: analytical redundant relationships, minimum structural overdetermination, test subsystems, minimum evaluation chains, etc. This application calls it the minimum consistency relationship. Summary of the invention

[0006] The purpose of the present invention is to solve the shortcomings of the structural analysis method that relies on the construction, analysis and derivation of an accurate analytical model of the system, which brings about a complex and costly design method, and proposes a device fault diagnosability design method based on a structural model. This method does not require the establishment of an accurate mathematical model and has advantages in the diagnosability design of large and complex systems; it avoids complex numerical calculations and is more efficient than using analytical methods.

[0007] In order to solve the above problems, the device fault diagnosability design method based on the structural model of the present application is implemented through the following technical solutions:

[0008] A device fault diagnosability design method based on a structural model is characterized in that it includes the following steps:

[0009] Step 1: Establish a diagnosability structural model of the device system;

[0010] Step 2: Perform structural analysis on the diagnosability structural model established in step 1 to obtain the minimum consistent relationship set of the device system;

[0011] Step 3: Establish a diagnosability optimization model based on the minimum consistent relationship set established in step 2;

[0012] Step 4: Solve the diagnosability optimization model established in step 3.

[0013] Furthermore, the step 1 is as follows: Using a tuple Indicates that is the set of constraint equations of the device system, ; is a set of system internal variables and parameters. ; The variable types in are divided into known variables and unknown variables ,Right now ;

[0014] The structural model of the device system is a bipartite graph Indicates that is the system edge set: ,

[0015] The structural model of the device system is a bipartite graph The correlation matrix A = [ a ij ] Describe: ,

[0016] “ " represents the equation Contains variables ,Right now ; " " represents the equation Does not contain variables ,Right now ;

[0017] A specific behavior pattern is not required All equations are described in:

[0018] make This is a mapping from a set of equations to a set of variables: ,

[0019] make This is a mapping from a set of variables to a set of equations: ,

[0020] because ,definition: for arrive The mapping of for arrive The mapping of

[0021] The system model under a certain behavior mode is expressed as: , the corresponding structural model is: ;

[0022] Unknown variables Related Models ;and The relevant structural model is defined as: .

[0023] Furthermore, the step 2 includes the following specific steps:

[0024] Step 2.1, determine the redundancy of the device system and judge whether there is a consistency relationship between the device system;

[0025] Step 2.2, Dulmage-Mendelsohn decomposition;

[0026] Step 2.3: Determine the device system Subsystems with consistency relations in .

[0027] Further, the step 2.1 is as follows: given a device system , and its structural model is ,make for A mapping of , then: for Structural redundancy;

[0028] like , then the device system There is a consistency relationship.

[0029] Furthermore, the step 2.2 is as follows: the structural model established in step 1 Perform Dulmage-Mendelsohn decomposition to transform the bipartite graph of the structural model into an upper triangular form. The specific results are as follows;

[0030] The device system M is divided into three subsystems: structural underdetermined subsystem , ; Structural positive subsystem , ; Structural superstator system , ;

[0031] because , , , so the consistency relation exists in the structural superdetermined subsystem middle;

[0032] The redundancy of the device system structure is further expressed as: ;

[0033] The corresponding structural model Decomposed into three subgraphs: Structural underdetermined subgraph , structural positive subgraph , Structural Super Stator Diagram .

[0034] Further, the step 2.3 is as follows: for the device system , if and only if ,and When Constitute a consistency relation CR if and only if , the consistency relation CR is the minimum consistency relation MCR;

[0035] The specific steps are as follows:

[0036] Step 2.3.1. Obtain the structural model The structural overdetermined part , calculate the structural redundancy of the overdetermined part of the structure ;

[0037] Step 2.3.2: Determine structural redundancy Is it equal to 1? When , the set of constraint equations of the device system is Constitute the minimum consistency relation MCR, when When , the minimum consistency relation MCR is an empty set, and proceed to step 2.3.3;

[0038] Step 2.3.3: For the structural model Each constraint equation in Loop through the following operations: Delete from the current structure diagram and repeat steps 2.3.1 to 2.3.2 until all the minimum consistency relations in the structure model are found to form the minimum consistency relation set of the device system. , the minimum consistent relation set Corresponding subsystem A subsystem with a consistency relationship.

[0039] Furthermore, the step 3 includes the following specific steps:

[0040] Step 3.1: Calculate the system failure matrix ;

[0041] Step 3.2: Calculate the system fault isolation matrix ;

[0042] Step 3.3: Based on the minimum consistent relation set Dimension , establish the corresponding optimization vector , ;

[0043] Step 3.4: Based on the optimized vector , establish the optimization matrix ;

[0044] Step 3.5: Use the optimization matrix X established in step 3.4 to calculate the system fault matrix in step 3.1 Perform optimization and calculate the optimized system fault isolation matrix ;

[0045] Step 3.6: Obtain the diagnosability optimization model and constraints.

[0046] Further, the step 3.1 is as follows: for each fault in the device system fault set F , the minimum consistent relation set Every minimal consistency relation in ,like ,but ,like ,but , establish the system fault matrix ,Right now:

[0047] System failure The minimum consistency relation with step 2 The logical mapping relationship of the fault feature matrix FSM = [ fr ij ] m × n express,

[0048] in: .

[0049] Furthermore, the step 3.2 is as follows: for the fault feature matrix , get The number of rows m and the number of columns n are used to establish an m*m dimensional matrix with all 1s as the fault isolation matrix , for each fault in the device system fault set F , ,like Right now ,but ,otherwise ,

[0050] Build a Fault Isolation Matrix I s o M = [ fr ij ] m × n ,

[0051] in: .

[0052] Furthermore, the specific steps of step 3.4 are as follows: is the n-dimensional identity matrix, E = [ e 1 , e 2 , … , e n ] ,from Start iterating for each element in the optimization vector x. If ,but ,get X = [ y 1 , y 2 , … ] ;

[0053] Furthermore, the specific steps of step 3.5 are as follows: Multiply it with the optimization matrix X to get the optimized fault feature matrix , use the method in step 3.2 to calculate the optimized system fault isolation matrix ;

[0054] Furthermore, the step 3.6, obtaining the diagnosability optimization model is: ,

[0055] In the formula The minimum set of consistent relations the number of

[0056] The constraints are: ,

[0057] After the optimization, the fault isolation matrix of the system System Fault Isolation Matrix Under the same conditions, the minimum set of consistent relations selected The number of is minimal.

[0058] Furthermore, the step 4 uses a tree species optimization algorithm to solve the diagnosability optimization model of step 3, and the specific steps are as follows:

[0059] Step 4.1. Parameter initialization: population size ;parameter ; Spatial Dimension ; Maximum number of iterations ;initialization ;

[0060] Step 4.2: Based on Optimize vector The definition range, using Initialize the tree positions randomly in the search space;

[0061] for In the The position of the dimensional space; for A random number in a range; For the The lower bound of dimensional space; For the The upper bound of the dimensional space;

[0062] Step 4.3: Targeting the population Every tree in Generate seeds;

[0063] Step 4.3.1. Initialize the seed tree ;

[0064] Step 4.3.2, when the random number When, based on Update the seed position; otherwise, based on Update the seed location, for A random number within the range controls the size and direction of the seed position update step; for In the The optimal position in dimensional space; for In the A random position in dimensional space;

[0065] Step 4.3.3 is based on Determine whether the seed meets the constraints. If so, based on Calculate the seed fitness value; otherwise, its fitness value is infinite;

[0066] Step 4.3.4 ;

[0067] Step 4.4, select the optimal solution: select an optimal solution from the seeds generated by each tree Ti, compare it with the current tree Ti, if it is better than tree Ti, replace the tree with the seed;

[0068] Step 4.5: If , repeat steps 4.3 and 4.4; otherwise, the algorithm ends and returns the optimal tree based on step 4.3.3 The calculated seed fitness value.

[0069] Compared with the traditional testability model method, this method can better reflect the diagnostic reasoning relationship of the device system, can achieve diagnosability optimization, and has the following beneficial effects:

[0070] Establish a structural model of the device system, and obtain relevant information for fault diagnosis from the structural model of the system. There is no need to establish an accurate mathematical model, which has advantages in the diagnosability design of large and complex systems. Based on the structural analysis method, the minimum consistency relationship of the system is solved, which avoids complex numerical calculations and has higher efficiency than the analytical method. The tree species optimization algorithm is used to optimize the diagnosability optimization model, taking into account the system diagnosability requirements and the consistency relationship design cost, and the optimal diagnosable optimization model is obtained. Improve the reliability and diagnosability of the device control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 is a flow chart of the present invention;

[0072] Figure 2 This is a schematic diagram of the Dulmage-Mendelsohn decomposition;

[0073] Figure 3 For consistency relations Figure 1 ;

[0074] Figure 4 For consistency relations Figure 2 ;

[0075] Figure 5 for The structural model. DETAILED DESCRIPTION

[0076] The following is a specific implementation of the present invention with reference to the accompanying drawings to further illustrate the composition of the present invention.

[0077] Example 1. Figure 1 A device fault diagnosability design method based on a structural model is shown in the figure. The specific process is as follows Figure 1 As shown, the following steps are included:

[0078] Step 1: Establish a diagnosability structural model of the device system;

[0079] Step 2: Perform structural analysis on the diagnosability structural model established in step 1 to obtain the minimum consistent relationship set of the device system;

[0080] Step 3: Establish a diagnosability optimization model based on the minimum consistent relationship set established in step 2;

[0081] Step 4: Solve the diagnosability optimization model established in step 3.

[0082] Furthermore, the step 1 is as follows: Using a tuple Indicates that is the set of constraint equations of the device system, ; is a set of system internal variables and parameters. ;

[0083] The variable types in are divided into known variables and unknown variables ,Right now ;

[0084] The structural model of the device system is a bipartite graph Indicates that is the system edge set: ,

[0085] The structural model of the device system is a bipartite graph The correlation matrix A = [ a ij ] Describe: ,

[0086] “ " represents the equation Contains variables ,Right now ; " " represents the equation Does not contain variables ,Right now ;

[0087] A specific behavior pattern is not required All equations are described in:

[0088] make This is a mapping from a set of equations to a set of variables: ,

[0089] make This is a mapping from a set of variables to a set of equations: ,

[0090] because ,definition: for arrive The mapping of for arrive The mapping of

[0091] The system model under a certain behavior mode is expressed as: , the corresponding structural model is: ;

[0092] Unknown variables Related Models ;and The relevant structural model is defined as: .

[0093] For example, a system model ,like Figure 5 As shown, ,

[0094] in , , , , , It is a system failure.

[0095] The diagnosability of the system is optimized by solving the consistency relationship. The calculation process of the consistency relationship is as follows: based on the redundant sub-model, a part of the equations is selected to solve the unknown variables; secondly, based on the redundant equations, a consistency check is performed.

[0096] like Figure 3 , Figure 4The calculation flow chart of the consistency relationship is shown in FIG. Figure 3 It can be seen that , pass , Calculated; Dynamic variables , pass , Differentiate and finally pass Get the consistency relation : ,

[0097] from Figure 4 It can be seen that the consistency relationship and The equations involved are the same, but the order in which the unknown variables are calculated is different. pass Calculated; , , Apply the equations in turn , , get. , A differential loop is formed, and the differential loop can be broken by an integrator, that is,

[0098] , in order to facilitate the description of the order Finally, based on Get the consistency relation: .

[0099] The commonly used tools for structural analysis are bipartite graph matching and structural decomposition. The basic principle of fault diagnosis based on structural analysis is to find out the subsystems that contain consistency relationships by analyzing the structural model of the system, and to conduct diagnosability analysis based on the sensitive relationship between the consistency relationship and the fault.

[0100] Furthermore, the step 2 includes the following specific steps:

[0101] Step 2.1, determine the redundancy of the device system and judge whether there is a consistency relationship between the device system;

[0102] Step 2.2, Dulmage-Mendelsohn decomposition;

[0103] Step 2.3: Determine the device system Subsystems with consistency relations in .

[0104] Further, the step 2.1 is specifically as follows: the basic principle of fault diagnosis based on structural analysis is to find out the subsystems containing consistency relations by analyzing the structural model of the system; the consistency relation is intuitively manifested in structural analysis as the number of equations being greater than the number of unknown variables, and its key indicator is structural redundancy;

[0105] Given device system , and its structural model is ,make for A mapping of , then: for Structural redundancy;

[0106] like , then the device system There is a consistency relationship.

[0107] Furthermore, the step 2.2 is as follows: the structural model established in step 1 Perform Dulmage-Mendelsohn decomposition to transform the bipartite graph of the structural model into an upper triangular form. The specific results are as follows;

[0108] The device system M is divided into three subsystems: structural underdetermined subsystem , ; Structural positive subsystem , ; Structural superstator system , ;

[0109] because , , , so the consistency relation exists in the structural superdetermined subsystem middle;

[0110] The redundancy of the device system structure is further expressed as: ;

[0111] The corresponding structural model Decomposed into three subgraphs: Structural underdetermined subgraph , structural positive subgraph , Structural Super Stator Diagram .

[0112] Further, the step 2.3 is as follows: for the device system , if and only if ,and When Constitute a consistency relation CR if and only if , the consistency relation CR is the minimum consistency relation MCR;

[0113] Fault is detectable if and only if: ;Fault Can be used with Isolate if and only if: ,The minimum consistent relation has advantages in analyzing fault isolability;

[0114] The specific steps are as follows:

[0115] Step 2.3.1. Obtain the structural model The structural overdetermined part , calculate the structural redundancy of the overdetermined part of the structure ;

[0116] Step 2.3.2: Determine structural redundancy Is it equal to 1? When , the set of constraint equations of the device system is Constitute the minimum consistency relation MCR, when When , the minimum consistency relation MCR is an empty set, and proceed to step 2.3.3;

[0117] Step 2.3.3: For the structural model Each constraint equation in Loop through the following operations:

[0118] The constraint equation Delete from the current structure diagram and repeat steps 2.3.1 to 2.3.2 until all the minimum consistency relations in the structure model are found to form the minimum consistency relation set of the device system. , the minimum consistent relation set Corresponding subsystem A subsystem with a consistency relationship.

[0119] like Figure 3-Figure 4 The consistency relationship and , expressed as . Assume that the same fault will not appear in different equations. If the equation There is a fault in , then define it as: .For example The fault equation is: , , .

[0120] In The fault is detectable, that is, if , then the fault is detectable. It also contains a smaller consistency relation, namely , .

[0121] Furthermore, the step 3 includes the following specific steps:

[0122] Step 3.1: Calculate the system failure matrix ;

[0123] Step 3.2: Calculate the system fault isolation matrix ;

[0124] Step 3.3: Based on the minimum consistent relation set Dimension , establish the corresponding optimization vector , ;

[0125] Step 3.4: Based on the optimized vector , establish the optimization matrix ;

[0126] Step 3.5: Use the optimization matrix X established in step 3.4 to calculate the system fault matrix in step 3.1 Perform optimization and calculate the optimized system fault isolation matrix ;

[0127] Step 3.6: Obtain the diagnosability optimization model and constraints.

[0128] Further, the step 3.1 is as follows: for each fault in the device system fault set F , the minimum consistent relation set Every minimal consistency relation in ,like ,but ,like ,but , establish the system fault matrix ,Right now:

[0129] System failure The minimum consistency relation with step 2 The logical mapping relationship of the fault feature matrix FSM = [ fr ij ] m × n represents, where: .

[0130] Furthermore, the step 3.2 is as follows: for the fault feature matrix , get The number of rows m and the number of columns n are used to establish an m*m dimensional matrix with all 1s as the fault isolation matrix , for each fault in the device system fault set F , ,like Right now ,but ,otherwise ,

[0131] Build a Fault Isolation Matrix I s o M = [ fr ij ] m × n ,

[0132] in: .

[0133] Furthermore, the specific steps of step 3.4 are as follows: is the n-dimensional identity matrix, E = [ e 1 , e 2 , … , e n ] ,from Start iterating for each element in the optimization vector x. If ,but ,get X = [ y 1 , y 2 , … ] .

[0134] Furthermore, the specific steps of step 3.5 are as follows: Multiply it with the optimization matrix X to get the optimized fault feature matrix , use the method in step 3.2 to calculate the optimized system fault isolation matrix ;

[0135] Furthermore, the step 3.6, obtaining the diagnosability optimization model is: ,

[0136] In the formula The minimum set of consistent relations the number of

[0137] The constraints are: ,

[0138] After the optimization, the fault isolation matrix of the system System Fault Isolation Matrix Under the same conditions, the minimum set of consistent relations selected The number of is minimal.

[0139] Furthermore, the step 4 uses a tree species optimization algorithm to solve the diagnosability optimization model of step 3, and the specific steps are as follows:

[0140] Step 4.1. Parameter initialization: population size ;parameter ; Spatial Dimension ; Maximum number of iterations ;initialization ;

[0141] Step 4.2: Based on Optimize vector The definition range, using Initialize the tree positions randomly in the search space;

[0142] for In the The position of the dimensional space; for A random number in a range; For the The lower bound of dimensional space; For the The upper bound of the dimensional space;

[0143] Step 4.3: Targeting the population Every tree in Generate seeds;

[0144] Step 4.3.1. Initialize the seed tree ;

[0145] Step 4.3.2, when the random number When, based on Update the seed position; otherwise, based on Update the seed location, for A random number within the range controls the size and direction of the seed position update step; for In the The optimal position in dimensional space; for In the A random position in dimensional space;

[0146] Step 4.3.3 is based on Determine whether the seed meets the constraints. If so, based on Calculate the seed fitness value; otherwise, its fitness value is infinite;

[0147] Step 4.3.4 ;

[0148] Step 4.4, select the optimal solution: select an optimal solution from the seeds generated by each tree Ti, compare it with the current tree Ti, if it is better than tree Ti, replace the tree with the seed;

[0149] Step 4.5: If , repeat steps 4.3 and 4.4; otherwise, the algorithm ends and returns the optimal tree based on step 4.3.3 The calculated seed fitness value.

Claims

1. A device fault diagnosability design method based on a structural model, characterized by: The following steps are involved: Step 1: Establish a diagnosability structural model of the device system; Step 2: Perform structural analysis on the diagnosability structural model established in step 1 to obtain the minimum consistent relationship set of the device system, including the following specific steps: Step 2.1: Determine the redundancy of the device system and judge whether there is a consistency relationship between the device system: Given a device system , and its structural model is ,make for A mapping of , then: for Structural redundancy; like , then the device system There is a consistency relationship in Step 2.2, Dulmage-Mendelsohn decomposition: The structural model established in step 1 Perform Dulmage-Mendelsohn decomposition to transform the bipartite graph of the structural model into an upper triangular form. The specific results are as follows; The device system M is divided into three subsystems: structural underdetermined subsystem , ; Structural positive subsystem , ; Structural superstator system , ; because , , , so the consistency relation exists in the structural superdetermined subsystem middle; The redundancy of the device system structure is further expressed as: ; The corresponding structural model Decomposed into three subgraphs: Structural underdetermined subgraph , structural positive subgraph , Structural Super Stator Diagram ; Step 2.3: Determine the device system Subsystems with consistency relations in :For device system , if and only if ,and When Constitute the consistency relation CR; If and only if , the consistency relation CR is the minimum consistency relation MCR; The specific steps are as follows: Step 2.3.

1. Obtain the structural model The structural overdetermined part , calculate the structural redundancy of the overdetermined part of the structure ; Step 2.3.2: Determine structural redundancy Is it equal to 1? When , the set of constraint equations of the device system is Constitute the minimum consistency relation MCR, when When , the minimum consistency relation MCR is an empty set, and proceed to step 2.3.3; Step 2.3.3: For the structural model Each constraint equation in Loop through the following operations: The constraint equation Delete from the current structure diagram and repeat steps 2.3.1 to 2.3.2 until all the minimum consistency relations in the structure model are found to form the minimum consistency relation set of the device system. , the minimum consistent relation set Corresponding subsystem For subsystems with consistency relations; Step 3: Establish a diagnosability optimization model based on the minimum consistent relationship set established in step 2, including the following specific steps: Step 3.1: Calculate the system failure matrix :For each fault in the device system fault set F , the minimum consistent relation set Every minimal consistency relation in ,like ,but ,like ,but , establish the system fault matrix , that is: system failure The minimum consistency relation with step 2 The logical mapping relationship of the fault feature matrix express, in: ; Step 3.2: Calculate the system fault isolation matrix :For the fault feature matrix , get The number of rows m and the number of columns n are used to establish an m*m dimensional matrix with all 1s as the fault isolation matrix , for each fault in the device system fault set F , , like Right now ,but ,otherwise , Build a Fault Isolation Matrix , in: ; Step 3.3: Based on the minimum consistent relation set Dimension , establish the corresponding optimization vector , ; Step 3.4: Based on the optimized vector , establish the optimization matrix :set up is the n-dimensional identity matrix, ,from Start iterating for each element in the optimization vector x. If ,but ,get ; Step 3.5: Use the optimization matrix X established in step 3.4 to calculate the system fault matrix in step 3.1 Perform optimization and calculate the optimized system fault isolation matrix :The system fault matrix Multiply it with the optimization matrix X to get the optimized fault feature matrix , use the method in step 3.2 to calculate the optimized system fault isolation matrix ; Step 3.6: Obtain the diagnosability optimization model and constraints: , In the formula The minimum set of consistent relations the number of The constraints are: , After the optimization, the fault isolation matrix of the system System Fault Isolation Matrix Under the same conditions, the minimum set of consistent relations selected The number of is the smallest; Step 4: Solve the diagnosability optimization model established in step 3.

2. The method for designing device fault diagnosability based on a structural model according to claim 1, characterized in that: The step 1 is specifically as follows: Device system Using a tuple Indicates that is the set of constraint equations of the device system, ; is a set of system internal variables and parameters. ; The variable types in are divided into known variables and unknown variables ,Right now ; The structural model of the device system is a bipartite graph Indicates that is the system edge set: , The structural model of the device system is a bipartite graph The correlation matrix Describe: , " " represents the equation Contains variables ,Right now ;" " represents the equation Does not contain variables ,Right now ; A specific behavior pattern is not required All equations are described in: make This is a mapping from a set of equations to a set of variables: , make This is a mapping from a set of variables to a set of equations: , because ,definition: for arrive The mapping of for arrive The mapping of The system model under a certain behavior mode is expressed as: , the corresponding structural model is: ; Unknown variables Related Models ;and The relevant structural model is defined as: .

3. A device fault diagnosability design method based on a structural model according to claim 1 or 2, characterized in that: The step 4 uses a tree species optimization algorithm to solve the diagnosability optimization model of step 3, and the specific steps are as follows: Step 4.

1. Parameter initialization: population size ;parameter ; Spatial Dimension ; Maximum number of iterations ;initialization ; Step 4.2: Based on Optimize vector The definition range, using Initialize the tree positions randomly in the search space; for In the The position of the dimensional space; for A random number in a range; For the The lower bound of dimensional space; For the The upper bound of the dimensional space; Step 4.3: Targeting the population Every tree in Generate seeds; Step 4.3.

1. Initialize the seed tree ; Step 4.3.2, when the random number When, based on Update the seed position; otherwise, based on Update the seed location, for A random number within the range controls the size and direction of the seed position update step; for In the The optimal position in dimensional space; for In the A random position in dimensional space; Step 4.3.3 is based on Determine whether the seed meets the constraints. If so, based on Calculate the seed fitness value; otherwise, its fitness value is infinite; Step 4.3.4 ; Step 4.4, select the optimal solution: select an optimal solution from the seeds generated by each tree Ti, compare it with the current tree Ti, if it is better than tree Ti, replace the tree with the seed; Step 4.5: If , repeat steps 4.3 and 4.4; otherwise, the algorithm ends and returns the optimal tree based on step 4.3.3 The calculated seed fitness value.

Images