Vehicle equipment health assessment method and system based on fault sorting score deduction rule

Abstract

The invention discloses a vehicle equipment health assessment method and system based on a fault sorting score deduction rule, and relates to the technical field of equipment health assessment, and the method comprises the steps: obtaining real-time state parameters of each subsystem in vehicle equipment; judging whether the real-time state parameters are abnormal or not based on a risk assessment index system; in an abnormal state, according to the risk assessment index system, respectively solving a first risk assessment index weight and a second risk assessment index weight by adopting an improved fuzzy analytic hierarchy process and an entropy weight method; fusing the first risk assessment index weight and the second risk assessment index weight to obtain a composite weight; according to the composite weight, based on an IFAHP-TOPSIS fault sorting score deduction rule, obtaining a fault sorting of each subsystem; and according to the fault sequence of each subsystem, based on the number of faults occurring in the subsystems and scores corresponding to the faults, performing health assessment on the vehicle equipment. According to the invention, the method can achieve the precise evaluation of the health state of the vehicle equipment.

Description

Technical Field

[0001] The present application relates to the technical field of equipment health assessment, and in particular to a vehicle equipment health assessment method and system based on IFAHP-TOPSIS fault ranking deduction rules. Background Art

[0002] For a complex equipment such as vehicle equipment, suppose it contains systems, each containing a different number of components Each component contains a different number of fault alarm parameters or information ,How to determine the health of a component based on its fault alarm information, ,has become a key issue in the health assessment of complex ,equipment. Summary of the invention

[0003] The purpose of this application is to provide a vehicle equipment health assessment method and system based on fault ranking and deduction rules. Based on the IFAHP-TOPSIS fault ranking and deduction rules, each fault on the vehicle equipment is ranked by the IFAHP-TOPSIS method, which can achieve an accurate assessment of the health status of the vehicle equipment.

[0004] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a vehicle equipment health assessment method based on a fault ranking deduction rule, comprising: Obtain the real-time status parameters of each subsystem in the vehicle equipment.

[0005] Based on the risk assessment indicator system, it is judged whether the real-time status parameters are abnormal; the risk assessment indicator system is an assessment indicator system established based on the hierarchical analysis method and the traditional FMEA method; the risk assessment indicator system includes several first-level risk assessment indicators and second-level risk assessment indicators; each first-level risk assessment indicator includes at least one second-level risk assessment indicator.

[0006] When the real-time status parameter is in an abnormal state, according to the risk assessment index system, the improved fuzzy hierarchical analysis method and the entropy weight method are used to respectively calculate the first risk assessment index weight and the second risk assessment index weight; the improved fuzzy hierarchical analysis method is based on fuzzy theory, and the priority judgment matrix in the traditional hierarchical analysis method is converted into a fuzzy consistency judgment matrix and then extracted.

[0007] The first risk assessment indicator weight and the second risk assessment indicator weight are combined to obtain a composite weight of the risk assessment indicator.

[0008] According to the composite weight of the risk assessment index, based on the IFAHP-TOPSIS fault ranking deduction rule, the fault ranking of each subsystem is obtained; the IFAHP-TOPSIS fault ranking deduction rule is used to determine the deduction value of each subsystem when different fault alarms occur.

[0009] According to the fault ranking of each subsystem, the health assessment of the vehicle equipment is carried out based on the number of faults occurring in the subsystem and the scores corresponding to the faults.

[0010] In a second aspect, the present application provides a vehicle equipment health assessment system based on fault ranking deduction rules, including: The parameter acquisition module is used to obtain the real-time status parameters of each subsystem in the vehicle equipment.

[0011] The status assessment module is used to determine whether the real-time status parameters are abnormal based on the risk assessment indicator system; the risk assessment indicator system is an assessment indicator system established based on the hierarchical analysis method and the traditional FMEA method; the risk assessment indicator system includes several first-level risk assessment indicators and second-level risk assessment indicators; each first-level risk assessment indicator includes at least one second-level risk assessment indicator.

[0012] The weight calculation module is used to, when the real-time status parameter is in an abnormal state, respectively obtain the first risk assessment indicator weight and the second risk assessment indicator weight according to the risk assessment indicator system by using the improved fuzzy hierarchical analysis method and the entropy weight method; the improved fuzzy hierarchical analysis method is based on fuzzy theory, and is extracted by converting the priority judgment matrix in the traditional hierarchical analysis method into a fuzzy consistency judgment matrix.

[0013] The weight fusion module is used to fuse the first risk assessment indicator weight and the second risk assessment indicator weight to obtain a composite weight of the risk assessment indicator.

[0014] The fault ranking module is used to obtain the fault ranking of each subsystem according to the composite weight of the risk assessment index and based on the IFAHP-TOPSIS fault ranking deduction rule; the IFAHP-TOPSIS fault ranking deduction rule is used to determine the deduction value of each subsystem when different fault alarms occur.

[0015] The health assessment module is used to perform health assessment on vehicle equipment according to the fault ranking of each subsystem, based on the number of faults occurring in the subsystem and the scores corresponding to the faults.

[0016] According to the specific embodiments provided in this application, this application discloses the following technical effects: The present application provides a vehicle equipment health assessment method and system based on fault ranking deduction rules. The method first obtains the real-time status parameters of each subsystem in the vehicle equipment, and judges whether the real-time status parameters are abnormal based on the risk assessment index system, in order to timely discover potential problems and ensure the accuracy and timeliness of the assessment. The risk assessment index system is established based on the analytic hierarchy process and the traditional FMEA method to improve the accuracy of the assessment. When the real-time status parameter is in an abnormal state, the first risk assessment index weight and the second risk assessment index weight are respectively obtained by using the improved fuzzy analytic hierarchy process and the entropy weight method, in order to comprehensively consider the influence of different risk factors on the health status of the vehicle equipment. The improved fuzzy analytic hierarchy process can convert the priority judgment matrix in the traditional analytic hierarchy process into a fuzzy consistency judgment matrix, which improves the accuracy and reliability of the assessment. The first risk assessment index weight and the second risk assessment index weight are merged to obtain the composite weight of the risk assessment index, in order to more comprehensively reflect the comprehensive influence of different risk factors on the health status of the vehicle equipment. According to the composite weight of the risk assessment index, based on the IFAHP-TOPSIS fault ranking deduction rule, the fault ranking of each subsystem is obtained. Finally, according to the fault ranking of each subsystem, the health assessment of the vehicle equipment is carried out based on the number of faults occurring in the subsystem and the corresponding scores of the faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flowchart of a vehicle equipment health assessment method based on fault ranking deduction rules is provided in one embodiment of the present application.

[0019] Figure 2 A schematic diagram of a health status assessment structure provided in one embodiment of the present application.

[0020] Figure 3 A flowchart of a health assessment algorithm provided in one embodiment of the present application.

[0021] Figure 4 A system fault sequence hierarchy diagram provided in an embodiment of the present application.

[0022] Figure 5 This is a definition diagram of the 0.1-0.9 scaling method provided in one embodiment of the present application.

[0023] Figure 6A flow chart of the entropy weight method provided in one embodiment of the present application.

[0024] Figure 7 This is a flow chart of IFAHP-TOPSIS fault sorting provided in one embodiment of the present application.

[0025] Figure 8 A schematic diagram of the functional modules of a vehicle equipment health assessment system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] The subjective weighting method of AHP is based on the subjective consciousness and experience of expert investigators, and can make more accurate judgments and understandings of different evaluation objects or evaluation targets; however, its survey results and scoring are easily affected by the inherent ideas, personal preferences and thinking patterns of experts themselves, making the results lack scientificity and objectivity, and the evaluation index system constructed only based on the subjective weighting method also lacks stability. The objective weighting method of entropy value determines the objective weights based on the degree of variation of indicators among indicators. It has a strong theoretical basis and data support, can avoid subjective bias caused by human factors and has high accuracy; however, sometimes it ignores the importance of the evaluation indicators themselves and the actual situation during use, resulting in a large gap with the expected results, and even completely opposite to the evaluation indicators in reality.

[0028] In order to take into account both subjective will and objective information, IFAHP is combined with the entropy weight method, and the dynamic weight preference coefficient is used to replace the fixed weight preference coefficient in the combined weighting method. The corresponding weight preference coefficient is determined according to the nature of the evaluation index itself, forming a subjective and objective dynamic combined weighting method to obtain the composite weight of risk assessment indicators.

[0029] The purpose of this application is to provide a vehicle equipment health assessment method and system based on fault ranking and deduction rules. Based on the IFAHP-TOPSIS fault ranking and deduction rules, each fault on the vehicle equipment is ranked by the IFAHP-TOPSIS method, which can achieve an accurate assessment of the health status of the vehicle equipment.

[0030] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0031] like Figure 1 As shown, this embodiment provides a vehicle equipment health assessment method based on fault ranking deduction rules, including: Step 101: Acquire real-time status parameters of each subsystem in vehicle equipment.

[0032] Step 102: Based on the risk assessment indicator system, determine whether the real-time status parameters are abnormal; the risk assessment indicator system is an assessment indicator system established based on the hierarchical analysis method and the traditional FMEA method; the risk assessment indicator system includes several first-level risk assessment indicators and second-level risk assessment indicators; each first-level risk assessment indicator includes at least one second-level risk assessment indicator.

[0033] Step 103: When the real-time status parameter is in an abnormal state, according to the risk assessment index system, the improved fuzzy hierarchical analysis method and the entropy weight method are used to respectively calculate the first risk assessment index weight and the second risk assessment index weight; the improved fuzzy hierarchical analysis method is based on fuzzy theory, and is extracted by converting the priority judgment matrix in the traditional hierarchical analysis method into a fuzzy consistency judgment matrix.

[0034] Step 104: The first risk assessment indicator weight and the second risk assessment indicator weight are integrated to obtain a composite weight of the risk assessment indicator.

[0035] Step 105: According to the composite weight of the risk assessment index, based on the IFAHP-TOPSIS fault ranking deduction rule, the fault ranking of each subsystem is obtained; the IFAHP-TOPSIS fault ranking deduction rule is used to determine the deduction value of each subsystem when different fault alarms occur.

[0036] Step 106: According to the fault ranking of each subsystem, based on the number of faults occurring in the subsystem and the scores corresponding to the faults, a health assessment is performed on the vehicle equipment.

[0037] like Figure 2 As shown in the figure, when evaluating the status of complex equipment, what needs to be obtained are the performance parameters of the complex equipment, the fault alarm information of the complex equipment, and the health status of each subsystem (also called subsystem). For any subsystem, for example, subsystem 1 needs to obtain the performance parameters of subsystem 1, the fault alarm information of subsystem 1, and the health status of each component. For any component, what needs to be obtained is the component performance parameters and component fault alarm information. Through these multi-level and multi-dimensional data collection, the benefactor can fully grasp the overall operation status of the complex equipment.

[0038] In some embodiments, when executing step 101, the specific steps may be as follows: The subsystems in vehicle equipment include transmission subsystem, motion subsystem, power subsystem, chassis subsystem and detection subsystem.

[0039] The real-time status parameters acquired may include key performance indicators such as the vehicle equipment's running speed, engine speed, oil temperature, oil pressure, coolant temperature, battery voltage, etc. These parameters are collected in real time by sensors and transmitted to the central processing unit for analysis through a data transmission system.

[0040] In some embodiments, when executing step 102, Figure 3 As shown, the risk assessment index system is an assessment index system established based on the hierarchical analysis method and the traditional FMEA method; the risk assessment index system includes several first-level risk assessment indicators and second-level risk assessment indicators; each first-level risk assessment indicator includes at least one second-level risk assessment indicator.

[0041] Specifically, risk assessment belongs to the category of comprehensive evaluation, which includes the construction of evaluation index system, weighting of evaluation index, establishment of mathematical model and quantification of evaluation scheme. The traditional FMEA method evaluates the failure mode by calculating the risk factor to obtain the corresponding risk priority value (RPN).

[0042] According to the basic principles of traditional AHP, with the system failure sequence as the goal, two criterion layers are decomposed to establish a risk assessment index system, with a total of 3 first-level risk assessment indicators and 6 second-level risk assessment indicators. The details are shown in Table 1 below: Table 1 System risk assessment indicators

[0043] In some embodiments, when executing step 103, the specific steps may be as follows: Step 301: Based on the risk assessment indicator system, a hierarchical structure model of each risk assessment indicator is established; the hierarchical structure model includes a target layer, a criterion layer and an indicator layer.

[0044] Step 302: Construct a priority judgment matrix based on the hierarchical model ; The judgment matrix is ​​used to determine the weights between indicators at each level; where, A is the priority judgment matrix; n is the number of risk assessment indicators in the criterion layer; a ij Risk assessment indicators i Compared with risk assessment indicators j The importance of; first determine the elements in matrix A that satisfy a ij + a ji =1.

[0045] Step 303: According to the formula and Prioritize the matrix A The elements in are converted into fuzzy consistent judgment matrix R =[ r ij ] n×n ; In the formula, r i is the priority judgment matrix A Middle i The sum of the row elements, i =1, 2, …, n; r ij is the fuzzy consistency judgment matrix R Middle i Line j The elements of the column, r j is the priority judgment matrix A Middle j The sum of the elements in the column.

[0046] Step 304: Using the conversion formula e ij = r ij / r ji， Convert the fuzzy consistent judgment matrix into a reciprocal matrix E =[ e ij ] n×n .

[0047] Step 305: Based on the square root method, the hierarchical single-rank weight vector of each risk assessment indicator under the criterion layer is calculated.

[0048] Step 306: Calculate the hierarchical total ranking weight according to the hierarchical single ranking weight vector of each risk assessment indicator; the hierarchical total ranking weight is the first risk assessment indicator weight.

[0049] When executing step 301, the hierarchical structure model constructed based on each risk assessment indicator is as follows: Figure 4 As shown, the model is divided into target layer, criterion layer L1 and criterion layer L2.

[0050] Specifically, when executing steps 302-303, the specific steps may be as follows: Establishing a hierarchical model: First, we need to clarify the evaluation object or evaluation goal. Then we start to conduct a comprehensive analysis of the entire evaluation index system and classify all evaluation indicators according to different levels, generally divided into the target level, criterion level and indicator level.

[0051] Constructing a judgment matrix: When determining the weights of indicators at each level, all indicators cannot be compared together, but should be compared in pairs. This can reduce the difficulty of comparing different indicators and improve the accuracy of weights. At this time, a judgment matrix should be used to compare different indicators in pairs.

[0052] .

[0053] Where: A is the priority judgment matrix; n is the number of risk assessment indicators in the criterion layer; a ij Risk assessment indicators i Compared with risk assessment indicators j The importance of is in the range of 0.1-0.9. The 0.1-0.9 scaling method and its definition are as follows Figure 5 shown.

[0054] Specifically, when executing step 305, it can be as follows: According to the formula , calculate the reciprocal matrix E The product of each row of elements E i .

[0055] According to the formula ,calculate E i of n square root .

[0056] According to the formula , for column vectors After normalization, the hierarchical single ranking weight vector of each risk assessment indicator under the criterion layer is obtained: .

[0057] Specifically, when executing step 306, it can be as follows: The hierarchical total ranking refers to the ranking process of determining the relative importance of all indicators at a certain level to the evaluation object or evaluation target. This process is carried out in the order from the target layer to the criterion layer and then to the indicator layer. For the target layer, the result of its hierarchical single ranking is also the result of the total ranking.

[0058] Assume that the criterion level in the hierarchical model L 1 contains m Indicators ( L 11 , L 12 , …, L 1m ), the hierarchical single ranking weight is ; The next level of criteria L 2Contains n Indicators ( L 21 , L 22 , …, L 2n ), relative to the hierarchical index L 1j The hierarchical single ranking weight is f 1j , f 2j , …, f nj ( j =1, 2, …, m ) ; Criteria layer L 2 in 1 i The total hierarchical ranking weight of each indicator relative to the target layer is: .

[0059] In some embodiments, when executing step 103, Figure 6 As shown, the calculation steps of using the entropy weight method to obtain the weight of the second risk assessment indicator can be specifically as follows: Based on the risk assessment indicator system, each risk assessment indicator is standardized to obtain the evaluation matrix .

[0060] Evaluation Matrix R After standardization, we get i The indicator is j Evaluation object or evaluation target r ij The proportion p ij , and generate a standardized decision matrix P ij .

[0061] According to the formula , calculate the i Information entropy of risk assessment indicators e i .

[0062] According to the formula , calculate the i The coefficient of variation of the risk assessment indicators α i ; The coefficient of variation α i Used to calculate the second risk assessment indicator weight.

[0063] According to the formula , calculate the iEntropy weight of risk assessment indicators u i ; The entropy weight u i is the weight of the second risk assessment indicator.

[0064] Specifically, the entropy weight method is one of the objective weighting methods. This method uses information entropy to calculate the entropy weight of each indicator according to the degree of variation of each indicator, and then uses the entropy weight to correct the weight of this indicator to obtain a more objective indicator weight. P Calculating Entropy e j . Standardize the data of each indicator. Assume that m Items to be evaluated, n evaluation indicators, which can form an evaluation matrix , the evaluation matrix R Perform standardization to obtain i The indicator is j Evaluation object or evaluation target r ij The proportion p ij , forming a standardized decision matrix P ij The formula expression obtained is .

[0065] Where: r ij Representative i Under the evaluation index j The data value of an evaluation object or evaluation target.

[0066] Calculate the i Information entropy of evaluation indicators e i : .

[0067] .

[0068] Where: k is the constant coefficient of entropy.

[0069] Calculate the i The coefficient of difference of the evaluation indicators α i When , the difference coefficient of the evaluation index can represent the information utility value of the index. α i The bigger the i The greater the information utility of an indicator, the more important it is to the evaluation results and the greater its weight.

[0070] .

[0071] Calculate the i Entropy weight of each indicator u i : .

[0072] In this embodiment, the transmission subsystem is taken as an example for calculation, which is as follows: The priority judgment matrix of experts and risk indicators is obtained through expert scoring. For the risk assessment indicator system in Table 2, IFAHP is used to obtain the indicator weights. The steps are as follows: Table 2 System risk assessment indicators

[0073] (1) Constructing a risk assessment indicator priority matrix: The scoring experts scored the relative importance of the risk assessment indicators according to the 0.1-0.9 scaling method and obtained the priority judgment matrix of risk assessment indicators at all levels (the first-level risk assessment indicator matrix is ​​a 3x3 matrix, the second-level risk assessment indicator S matrix includes three indicators C1, C2 and C3, S is a 3x3 matrix, and the second-level risk assessment indicator D matrix includes two indicators C5 and C6, D is a 2x2 matrix).

[0074] Specifically, the first-level risk assessment indicator priority judgment matrix elements are shown in Table 3: Table 3. Element table of the priority matrix of first-level risk assessment indicators

[0075] Specifically, the priority judgment matrix elements of the secondary risk assessment indicator S are shown in Table 4: Table 4. Secondary risk assessment index S priority judgment matrix element table

[0076] Specifically, the priority judgment matrix elements of the secondary risk assessment indicator D are shown in Table 5: Table 5 Secondary risk assessment index priority judgment matrix elements

[0077] (2) The priority judgment matrix is ​​converted into a fuzzy consistent judgment matrix: 1) Take the priority determination matrix of the first-level risk assessment indicators as an example for calculation, as follows: .

[0078] .

[0079] The fuzzy consistent judgment matrix elements of the first-level risk assessment indicators are calculated: r 1=0.5+0.6+0.7=1.8; r 2=0.4+0.5+0.6=1.5; r 3=0.3+0.4+0.5=1.2; r 11 =( r 1- r 1) / 2×3+0.5=0.5; r 12 =( r 1- r 2) / 2×3+0.5=(1.8-1.5) / 6+0.5=0.55; r 13 =( r 1- r 3) / 2×3+0.5=(1.8-1.2) / 6+0.5=0.6; r 21 =( r 2- r 1) / 2×3+0.5=(1.5-1.8) / 6+0.5=0.45; r 22 =( r 2- r 2) / 2×3+0.5=0.5; r 23 =( r 2- r 3) / 2×3+0.5=(1.5-1.2) / 6+0.5=0.55; r 31 =( r 3- r 1) / 2×3+0.5=(1.2-1.8) / 6+0.5=0.4; r 32 =( r 3- r 2) / 2×3+0.5=(1.2-1.5) / 6+0.5=0.45; r 33 =( r 3- r 3) / 2×3+0.5=0.5.

[0080] Then the fuzzy consistent judgment matrix of the first-level risk assessment index is: .

[0081] Using the conversion formula eij = r ij / r ji Convert a matrix to a reciprocal matrix E =[ e ij ] n×n , as shown below: e 11 = r 11 / r 11 =1; e 12 = r 12 / r 21 =1.222; e 13 = r 13 / r 31 =1.5; e 21 = r 21 / r 12 =0.818; e 22 = r 22 / r 22 =1; e 23 = r 23 / r 32 =1.222; e 31 = r 31 / r 13 =0.667; e 32 = r 32 / r 23 =0.818; e 33 = r 33 / r 33 =1.

[0082] Then the fuzzy consistent judgment reciprocal matrix of the first-level risk assessment index is: .

[0083] 2) Take the priority determination matrix of the secondary risk assessment indicator S as an example to calculate: According to the priority judgment matrix elements meet a ij + a ji =1, perform mathematical transformation on the elements and convert them into fuzzy consistent judgment matrix: .

[0084] .

[0085] Calculate the secondary risk assessment index S Fuzzy consistent judgment matrix elements: r 1=0.5+0.65+0.8=1.95; r 2=0.35+0.5+0.7=1.55; r 3=0.2+0.3+0.5=1; r 11 =( r 1- r 1) / 2×3+0.5=0.5; r 12 =( r 1- r 2) / 2×3+0.5=(1.95-1.55) / 6+0.5=17 / 30; r 13 =( r 1- r 3) / 2×3+0.5=(1.95-1) / 6+0.5=79 / 120; r 21 =( r 2- r 1) / 2×3+0.5=(1.55-1.95) / 6+0.5=13 / 30; r 22 =( r 2- r 2) / 2×3+0.5=0.5; r 23 =( r 2- r 3) / 2×3+0.5=(1.55-1) / 6+0.5=71 / 120; r 31 =( r 3- r 1) / 2×3+0.5=(1-1.95) / 6+0.5=41 / 120; r32 =( r 3- r 2) / 2×3+0.5=(1 -1.55) / 6+0.5=49 / 120; r 33 =( r 3- r 3) / 2×3+0.5=0.5.

[0086] Among them, r 11 , r 12 , r 13 , ..., r 55 As shown in Table 6: Table 6 Entropy weight method to obtain subsystem weights

[0087] Then the fuzzy consistent judgment matrix of the secondary risk assessment index is: .

[0088] Using the conversion formula e ij = r ij / r ji Convert a matrix to a reciprocal matrix E =[ e ij ] n×n , as shown below: e 11 = r 11 / r 11 =1; e 12 = r 12 / r 21 =17 / 13; e 13 = r 13 / r 31 =79 / 41; e 21 = r 21 / r 12 =13 / 17; e 22 = r 22 / r 22 =1;e 23 = r 23 / r 32 =71 / 49; e 31 = r 31 / r 13 =41 / 79; e 32 = r 32 / r 23 =49 / 71; e 33 = r 33 / r 33 =1.

[0089] Then the fuzzy consistent judgment reciprocal matrix of the secondary risk assessment index is: .

[0090] (3) Obtain the hierarchical single-rank weight vector: The hierarchical single-rank weight vector of each risk assessment indicator under the criterion layer is obtained by the square root method.

[0091] 1) Calculate the product of each row of the reciprocal matrix E E i , the formula is as follows: .

[0092] .

[0093] ① Taking the priority determination matrix of the first-level risk assessment indicator as an example, we can calculate E 1=1.8330, E 2=0.9996, E 3=0.5256.

[0094] ② Taking the priority decision matrix of the secondary risk assessment index S as an example, we can calculate E 1=2.5197, E 2=1.1080, E 3=1.4893.

[0095] 2) Calculation E i of n square root : .

[0096] ① Taking the priority determination matrix of the first-level risk assessment indicator as an example, we can calculate =1.2238, =0.9999, =0.8070.

[0097] ② Taking the priority decision matrix of the secondary risk assessment index S as an example, we can calculate =1.3608, =1.0348, =1.1420.

[0098] 3) Column vector Perform normalization: .

[0099] but is the desired weight vector.

[0100] ①ω1=0.4038,ω2=0.3299,ω3=0.2663 (ω1 represents the weight value of the first-level risk indicator S, ω2 represents the weight value of O, and ω3 represents the weight value of D).

[0101] ②ω1=0.3842, ω2=0.2923, ω3=0.3226 (ω1 represents the weight value of C1 in the secondary risk indicator S, ω2 represents the weight value of C2, and ω3 represents the weight value of C3). According to the above method, the weight values ​​of the experts, the weight values ​​of the primary risk indicators S, O, and D, and the weight values ​​of the secondary risk indicators C1-C6 are obtained respectively.

[0102] (4) Obtain the total hierarchical ranking weight vector: Criteria Layer in Hierarchical Model L 1 contains 3 indicators S, O, D ( L 11 , L 12 , L 13 ), whose weight value is ω 11 ,ω 12 ,ω 13 ; Next criteria layer L 2 contains 6 indicators C1-C6, ( L 21 , L 22 , …, L 26 ), relative to the hierarchical index L 1j The hierarchical single ranking weight is f 1j ,f 2j , …, f nj ( j =1, 2, …, m ). The weight of C1 on S is recorded as f 11 , the weight of C2 on S is recorded as f 21 , the weight of C3 on S is recorded as f 31 Etc., as shown in Table 7-8 below: Table 7 Weight values ​​of first-level risk indicators

[0103] Table 8 Secondary risk indicator S weight value

[0104] Criteria Layer L The total hierarchical ranking weights of the six indicators in 2 for the target layer (system fault ranking) are: , as shown in Table 9: Table 9 Total ranking values ​​of levels

[0105] Table 10 Risk indicator expert scoring table

[0106] Based on the expert scores of the risk indicators in Table 10, the following formula is used to calculate the j Experts on i The proportion of risk indicators P ij .

[0107] .

[0108] According to the above formula, we can get: P 11 =0.1905, P 12 =0.2143; P 13 =0.1905; P 14 =0.1905; P 15 =0.2142; P 21 =0.1875, P 22 =0.21875; P 23=0.21875; P 24 =0.15625; P 25 =0.21875; P 31 =0.2258, P 32 =0.1935; P 33 =0.1935; P 34 =0.2258; P 35 =0.1614; P 41 =0.2093, P 42 =0.1860; P 43 =0.2093; P 44 =0.2093; P 45 =0.1861; P 51 =0.2083, P 52 =0.1667; P 53 =0.2083; P 54 =0.1667; P 55 =0.25; P 61 =0.1739, P 62 =0.2174; P 63 =0.1739; P 64 =0.2609; P 65 =0.1739.

[0109] The information entropy of the subsystem is calculated according to the following formula e i , the coefficient of difference of evaluation indicators α i And the entropy weight of the evaluation index t i ,in , , , .

[0110] According to the above formula, we can get: e1=0.99895; e 2 = 0.99494; e 3=0.99540; e 4=0.99893; e 5=0.99254; e 6=0.99097; α 1=0.00105; α 2=0.00506; α 3=0.0046; α 4=0.00107; α 5=0.00746; α 6=0.00903; t 1=0.0372; t 2=0.1790; t 3=0.1627; t 4=0.0378; t 5=0.2639; t 6=0.3194.

[0111] Finally, the weight values ​​of C1-C6 obtained by the entropy weight method are obtained as shown in Table 11.

[0112] Table 11 C1-C6 weight values ​​obtained by entropy weight method

[0113] In some embodiments, when executing step 104, the specific steps may be as follows: The subjective weighting method of the analytic hierarchy process is based on the subjective consciousness and experience of the expert investigators, and can make more accurate judgments and understandings of different evaluation objects or evaluation targets; however, its survey results and scoring are easily affected by the inherent ideas, personal preferences and thinking patterns of the experts themselves, making the results lack scientificity and objectivity, and the evaluation index system constructed only based on the subjective weighting method also lacks stability. The objective weighting method of entropy value determines the objective weights based on the degree of variation between the indicators. It has a strong theoretical basis and data support, can avoid subjective bias caused by human factors and has high accuracy; however, sometimes it ignores the importance of the evaluation indicators themselves and the actual situation during use, resulting in a large gap with the expected results, and even completely opposite to the evaluation indicators in reality.

[0114] In order to take into account both subjective will and objective information, AHP is combined with entropy weight method, and dynamic weight preference coefficient is used to replace the fixed weight preference coefficient in the combination weighting method. The corresponding weight preference coefficient is determined according to the nature of the evaluation index itself, forming a subjective and objective dynamic combination weighting method to obtain the composite weight of risk assessment indicators.

[0115] Assume that the subjective weight of an indicator in the evaluation index system is obtained by using the hierarchical analysis method: ω 1. The objective weight obtained using the entropy weight method is ω 2. The combined weight obtained by using the analytic hierarchy process-entropy weight method is ω i ,but: .

[0116] To meet the combined weight ω i Subjective weights of analytic hierarchy process ω 1 and the combined weight ω i Objective weights with entropy weight method ω 2 The sum of squares of the deviations is minimized, and the objective function is established as follows: .

[0117] The objective function is derived. When the first-order derivative of the objective function is 0, the minimum value of the sum of squares of the two deviations can be obtained. At this time, α =0.5, then: .

[0118] That is, when the subjective weight of the analytic hierarchy process and the objective weight of the entropy weight method each account for 50%, the combined weighting method is the optimal state of the risk assessment indicator system.

[0119] As shown in Table 12, the composite weights are: Table 12 C1-C6 composite weight values

[0120] In some embodiments, when executing step 105, the specific steps may be as follows: According to the basic assumptions of group decision-making, the quality of risk assessment is related to the evaluation method used. The TOPSIS method is a comprehensive evaluation method that is widely used in the field of evaluation and decision-making. The TOPSIS method also requires the construction of a standardized decision matrix, which is similar to the process of obtaining weights using the entropy weight method. In order to ensure the comprehensiveness of the risk assessment method, the TOPSIS method is used to sort faults. In the sorting process, triangular fuzzy number factors are introduced and the distance calculation formula is improved to obtain the IFAHP-TOPSIS method. Figure 7 As shown in the figure, the fault sorting steps based on the IFAHP-TOPSIS method are as follows: 1. Obtain the fuzzy decision matrix: (1) In various evaluation processes, there are usually fuzzy phenomena such as unclear classification standards and unclear boundaries. Therefore, the introduction of triangular fuzzy numbers in fuzzy theory can more objectively and accurately reflect the characteristics of each risk assessment indicator and reduce the influence of subjective factors. Assuming that there are k experts, fuzzy evaluation is performed on m fault modes based on n risk assessment indicators. The fuzzy evaluation value of the indicator of the fault mode FMi can be expressed as: .

[0121] Where: is the fuzzy rating of the risk assessment index of the failure mode by the kth expert; is the expert's weight. The final fuzzy decision matrix for: .

[0122] (2) Normalized fuzzy decision matrix: According to the different attributes of risk assessment indicators, there are three types of fuzzy decision matrices, namely benefit type, cost type and deviation type. In the benefit type fuzzy decision matrix, the rank increases with the increase of risk assessment indicators. In the benefit type fuzzy decision matrix, the rank increases with the increase of risk assessment indicators. Since the risk factors S, O and D in the traditional FMEA method are all benefit type risk assessment indicators, the normalization method of benefit type fuzzy decision matrix is ​​adopted.

[0123] .

[0124] Where: is the fuzzy decision matrix Elements in The corresponding variables after normalization; , are constants, and .

[0125] (3) Using the centroid method to normalize the fuzzy decision matrix Perform defuzzification and get the matrix , normalize the Y elements in the matrix: .

[0126] 2. The positive and negative ideal solutions are determined by the elements in the standardized decision matrix.

[0127] .

[0128] Where: A + is a positive ideal solution; A - is a negative ideal solution; .

[0129] 3. Calculate the distance between the failure mode and the positive and negative ideal solutions.

[0130] When the traditional TOPSIS method is used to calculate the final ranking, the weights of risk assessment indicators change the relationship structure between the original decision data, resulting in a reverse order problem. According to the method of eliminating the reverse order problem caused by weights, the distance calculation formula of the TOPSIS method is improved.

[0131] .

[0132] Where: is the distance between the failure mode and the positive ideal solution; is the distance between the failure mode and the negative ideal solution.

[0133] 4. Calculate the failure mode FM i Relative closeness C l,i , .

[0134] Finally, the IFAPH-IFTOPSIS input and output parameters are shown in Table 13: Table 13 IFAPH-IFTOPSIS input and output parameters

[0135] In this embodiment, the transmission subsystem is taken as an example for calculation. The details are as follows: The TOPSIS method is used to sort faults. In the sorting process, triangular fuzzy numbers are introduced to deal with uncertain factors and the distance calculation formula is improved to obtain the IFTOPSIS method.

[0136] For all failure modes, fuzzy evaluation is performed based on the secondary risk assessment index to obtain the fuzzy decision matrix. The steps are as follows: 1. Obtain the fuzzy decision matrix: (1) The introduction of triangular fuzzy numbers in fuzzy theory can more objectively and accurately reflect the characteristics of each risk assessment indicator and reduce the influence of subjective factors. Five experts conducted fuzzy evaluation on 60 failure modes based on six risk assessment indicators: .

[0137] For the k Failure Mode FM i Risk Assessment Indicators c j fuzzy ratings; The weight of the experts.

[0138] The expert's evaluation of C1 in fault mode FM1 is recorded as X1(1), and the calculation formula is: X1(1)=80×0.19+90×0.231+80×0.154+80×0.256+90×0.169=84.

[0139] X1(2)=60×0.19+70×0.231+70×0.154+50×0.256+70×0.169=62.98.

[0140] X1(3)=70×0.19+60×0.231+60×0.154+70×0.256+50×0.169=62.77.

[0141] X1(4)=90×0.19+80×0.231+90×0.154+90×0.256+80×0.169=86.

[0142] X1(5)=50×0.19+40×0.231+50×0.154+40×0.256+60×0.169=46.82.

[0143] X1(6)=40×0.19+50×0.231+40×0.154+60×0.256+40×0.169=47.43.

[0144] C1 in FM2: .

[0145] C2 in FM2: .

[0146] C3 in FM2: .

[0147] C4 in FM2: .

[0148] C5 in FM2: .

[0149] C6 in FM2: .

[0150] FM 60 C1 in: .

[0151] FM 60 C2 in: .

[0152] FM 60 C3 in: .

[0153] FM60 C4 in: .

[0154] FM 60 C5 in: .

[0155] FM 60 C6 in: .

[0156] The final fuzzy decision matrix is: .

[0157] (2) Normalized fuzzy decision matrix: The normalization method of benefit-based fuzzy decision matrix is ​​adopted.

[0158] .

[0159] (3) Defuzzification is performed using the centroid method to obtain Y, and the elements in the matrix are normalized, as shown in Table 14: Table 14 Failure mode normalization

[0160] 2. Determine the positive and negative ideal solutions by the elements in the standardized decision matrix.

[0161] .

[0162] .

[0163] in, =0.115; =0.047; =0.110; =0.051.

[0164] 3. Calculate the distance between the failure mode and the positive and negative ideal solutions.

[0165] .

[0166] According to the above calculation formula, the failure mode and positive and negative ideal solutions are shown in Table 15: Table 15 Failure modes and positive and negative ideal solutions

[0167] 4. Calculate the failure mode FM i Relative closeness C l,i , sort the faults according to their proximity.

[0168] .

[0169] The relative closeness is calculated according to the above formula, as shown in Table 16: Table 16 Fault proximity table

[0170] In some embodiments, when executing step 106, the specific steps may be as follows: When a common fault occurs or when the parameter threshold exceeds the upper or lower limit, according to the formula , and the health degree is calculated. The results are shown in Table 17.

[0171] Table 17 Health calculation input and output parameters

[0172] When a serious component self-test failure occurs, according to the formula , and the health degree is calculated. The results are shown in Table 18: Table 18 Health calculation input and output parameters

[0173] When the parameter threshold is between the upper limit and the upper limit or between the lower limit and the lower limit, the warning is triggered according to the formula , and the health degree is calculated. The results are shown in Table 19: Table 19 Health calculation input and output parameters

[0174] Embodiment 2 like Figure 8 As shown, a vehicle equipment health assessment system based on fault ranking deduction rules includes: The parameter acquisition module 801 is used to acquire the real-time status parameters of each subsystem in the vehicle equipment.

[0175] The status assessment module 802 is used to determine whether the real-time status parameters are abnormal based on the risk assessment indicator system; the risk assessment indicator system is an assessment indicator system established based on the hierarchical analysis method and the traditional FMEA method; the risk assessment indicator system includes several first-level risk assessment indicators and second-level risk assessment indicators; each first-level risk assessment indicator includes at least one second-level risk assessment indicator.

[0176] The weight calculation module 803 is used to, when the real-time status parameter is in an abnormal state, respectively obtain the first risk assessment indicator weight and the second risk assessment indicator weight according to the risk assessment indicator system by using the improved fuzzy hierarchical analysis method and the entropy weight method; the improved fuzzy hierarchical analysis method is based on fuzzy theory, and is extracted by converting the priority judgment matrix in the traditional hierarchical analysis method into a fuzzy consistency judgment matrix.

[0177] The weight fusion module 804 is used to fuse the first risk assessment indicator weight and the second risk assessment indicator weight to obtain a composite weight of the risk assessment indicator.

[0178] The fault ranking module 805 is used to obtain the fault ranking of each subsystem according to the composite weight of the risk assessment index based on the IFAHP-TOPSIS fault ranking deduction rule; the IFAHP-TOPSIS fault ranking deduction rule is used to determine the deduction value of each subsystem when different fault alarms occur.

[0179] The health assessment module 806 is used to perform health assessment on the vehicle equipment according to the fault ranking of each subsystem, based on the number of faults occurring in the subsystem and the scores corresponding to the faults.

[0180] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0181] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A vehicle equipment health assessment method based on fault ranking deduction rules, characterized in that: The vehicle equipment health assessment method based on fault ranking deduction rules includes: Obtain real-time status parameters of each subsystem in vehicle equipment; Based on the risk assessment indicator system, determine whether the real-time status parameters are abnormal; the risk assessment indicator system is an assessment indicator system established based on the hierarchical analysis method and the traditional FMEA method; the risk assessment indicator system includes several first-level risk assessment indicators and second-level risk assessment indicators; each first-level risk assessment indicator includes at least one second-level risk assessment indicator; When the real-time status parameter is in an abnormal state, according to the risk assessment index system, the improved fuzzy analytic hierarchy process and the entropy weight method are used to respectively obtain the first risk assessment index weight and the second risk assessment index weight; the improved fuzzy analytic hierarchy process is based on fuzzy theory, and is extracted by converting the priority judgment matrix in the traditional analytic hierarchy process into a fuzzy consistency judgment matrix; The first risk assessment indicator weight and the second risk assessment indicator weight are combined to obtain a composite weight of the risk assessment indicator; According to the composite weight of the risk assessment index, based on the IFAHP-TOPSIS fault ranking deduction rule, the fault ranking of each subsystem is obtained; the IFAHP-TOPSIS fault ranking deduction rule is used to determine the deduction value of each subsystem when different fault alarms occur; According to the fault ranking of each subsystem, the health assessment of the vehicle equipment is carried out based on the number of faults occurring in the subsystem and the scores corresponding to the faults.

2. A vehicle equipment health assessment method based on fault ranking deduction rules according to claim 1, characterized in that: The calculation steps of obtaining the weight of the first risk assessment index by using the improved fuzzy analytic hierarchy process specifically include: Based on the risk assessment indicator system, a hierarchical model of each risk assessment indicator is established; the hierarchical model includes a target layer, a criterion layer and an indicator layer; According to the hierarchical model, construct a priority judgment matrix ; The judgment matrix is ​​used to determine the weights between indicators at each level; where, A is the priority judgment matrix; n is the number of risk assessment indicators in the criterion layer; a ij Risk assessment indicators i Compared with risk assessment indicators j The importance of; first determine the elements in matrix A that satisfy a ij + a ji =1; According to the formula and Prioritize the matrix A The elements in are converted into fuzzy consistent judgment matrix R =[ r ij ] n×n ; In the formula, r i is the priority judgment matrix A Middle i The sum of the row elements, i =1, 2, …, n; r ij is the fuzzy consistency judgment matrix R Middle i Line j The elements of the column, r j is the priority judgment matrix A Middle j The sum of the column elements; Using the conversion formula e ij = r ij / r ji , convert the fuzzy consistent judgment matrix into a reciprocal matrix E =[ e ij ] n×n ; Based on the square root method, the hierarchical single ranking weight vector of each risk assessment indicator under the criterion layer is calculated; According to the hierarchical single ranking weight vector of each risk assessment indicator, the hierarchical total ranking weight is calculated; the hierarchical total ranking weight is the first risk assessment indicator weight.

3. A vehicle equipment health assessment method based on fault ranking deduction rules according to claim 2, characterized in that: Based on the square root method, the hierarchical single ranking weight vector of each risk assessment indicator under the criterion layer is calculated, including: According to the formula , calculate the reciprocal matrix E The product of each row of elements E i ; According to the formula ,calculate E i of n square root ; According to the formula , for column vectors After normalization, the hierarchical single ranking weight vector of each risk assessment indicator under the criterion layer is obtained: .

4. A vehicle equipment health assessment method based on fault ranking deduction rules according to claim 3, characterized in that: According to the hierarchical single ranking weight vector of each risk assessment indicator, the hierarchical total ranking weight is calculated, including: According to the formula , calculate the total ranking weight of the hierarchy; in, Criteria Layer L A hierarchical single ranking weight of 1, Criteria Layer L 2 in 1 i The total hierarchical ranking weight of each indicator relative to the target layer is: Criteria Layer L 2 Relative to hierarchical indicators L 1j The hierarchical single ranking weights.

5. The vehicle equipment health assessment method based on fault ranking deduction rules according to claim 1 is characterized in that: The calculation steps of obtaining the weight of the second risk assessment indicator by using the entropy weight method specifically include: Based on the risk assessment indicator system, each risk assessment indicator is standardized to obtain the evaluation matrix ; Evaluation Matrix R After standardization, we get i The indicator is j Evaluation object or evaluation target r ij The proportion p ij , and generate a standardized decision matrix P ij ; According to the formula , calculate the i Information entropy of risk assessment indicators e i ; In the formula, k is the entropy constant coefficient, m is the total number of projects to be evaluated; According to the formula , calculate the i The coefficient of variation of the risk assessment indicators α i ; The coefficient of variation α i Used to calculate the weight of the second risk assessment indicator; According to the formula , calculate the i Entropy weight of risk assessment indicators u i ; The entropy weight u i is the weight of the second risk assessment indicator.

6. A vehicle equipment health assessment method based on fault ranking deduction rules according to claim 5, characterized in that: The standardized decision matrix P ij The formula expression is: ； Where: r ij Representative i Under the risk assessment indicator j The data value of an evaluation object or evaluation target.

7. The vehicle equipment health assessment method based on fault ranking deduction rules according to claim 1 is characterized in that: The subsystems include a transmission subsystem, a motion subsystem, a power subsystem, a chassis subsystem and a detection subsystem.

8. The vehicle equipment health assessment method based on fault ranking deduction rules according to claim 1 is characterized in that: The first-level risk assessment indicators include fault severity, fault frequency and fault detectability.

9. A vehicle equipment health assessment method based on fault ranking deduction rules according to claim 8, characterized in that: The secondary risk assessment indicators corresponding to the fault severity in the primary risk assessment indicators include the impact of the fault on vehicle equipment, the impact of the fault on the subsystem, and the impact of the fault on other subsystems; The secondary risk assessment indicator corresponding to the fault occurrence frequency in the primary risk assessment indicator includes the proportion of the number of fault occurrences to all faults; The second-level risk assessment indicators corresponding to the fault detectability in the first-level risk assessment indicators include the difficulty of online fault diagnosis and the difficulty of fault diagnosis during maintenance.

10. A vehicle equipment health assessment system based on fault ranking deduction rules, characterized in that: include: Parameter acquisition module, used to obtain real-time status parameters of each subsystem in vehicle equipment; The status assessment module is used to determine whether the real-time status parameters are abnormal based on the risk assessment indicator system; The risk assessment index system is an assessment index system established based on the hierarchical analysis method and the traditional FMEA method; the risk assessment index system includes a number of first-level risk assessment indicators and second-level risk assessment indicators; Each first-level risk assessment indicator includes at least one second-level risk assessment indicator; A weight calculation module, used for, when the real-time state parameter is in an abnormal state, using an improved fuzzy analytic hierarchy process and an entropy weight method to respectively obtain a first risk assessment indicator weight and a second risk assessment indicator weight according to a risk assessment indicator system; The improved fuzzy hierarchical analysis method is based on fuzzy theory, and is extracted by converting the priority judgment matrix in the traditional hierarchical analysis method into a fuzzy consistency judgment matrix; A weight fusion module, used to fuse the first risk assessment indicator weight and the second risk assessment indicator weight to obtain a composite weight of the risk assessment indicator; A fault ranking module is used to obtain the fault ranking of each subsystem according to the composite weight of the risk assessment index and based on the IFAHP-TOPSIS fault ranking deduction rule; the IFAHP-TOPSIS fault ranking deduction rule is used to determine the deduction value of each subsystem when different fault alarms occur; The health assessment module is used to perform health assessment on vehicle equipment according to the fault ranking of each subsystem, based on the number of faults occurring in the subsystem and the scores corresponding to the faults.

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