Hybrid event trigger fault detection method for fuzzy Markov jump system

Abstract

The invention provides a hybrid event trigger fault detection method for a fuzzy Markov jump system, and the method comprises the following steps: for a discrete time T-S fuzzy Markov jump system, considering the modal asynchronism between the system and a fault detection filter and the asynchronism of a membership function at the same time based on a hidden Markov model and a non-parallel distributed compensation strategy; introducing a double-asynchronous fault detection filter; considering system measurement output and network communication between filters, and introducing an adaptive event triggering mechanism; the mismatching degree of the membership function is integrated into an event triggering condition, a hybrid event triggering mechanism is introduced, and the phenomenon of the mismatched membership function is processed; constructing a fault detection closed-loop system; and fault detection performance analysis and fault detection filter design are realized. The invention designs a hybrid adaptive event triggering scheme. The mismatching degree of the membership function is integrated as a part of a trigger mechanism, so that the mismatching problem is effectively solved while the trigger condition is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection methods, and in particular to a hybrid event-triggered fault detection method for a fuzzy Markov jump system. Background Art

[0002] Markov jump systems have powerful modeling capabilities for characterizing sudden system transitions and their random switching phenomena, and thus have been widely used in theoretical research and engineering. Considering the nonlinearities in real systems, the TS fuzzy model, with its ability to approximate nonlinear smooth functions, has been used to model nonlinear Markov jump systems. Furthermore, with the continuous development of industrial automation and information technology, the requirements for the safety and reliability of controlled systems are also increasing. In this context, fault detection is considered an effective means to reduce the economic and social losses caused by catastrophic failures. Therefore, fault detection for TS fuzzy Markov jump systems has become a research hotspot. In most existing work, fault detection filters require accurate access to the system's modal information. However, due to equipment limitations, economic efficiency, and environmental interference, this requirement can be difficult to meet in practical engineering. Furthermore, in network communication environments, event-triggered mechanisms have been proposed to effectively improve data transmission efficiency and conserve communication resources. However, the membership function mismatch between the system and the filter caused by event-triggered communication has received little attention. How to implement event-triggered communication while simultaneously characterizing the membership function mismatch and ensuring fault detection performance warrants further investigation.

[0003] In existing literature, fault detection for TS fuzzy Markov jump systems remains a worthy research topic. On the one hand, to achieve good fault detection performance, the fault detection filter is typically required to obtain accurate system modal information and maintain the same membership function as the system. However, due to economic considerations, equipment limitations, and factors such as failures and attacks during network communications, these two aspects of information are difficult for the filter to fully capture, which limits the further expansion of existing methods in practical applications. On the other hand, to conserve communication resources, event triggering is used to reduce the number of communications. However, the resulting mismatch between the filter and the system's membership function has not been fully considered. Although the number of triggers can be effectively reduced, the filter can only obtain a small amount of data, which reduces fault detection performance. Therefore, to address the issues of modal asynchrony and mismatched membership functions, and to consider the fault detection requirements, it is necessary to propose effective fault detection filter design methods and design reliable fault detection mechanisms to effectively meet the fault detection requirements. Summary of the Invention

[0004] Based on the technical problems mentioned in the above background technology, a fault detection method is provided. The present invention takes into account the modal asynchrony between the system and the fault detection filter and the asynchrony of the membership function, and designs a dual asynchronous fault detection filter based on the hidden Markov model and the non-parallel distributed compensation strategy, which overcomes the problem of difficulty in accurately obtaining the system mode and further relaxes the assumption of the same membership function. The present invention takes into account the network communication between the system measurement output and the filter, and proposes an adaptive event triggering mechanism in order to improve transmission efficiency and save communication resources. At the same time, in order to solve the mismatched membership function between the system and the filter, the present invention integrates the mismatch degree of the membership function into the event triggering condition, and proposes a mixed event triggering mechanism to effectively deal with the mismatched membership function phenomenon.

[0005] The technical means adopted in the present invention are as follows:

[0006] A method for detecting a fault triggered by a fuzzy Markov jump system, comprising the following steps:

[0007] Step 1: For the discrete-time TS fuzzy Markov jump system, the modal asynchrony between the system and the fault detection filter and the asynchrony of the membership function are considered simultaneously, based on the hidden Markov model and non-parallel distributed compensation strategy;

[0008] Step 2: Introduce dual asynchronous fault detection filters;

[0009] Step 3: Considering the network communication between the system measurement output and the filter, an adaptive event triggering mechanism is introduced;

[0010] Step 4: Integrate the mismatch degree of the membership function into the event trigger condition, introduce a mixed event trigger mechanism, and realize the processing of the mismatched membership function phenomenon;

[0011] Step 5: Build a fault detection closed-loop system;

[0012] Step 6: Implement fault detection performance analysis and fault detection filter design.

[0013] Furthermore, the fault detection performance analysis is as follows:

[0014] (1) When d(k) = 0, the fault detection closed-loop system is stochastically stable; under zero initial conditions, the fault detection closed-loop system satisfies H ∞ performance:

[0015]

[0016] (2) In order to detect the fault, the residual signal evaluation function J is defined as follows: r and the corresponding detection threshold Hth for:

[0017]

[0018] Among them, k n =K ι -k0+1; therefore, the following fault detection logic is given

[0019]

[0020] Furthermore, the discrete-time TS fuzzy Markov jump system includes: r fuzzy rules.

[0021] Furthermore, the fuzzy rule is:

[0022]

[0023] in, represents a fuzzy set, i=1,…,r and r represents the number of fuzzy rules; ψ(k) represents a function related to the state, is the antecedent variable; and Represent the state and measurement output of the system respectively; disturbance Belongs to L2[0,+∞); Represents the fault signal; assuming the matrix is a known matrix of suitable dimension;

[0024] Random variable δ k Indicates that in the collection The homogeneous Markov process with values ​​in is characterized by the following transition probabilities:

[0025]

[0026] Among them, π mn ∈[0,1], The transition probability matrix is ​​Π=[π mn ].

[0027] By defuzzifying (1), we obtain the following global fuzzy MJSs:

[0028]

[0029] in,

[0030]

[0031] Satisfy h i (ψ(k))≥0, represents the gradient of the membership function.

[0032] Furthermore, the dual asynchronous fault detection filter satisfies the rule:

[0033]

[0034] in, Indicates the status of FDF, represents the residual signal; y(k v ) represents the system output signal actually received by FDF, where k v Indicates the time of the vth trigger; represents the FDF parameter matrix to be determined. k} represents a hidden Markov process for detecting system modes, which is in the set The value is taken from the equation, and the conditional probability matrix Φ=[φ mt ]as well as

[0035]

[0036] Among them, φ mt ∈[0,1] and The following global fuzzy FDF is obtained:

[0037]

[0038] in make

[0039] Furthermore, the confounding event triggering mechanism is:

[0040] Define the error signal e(k)

[0041]

[0042] in, represents a set of positive integers; the trigger sequence {k v}, where k v Indicates the time of the vth trigger; the triggering conditions are:

[0043] The current measurement output is transmitted to the FDF via network communication when the following conditions are met:

[0044]

[0045] Among them, Λ t >0 indicates the matrix parameters to be designed, which are asynchronous with the system mode and share the same hidden Markov process with the controller; the variable η kThe update satisfies the following adaptive law:

[0046]

[0047] The preset event trigger parameters η1, η2 satisfy the condition 0<η1≤η2<1; ε>0 is introduced to adjust the function ‖e(k)‖ 2 The sensitivity is ‖e(k)‖ 2 For variable η k Degree of influence; ρ i ∈[0,1] is used to evaluate the degree of asynchrony of the membership function;

[0048] Introducing a fault reference model into the fault detection filter: Where W(') represents the weight matrix; the following state space model is obtained:

[0049]

[0050] in, represents the state vector of the reference model, represents weighted fault; G W (G=A, B, C, D) represents a known matrix.

[0051] Furthermore, setting δ k =m,τ k =t, the fault detection closed-loop system is:

[0052]

[0053] in, d(k)=[e T (k)w T (k)f T (k)] T ;

[0054]

[0055] Compared with the prior art, the present invention has the following advantages:

[0056] (1) Relaxing the requirements for obtaining accurate system modal information and matching membership functions. Compared with existing forms of system modal dependence and the requirement for the same membership function as the system, the dual asynchronous fault design of the present invention relaxes these requirements. Based on the hidden Markov model and non-parallel distributed compensation scheme, the present invention effectively solves the problems of modal asynchrony and mismatched membership functions. Therefore, the proposed method is more general and practical.

[0057] (2) Integrating adaptive event triggering and membership function mismatch information. To effectively address incomplete membership function matches and improve transmission efficiency to conserve network communication resources, the present invention designs a hybrid adaptive event triggering scheme. By integrating the mismatch degree of the membership function as part of the triggering mechanism, the mismatch problem is effectively resolved while ensuring the trigger conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0059] Figure 1 This is a block diagram of the fault detection under the mixed event triggering mechanism of the present invention.

[0060] Figure 2 Schematic diagram of the system mode and filter mode of the present invention.

[0061] Figure 3 Schematic diagram of adaptive parameters and event triggering time of the present invention.

[0062] Figure 4 Schematic diagram of the evaluation function and fault detection results of the present invention. DETAILED DESCRIPTION

[0063] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0064] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0065] like Figure 1-4 As shown, the present invention provides a method for detecting a fault triggered by a hybrid event in a fuzzy Markov jump system, comprising the following steps:

[0066] Step 1: For the discrete-time TS fuzzy Markov jump system, the modal asynchrony between the system and the fault detection filter and the asynchrony of the membership function are considered simultaneously, based on the hidden Markov model and non-parallel distributed compensation strategy;

[0067] Step 2: Introduce dual asynchronous fault detection filters;

[0068] Step 3: Considering the network communication between the system measurement output and the filter, an adaptive event triggering mechanism is introduced;

[0069] Step 4: Integrate the mismatch degree of the membership function into the event trigger condition, introduce a mixed event trigger mechanism, and realize the processing of the mismatched membership function phenomenon;

[0070] Step 5: Build a fault detection closed-loop system;

[0071] Step 6: Implement fault detection performance analysis and fault detection filter (FDF) design;

[0072] The fault detection performance analysis is as follows:

[0073] (1) When d(k) = 0, the fault detection closed-loop system is stochastically stable; under zero initial conditions, the fault detection closed-loop system satisfies H ∞ performance:

[0074]

[0075] (2) In order to detect the fault, the residual signal evaluation function J is defined as follows: r and the corresponding detection threshold J thfor:

[0076]

[0077] Among them, k n =k ι -k0+1; therefore, the following fault detection logic is given

[0078]

[0079] Preferably, the discrete-time TS fuzzy Markov jump system comprises: r fuzzy rules;

[0080] The fuzzy rules are:

[0081]

[0082] in, represents a fuzzy set, i=1,…,r and r represents the number of fuzzy rules; ψ(k) represents a function related to the state, is the antecedent variable; and Represent the state and measurement output of the system respectively; disturbance Belongs to L2[0,+∞); Represents the fault signal; assuming the matrix is a known matrix of suitable dimension;

[0083] Random variable δ k Indicates that in the collection The homogeneous Markov process with values ​​in is characterized by the following transition probabilities:

[0084]

[0085] Among them, π mn ∈[0,1], The transition probability matrix is ​​Π=[π mn ].

[0086] By defuzzifying (1), we obtain the following global fuzzy MJSs:

[0087]

[0088] in,

[0089]

[0090] Satisfy h i (ψ(k))≥0, represents the gradient of the membership function.

[0091] 3. The dual asynchronous fault detection method for a TS fuzzy Markov transition system based on hybrid event triggering according to claim 1, wherein the dual asynchronous fault detection filter satisfies the rule:

[0092] IF is , and is , and , and is , THEN ; (5)

[0093] in, Indicates the status of FDF, represents the residual signal; y(k v ) represents the system output signal actually received by FDF, where k v Indicates the time of the vth trigger; represents the FDF parameter matrix to be determined. k} represents a hidden Markov process for detecting system modes, which is in the set The value is taken from the equation, and the conditional probability matrix Φ=[φ mt ]as well as

[0094]

[0095] Among them, φ mt ∈[0,1] and The following global fuzzy FDF is obtained:

[0096]

[0097] in make

[0098] It is worth noting that the existence of the event-triggered communication mechanism leads to a mismatch between the membership functions of the system and the FDF. Furthermore, due to factors such as economic efficiency, equipment limitations, and estimation errors, the FDF struggles to accurately and in real time obtain the system's modal information, resulting in a mode asynchrony between the two. This mismatched membership function and asynchronous modes complicate stability analysis.

[0099] Furthermore, the confounding event triggering mechanism is:

[0100] Define the error signal e(k)

[0101]

[0102] in, represents a set of positive integers; the trigger sequence {k v}, where k v Indicates the time of the vth trigger; the triggering conditions are:

[0103] The current measurement output is transmitted to the FDF via network communication when the following conditions are met:

[0104]

[0105] Among them, Λ t >0 indicates the matrix parameters to be designed, which are asynchronous with the system mode and share the same hidden Markov process with the controller; the variable η k The update satisfies the following adaptive law:

[0106]

[0107] The preset event trigger parameters η1, η2 satisfy the condition 0<η1≤η2<1; ∈>0 is introduced to adjust the function ‖e(k)‖ 2 Sensitivity; ρ i ∈[0,1] is used to evaluate the degree of asynchrony of the membership function;

[0108] It should be noted that the fuzzy membership function information For the first time, it is considered as part of the event triggering mechanism to determine whether to transmit a signal. Unlike the traditional adaptive event triggering mechanism, the hybrid event triggering mechanism proposed in this invention enhances the flexibility of adjusting data transmission by utilizing membership function information and is conducive to handling membership function mismatches.

[0109] Introducing a fault reference model into the fault detection filter: Where W(k) represents the weight matrix; the following state space model is obtained:

[0110]

[0111] in, represents the state vector of the reference model, represents weighted fault; G W (G=A, B, C, D) represents a known matrix.

[0112] 5. The dual asynchronous fault detection method of TS fuzzy Markov jump system based on hybrid event triggering according to claim 1, characterized in that δ k =m,τk =t, the fault detection closed-loop system is:

[0113]

[0114] in, d(k)=[e T (k)w T (k)f T (k)] T ;

[0115]

[0116] For a given parameter ∈,η2∈(0,1), if there exists a matrix Λ t >0,H mti >0,P mi >0, Ω i For i,j={1,…,r}, Make the following conditions hold

[0117]

[0118] Ψ mtij -Ω i <0(0.2)

[0119] ρ i Ψ mtii +(1-ρ i )Ω i <0(0.3)

[0120] ρ j Ψ mtij +ρ i Ψ mtji +(1-ρ j )Ω i +(1-ρ i )Ω j <0,i <j(0.4)

[0121] in

[0122]

[0123] Then the fault detection system is stochastically stable when d(k) = 0 and satisfies H under zero initial conditions. ∞ performance.

[0124] Proof: Choose the Lyapunov function:

[0125]

[0126] Let δk =m,δ k+1 =n,τ k =t. Definition Then the difference in V(k) can be calculated as:

[0127]

[0128] Can be easily obtained

[0129] In order to obtain more relaxed conditions and handle asynchronous phenomena, we introduce the relaxation matrix Ω i , and satisfies The following relationship can be obtained:

[0130]

[0131] Therefore, we get (5.8) definition From the above analysis, it can be deduced that

[0132] The right side of the above inequality can be equivalently expressed by Ψ ij <0 can be derived, so we get

[0133] E{ΔV(k)+θ T (k)θ(k)-γ 2 d T (k)d(k)}<0 (0.10)

[0134] Therefore, under zero initial conditions, we have

[0135]

[0136] Established, thus ensuring Performance. At the same time, under generalized perturbations Below , so the system is stochastically stable. The proof is complete.

[0137] Since the theorem only gives the existence conditions of the filter and contains nonlinear functions, it cannot be solved directly. In the following theorem, we give the linear matrix inequality conditions that can be numerically tested.

[0138] For a given parameter ∈,η2∈(0,1), if there exists a matrix Λ t >0,H mti >0,Pmi >0, X1>0, X2>0, Y>0, For i,j={1,…,r}, Make the following conditions hold

[0139]

[0140] in

[0141]

[0142]

[0143] Then the fault detection system is stochastically stable when d(k) = 0 and satisfies H under zero initial conditions. ∞ Performance. The parameters of the fault detection filter are calculated as follows:

[0144]

[0145] Proof: Define relevant parameter matrix information

[0146]

[0147] Multiply both sides of the inequality by , multiplying right by its transpose, we can get

[0148] in

[0149]

[0150] Review of inequalities Established. Proof complete.

[0151] Furthermore, in order to obtain the optimal Performance, given the following convex optimization problem:

[0152] The present invention uses an example of a tunnel diode circuit system to verify the effectiveness of the proposed method and its advantages compared with the adaptive event triggering mechanism. Set the fault parameters to

[0153]

[0154] The simulation results are as follows. Figure 2 The mode hopping of the system and the filter are shown respectively. Figure 3 The evolution curve of the adaptive parameters in the event triggering mechanism and the triggering moment are plotted.

[0155] The evaluation function in the case of fault and no fault is as follows Figure 4 As shown. According to the given detection logic and evaluation function, the detection threshold is set to When a fault occurs, it can be found in The fault is detected at any time. The simulation results show the effectiveness of the designed FDF and detection algorithm.

[0156] Table 1 Data transmission rate under different η1

[0157]

[0158] As shown in Table 1, it can be seen that compared with the static event triggering mechanism, the proposed promiscuous event triggering mechanism can effectively reduce the data transmission rate, which reduces the consumption of communication resources.

[0159] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0160] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0161] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0162] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0163] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0164] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A hybrid event-triggered fault detection method for fuzzy Markov jump systems, characterized in that: The following steps are involved: Step 1: For the discrete-time TS fuzzy Markov jump system, the modal asynchrony between the system and the fault detection filter and the asynchrony of the membership function are considered simultaneously, based on the hidden Markov model and non-parallel distributed compensation strategy; Step 2: Introduce dual asynchronous fault detection filters; Step 3: Considering the network communication between the system measurement output and the filter, an adaptive event triggering mechanism is introduced; Step 4: Integrate the mismatch degree of the membership function into the event trigger condition, introduce a mixed event trigger mechanism, and realize the processing of the mismatched membership function phenomenon; Step 5: Build a fault detection closed-loop system; Step 6: Implement fault detection performance analysis and fault detection filter FDF design.

2. The method for detecting miscellaneous event-triggered faults in a fuzzy Markov jump system according to claim 1, characterized in that: The fault detection performance analysis is as follows: (1) When d(k) = 0, the fault detection closed-loop system is stochastically stable; under zero initial conditions, the fault detection closed-loop system satisfies H ∞ performance: (2) In order to detect the fault, the residual signal evaluation function J is defined as follows: r and the corresponding detection threshold J th for: I th =sup w(k)≠0,f(k)＝0 I r ; Among them, k n =k ι -k0+1; therefore, the following fault detection logic is given 3. The method for detecting miscellaneous event-triggered faults in a fuzzy Markov jump system according to claim 1, characterized in that: The discrete-time TS fuzzy Markov jump system includes: r fuzzy rules.

4. The method for detecting a miscellaneous event-triggered fault in a fuzzy Markov jump system according to claim 3, wherein: The fuzzy rules are: IFf1(ψ(k))is andf2(ψ(k))is and…,and is THEN; in, represents a fuzzy set, i=1,…,r and r represents the number of fuzzy rules; ψ(k represents a function related to the state, is the antecedent variable; and Represent the state and measurement output of the system respectively; disturbance Belongs to L2[0,+∞); Represents the fault signal; assuming the matrix is a known matrix of suitable dimension; Random variable δ k Indicates that in the collection The homogeneous Markov process with values ​​in is characterized by the following transition probabilities: Among them, π mn ∈[0,1], The transition probability matrix is ​​Π=[π mn ]. By defuzzifying (1), we obtain the following global fuzzy MJSs: in, Satisfy h i (ψ(k))≥0, represents the gradient of the membership function.

5. The method for detecting miscellaneous event-triggered faults in a fuzzy Markov jump system according to claim 1, wherein: The dual asynchronous fault detection filter satisfies the rules: IFf1(ψ(k v ))is andf2(ψ(k v ))is and…,and is THEN in, Indicates the status of FDF, represents the residual signal; y(k v ) represents the system output signal actually received by FDF, where k v Indicates the time of the vth trigger; represents the FDF parameter matrix to be determined. k } represents a hidden Markov process for detecting system modes, which is in the set The value is taken from the equation, and the conditional probability matrix φ=[φ mt ]as well as Among them, φ mt ∈[0,1] and The following global fuzzy FDF is obtained: in make 6. The method for detecting miscellaneous event-triggered faults in a fuzzy Markov jump system according to claim 1, characterized in that: The promiscuous event triggering mechanism is: Define the error signal e(k) in, represents a set of positive integers; the trigger sequence {k v }, where k v Indicates the time of the vth trigger; the triggering condition is: The current measurement output is transmitted to the FDF via network communication when the following conditions are met: Among them, Λ t >0 indicates the matrix parameters to be designed, which are asynchronous with the system mode and share the same hidden Markov process with the controller; the variable η k The update satisfies the following adaptive law: The preset event trigger parameters η1, η2 satisfy the condition 0<η1≤η2<1; ε>0 is introduced to adjust the function ‖e(k)‖ 2 The sensitivity is ‖e(k)‖ 2 For variable η k Degree of influence; ρ i ∈[0,1] is used to evaluate the degree of asynchrony of the membership function; Introducing a fault reference model into the fault detection filter: Where W(k) represents the weight matrix; the following state space model is obtained: in, represents the state vector of the reference model, represents weighted fault; G W (G=A, B, C, D) represents a known matrix.

7. The method for detecting miscellaneous event-triggered faults in a fuzzy Markov jump system according to claim 1, characterized in that: Setting δ k =m,τ k =t, the fault detection closed-loop system is: in, d(k)=[e T (k)w T (k)f T (k)] T ;

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