Evaluation method and device for assembly fault detection, and application

Abstract

The invention provides an evaluation method and device for set membership fault detection and application, and belongs to the technical field of fault diagnosability. The performance of the fault detection system is evaluated according to a minimum detectable fault index of the fault detection system, and the minimum detectable fault index is a fault amplitude when a feasible set with a fault residual error and a feasible set without a fault residual error of the system to be detected are just separated. Combining the used observer equation to obtain a residual system equation of a fault-free state and a fault state of the system to be detected; measuring a noise range by using geometry characterization, and obtaining residual feasible set characterization of a fault-free state and a fault state of the to-be-detected system in combination with set membership operation; and determining the minimum detectable fault index of the to-be-detected system. According to the method, observer design optimization, control system design and correction system model parameters can be guided, so that the control system is more stable.

Description

Technical Field

[0001] The present invention belongs to the technical field of fault diagnosability, and in particular relates to an evaluation method, device and application of collective fault detection. Background Art

[0002] In fault diagnosis technology, the accuracy of fault diagnosis will be affected by the existence of factors such as noise, model uncertainty, and unknown input. In the model-based fault diagnosis method, according to the different noise assumptions, it can be divided into statistical theory framework method and set membership theory framework method. The former assumes that the system uncertainty has probabilistic characteristics such as mean and probability density; the latter assumes that the system uncertainty is unknown but bounded, and can be characterized using the set membership method.

[0003] The process disturbance situation is complex, and the probability characteristics of uncertainty such as noise are difficult to obtain accurately. It is more reasonable to model it as a bounded signal. There are many existing fault detection methods proposed under bounded uncertainty. Generally, the interference robustness and fault sensitivity indicators are considered in the design process so that the observer meets a certain minimum design performance. However, in the actual application process, the actual performance that the designed fault diagnosis algorithm can achieve is often difficult to accurately analyze. As a key parameter indicator in engineering applications, the performance evaluation index of the fault detection method is crucial. The literature (Wang Weiliang, Geng Yanfeng, Sun Jian, et al. Sensor fault detection and minimum detectable fault analysis of dynamic pointing rotary guidance system [J]. Journal of the International Society of Automation, 2022, 127: 108-119.) gives a fault diagnosis performance analysis method, but this method can only analyze the fault detection performance in steady state. In practical applications, faults often trigger fault alarms in the dynamic process, without waiting for the system to enter the steady state. Therefore, the fault diagnosability analysis method under conventional steady-state conditions is relatively conservative. Studying the fault diagnosability in the dynamic process helps to more accurately analyze the performance of the fault diagnosis system. However, there is no diagnostic analysis method for faults in dynamic processes in the currently published literature. Therefore, from the perspective of engineering application theory research, how to select a better fault detection method and further guide the design and optimization of the fault detection method is a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of the problem that the actual effect of the fault detection method in the prior art is difficult to evaluate, the present invention proposes an evaluation method, device and application of set membership fault detection, which can effectively guide the observer design and fault detection performance evaluation under the set membership theory framework method.

[0005] A first aspect of the present invention provides a performance evaluation method for a set-membership fault detection method, wherein the method evaluates the performance of a fault detection system according to a minimum detectable fault index of the fault detection system, wherein the minimum detectable fault index is a fault amplitude when a residual feasible set with faults and a residual feasible set without faults of a system to be detected are just separated, and the performance evaluation method comprises: S101, for the system to be detected, combining the observer equation used, obtaining the residual system equations of the system to be detected in a non-fault state and a fault state; S102, using a geometric body to characterize the measurement noise range, and combining set membership operation to obtain a residual feasible set characterization of a fault-free state and a faulty state of the system to be detected; S103 , determining a minimum detectable fault index of the system to be detected, wherein the minimum detectable fault index is a fault amplitude when the residual feasible sets of the fault state and the non-fault state of the system to be detected are just separated at the sampling time.

[0006] Furthermore, in step S101, the system to be detected is a linear discrete time-invariant system containing a fault, and the linear discrete time-invariant system containing a fault is:

[0007] in, , , , , They are System state vector, system input vector, system output vector, measurement noise vector, sensor fault vector, for The system state vector at time is the system matrix, is the input matrix, is the output matrix, is the noise distribution matrix; is the fault distribution matrix; The observer equation is:

[0008] in, , They are The observed values ​​of the system state vector and the output vector at time instant, for The observed value of the system state vector at time is the observer parameter matrix; The state estimation error is:

[0009] The residual is:

[0010] The residual system equation of the system without fault is:

[0011] in, and They are Moment and The error in the estimation of the fault-free state at time t; for The fault-free residual at time t; The residual system equation of the system with fault is:

[0012] in, and They are Moment and The fault state estimation error at time t; for The faulty residual at time.

[0013] Furthermore, in step S102, the method for calculating the residual feasible set is: The residual feasible set is represented by a central symmetric polyhedron, and the central symmetric polyhedron of the system fault-free residual system is:

[0014] in, represents the Minkowski sum, represents a linear mapping, For trouble-free system Moment residual centrosymmetric polytope, For trouble-free system The center vector of the moment residual centrosymmetric polytope, For trouble-free system Generator matrix of the moment residual centrosymmetric polytope; For trouble-free system The central symmetric polytope of the state estimation error at the moment, For trouble-free system The center vector of the central symmetric polytope of the state estimation error at the moment, For trouble-free system The generator matrix of the central symmetric polytope of the state estimation error at the moment, For trouble-free system The central symmetric polytope of the state estimation error at the moment, for Centrosymmetric polytope that measures noise at every moment; The central symmetric polytope of the faulty residual system is:

[0015] in, For faulty systems The centrosymmetric polytope of the moment residual, For faulty systems The center vector of the moment residual centrosymmetric polytope, For faulty systems Generator matrix of the moment residual centrosymmetric polytope; For faulty systems The central symmetric polytope of the state estimation error at the moment, For faulty systems The center vector of the central symmetric polytope of the state estimation error at the moment, For faulty systems The generator matrix of the central symmetric polytope of the state estimation error at the moment, For faulty systems The central symmetric polytope of the state estimation error at the moment, For faulty systems Centrosymmetric polytope of moment failure.

[0016] Furthermore, in step S103, the method for calculating the minimum detectable fault index is: by calculating the fault state Centrosymmetric polytope of time residuals and fault-free state When the intersection of the centrosymmetric polytopes of the moment residuals is an empty set, The fault with the minimum value is recorded as the minimum detectable fault:

[0017]

[0018] in, is the minimum value operator, Matrix is ​​the given weight matrix; Convert the above formula into a mixed integer quadratic programming problem and solve it:

[0019]

[0020]

[0021] in, , , To solve the Lagrange multiplier, for No. elements, for No. elements, For transposition, , is the binary variable to be found, , are the vector and scalar to be determined, and for The number of rows and columns, , Known as The lower and upper bounds of , They are The center and generator matrix of the new polytope generated by applying the polytope intersection empty condition transformation theorem at any time; , , is the weight matrix, For trouble-free system The generator matrix of the time residual centrosymmetric polytope, For faulty systems The generator matrix of the time residual centrosymmetric polytope, is the weight matrix; Calculate the minimum detectable fault index of the fault detection system .

[0022] The second aspect of the present invention provides an evaluation device, which includes a data acquisition module, a system model parameter identification module, a minimum detectable fault index calculation module, a communication decoding module, and a host computer storage analysis module; The data acquisition module is used to collect measurement information output by the sensor of the system to be detected; The system model parameter identification module is connected to the data acquisition module and is used to identify the system model parameters in real time through the output information measured by the system sensor, and to modify the nominal system parameter matrix using the particle swarm parameter identification and least squares parameter identification methods; The minimum detectable fault index calculation module is connected to the system model parameter identification module, and after obtaining the parameters of the system model, the minimum detectable fault index at each moment is calculated according to the performance evaluation method described in the first aspect of the present invention; The communication decoding module is connected to the minimum detectable fault index calculation module, performs communication decoding on the minimum detectable fault index and sends the data to the host computer storage and analysis module; The host computer storage and analysis module stores the measurement information output by the sensor, the identified model parameters and the minimum detectable fault index, and analyzes the stored results.

[0023] A third aspect of the present invention provides an application of a performance evaluation method for a member fault detection method to a rotary steerable drilling tool, comprising the following steps: S201, establishing a system model of the rotary steerable drilling tool according to the physical and electrical characteristics of the rotary steerable drilling tool, identifying the model parameters of the rotary steerable drilling tool; combining the system state observer equation to obtain the residual system equations of the system in a fault-free state and a faulty state; S202, using a centrosymmetric polyhedron to characterize the measurement noise range of the rotary steerable drilling tool, and obtaining a residual centrosymmetric polyhedron of the rotary steerable drilling tool system based on the computational properties of the centrosymmetric polyhedron; S203. Taking the minimization of the weighted bi-norm of the vector composed of the current fault and its variation as the optimization target, the distance between the residual central symmetric polytopes in the faulty and non-faulty states of the rotary steerable drilling tool at the current moment is maximized to obtain the minimum detectable current fault index of the rotary steerable drilling tool system; the minimum detectable current fault index is the fault amplitude when the residual feasible sets of the faulty state and the non-faulty state of the system to be detected at the sampling moment are just separated.

[0024] Furthermore, the system model of the rotary steerable drilling tool is:

[0025]

[0026] in, for The system state vector at the moment, that is, the motor Shaft current , permanent magnet synchronous motor speed , stable platform speed and tool face angle , for The system state vector at time , , , and They are The system input vector, unknown input vector, system output vector, measurement noise vector and sensor fault vector at each moment, is the singular matrix of the known singular system, , and are the known system matrix, input matrix and output matrix respectively, For the system The parameter uncertainty matrix of the matrix, is the unknown input distribution matrix, is the noise distribution matrix, is the fault distribution matrix; The observer used is of the form:

[0027] in, , is the observer parameter matrix; the observer parameter matrix satisfies ; is the 4-dimensional identity matrix; for The system output vector at the moment; is the observer parameter matrix; State estimation error at the moment , residual , noise variation , fault variation , , They are The noise vector and sensor fault vector are measured at the same time, and the residual system of the rotary steerable drilling tool with and without fault is as follows:

[0028]

[0029] in, for The error in the estimation of the fault-free state at time t; and They are Moment and The fault state estimation error at time t; for The faulty residual at time.

[0030] Furthermore, the error-free state estimation center-symmetric polytope , residual centrosymmetric polytope , the central symmetric polytope of the faulty system state estimation error , residual centrosymmetric polytope The iteration is expressed as:

[0031]

[0032] in, is the parameter model uncertainty matrix The parameter matrix obtained by taking the maximum value is and Respectively Moment and The error in the system state estimation without any fault at any time, For trouble-free system The center vector of the central symmetric polytope of the state estimation error at the moment, For trouble-free system +1 time state estimation error centrosymmetric polytope generator matrix, For trouble-free system The center vector of the central symmetric polytope of the state estimation error at the moment, For trouble-free system The generator matrix of the central symmetric polytope of the state estimation error at the moment, and Respectively Moment and There is always a fault system state estimation error, For faulty systems The center vector of the central symmetric polytope of the state estimation error at the moment, For faulty systems The generator matrix of the central symmetric polytope of the state estimation error at the moment, For faulty systems The center vector of the central symmetric polytope of the state estimation error at the moment, For faulty systems The generator matrix of the central symmetric polytope of the state estimation error at the moment, for The system state vector at time for The central symmetric polytope of the system state vector at time, for Generator matrix of the centrosymmetric polytope of the system state vector at time instant; for The noise variation at each moment, for The central symmetric polyhedron of the momentary noise variation, for The generator matrix of the centrosymmetric polytope of the momentary noise variation; for The noise vector at time instant, for The centrosymmetric polytope of the noise vector at time instant, for Generator matrix of the centrosymmetric polytope of the moment noise vector; for The change of fault at any moment, for The central symmetric polyhedron of the moment fault variation, for The center vector of the centrosymmetric polyhedron of the fault variation at each moment; for The moment fault vector, for The centrosymmetric polytope of moment faults, for The central vector of the centrosymmetric polytope of the moment fault vector; and Respectively The residuals of the fault-free system and the faulty system at time instant, For trouble-free system The center vector of the moment residual centrosymmetric polytope, For trouble-free system The generator matrix of the residual centrosymmetric polytope at time +1, For faulty systems The center vector of the moment residual centrosymmetric polytope, For faulty systems The generator matrix of the residual centrosymmetric polytope at time +1.

[0033] Furthermore, the calculation method of the centrosymmetric polyhedron of the error and residual of the state estimation of the fault-free system is:

[0034] The calculation method of the centrosymmetric polytope of the faulty system state estimation error and residual is:

[0035] in, , , , They are , is the matrix after dimensionality reduction, and the dimensionality reduction process is as follows: against Centrosymmetric polytope and integers , and satisfies , is the center vector of the centrosymmetric polyhedron, is the Minkowski sum operation, Generate a matrix for a centrosymmetric polytope, for the number of rows, For the general The matrix obtained by arranging the column vectors of in descending order of Euclidean norm is: for dimensional hypercube, then the matrix after dimensionality reduction is , and there are Established, among which, for dimensional hypercube, Depend on Before column vectors, for Remove part, satisfy: , For the matrix No. Line Column elements.

[0036] Furthermore, the minimum detectable current fault index calculation method is as follows:

[0037] The above formula is transformed into a mixed integer quadratic programming problem. The problem is solved by writing a solution program in MATLAB software using the YALMIP toolkit and using the CPLEX commercial solver:

[0038] in, , , To solve the Lagrange multiplier, for No. elements, for No. elements, For transposition, , is the binary variable to be found, , are the vector and scalar to be determined, and for The number of rows and columns, , Known as The lower and upper bounds of , .

[0039] The present invention calculates the minimum detectable fault index of different set membership fault detection methods. The smaller the minimum detectable fault index is, the smaller the fault that can be detected by the fault detection method is, and the performance of the set membership fault detection method is better. The minimum detectable fault index is related to the design parameters of the observer and the design of the control system. Therefore, by calculating the minimum detectable fault index, it can not only be used to guide the design optimization of the observer, but also can be applied to the control system design and the correction of system model parameters, so that the control system operation is more stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of an embodiment of the present invention; Figure 2 This is a module connection diagram of another embodiment of the present invention; Figure 3 This is a flow chart of another embodiment of the present invention; Figure 4 This is a flow chart of another embodiment of the present invention; Figure 5 For the ( )to ( ) starts to calculate the minimum detectable current fault amplitude curve; Figure 6 For the ( )to ( ) starts to calculate the minimum detectable current fault amplitude change curve.

[0041] Figure 7 This is the residual result diagram obtained after adding the calculated minimum detectable fault.

[0042] Figure 8 It is the fault alarm result. DETAILED DESCRIPTION

[0043] In the model-based fault diagnosis technology, it can be divided into a probabilistic method based on statistical characteristics and a deterministic method based on set membership theory. Among them, the deterministic method can characterize the system uncertainty with a set membership geometric body such as a centrosymmetric polyhedron, and then based on the set membership operation combined with the set membership geometric body operation logic, after analyzing the model, the theoretical boundary of the system state estimation error and residual can be obtained, thereby realizing the detection of faults. Since the noise characteristics of actual uncertainty are often difficult to obtain accurately, and the approximate range of noise can be obtained relatively easily, therefore, the main purpose of the present invention is to design a quantitative evaluation index for the performance of the fault detection method under the set membership method.

[0044] The invention provides a method and device for designing a minimum detectable fault index of a set member fault detection method and an application thereof in a rotary steerable drilling tool, which can dynamically and automatically calculate the minimum detectable fault index of a rotary steerable drilling tool under a set member fault detection method. The minimum detectable fault index at the moment can be used to evaluate the performance of the set fault detection method, and can be further combined with the observer design process to guide the design of the observer, improve the performance of the set fault detection method, and then enhance the reliability of the rotary steerable drilling tool, improve drilling efficiency, and reduce the cost of oil and gas production.

[0045] Compared with the research on fault detection performance evaluation indicators under the framework of statistical theory, the research results on fault detection performance evaluation indicators under the framework of set membership theory are relatively few. The existing minimum detectable fault analysis method based on invariant sets can only obtain the minimum detectable fault steady-state value in the final steady state, and cannot solve the minimum detectable fault in each dynamic process. In some cases, some systems take a long time to reach a steady state, and even alarm in the dynamic process, but not in the steady state. Therefore, the existing methods are very conservative when analyzing the sensor diagnosability of rotary steerable drilling tools, and even cannot be analyzed. In addition, the strong interference in the drilling process is diverse and complex, which further increases the difficulty of fault diagnosability analysis. Therefore, it is necessary to design the minimum detectable fault indicator for the dynamic process of the system under strong interference.

[0046] An embodiment of the first aspect of the present invention provides a performance evaluation method for a set membership fault detection method, wherein the performance of the fault detection method is evaluated based on a minimum detectable fault index of the fault detection method, wherein the minimum detectable fault index is the distance between the set membership of the faulty residuals and the feasible set of the fault-free residuals of the system to be detected, and the system to be detected is a system for which fault detection is to be performed.

[0047] like Figure 1 As shown, the performance evaluation method includes: S101, for the system to be detected, combining the observer equation used, obtaining the residual system equations of the system in a fault-free state and a faulty state; The system to be detected can be a linear system or a nonlinear system. It only needs to design a suitable observer or processing method to make the residual system of the system to be detected a linear discrete time-invariant system. The following description assumes that the system to be detected is a linear discrete time-invariant system containing a fault. In this embodiment, the linear discrete time-invariant system containing faults is:

[0048] in, , , , , They are System state vector, system input vector, system output vector, measurement noise vector, sensor fault vector, is the system matrix, is the input matrix, is the output matrix, is the noise distribution matrix; is the fault distribution matrix.

[0049] The observer equation is:

[0050] in, , They are The observed values ​​of the system state and output vector at time instant, for The system state vector at time is the observer parameter matrix, The design of affects the observer's estimate of the system state.

[0051] The state estimation error is:

[0052] The residual is:

[0053] The fault-free residual system equation is:

[0054] in, , They are interference and noise when the system is fault-free. for The number of rows in the column vector, for The number of rows in the column vector. , , , is the parameter matrix of the residual system when there is no fault, for The number of rows in the column vector, for The number of rows in the column vector; where and Respectively represent Step and dimensional Euclidean space.

[0055] Substitute the parameters of the linear discrete time-invariant system equation containing the fault and the observer equation parameters into, , , , , interference when the system is fault-free is the system noise , then the system error residual system equation is:

[0056] in and They are Moment and The error in the estimation of the fault-free state at time t; for The residual under the fault-free state at time; The fault residual equation of the system is:

[0057] in, , They are interference and noise when the system fails. , , , , , is the parameter matrix when the residual system has faults, for The number of rows in the column vector.

[0058] Substitute the parameters of the linear discrete time-invariant system equation containing the fault and the observer equation parameters into, , , , , , , interference when the system is fault-free is the system noise , then the residual system equation of the system with fault is:

[0059] in, and They are Moment and The fault state estimation error at time t; for The residual error under fault condition at time.

[0060] The initial estimation error vector and noise vector of the system are unknown but bounded, expressed as , , , , , .in Indicates a A collection of dimensions.

[0061] According to the operation rules and theorems of the corresponding geometric bodies, we can get The feasible set representation of the residual vector of the residual system with and without faults at each moment is: , .

[0062] S102, using a geometric body to characterize the measurement noise range, and combining set membership operation to obtain a residual feasible set characterization of a linear discrete time-invariant system containing a fault in a non-fault state and a fault state; The member aggregate may be a centrosymmetric polyhedron, an ellipsoid, a parallelepiped or other geometrical bodies. Different geometrical bodies have different computational properties. For example, the properties of a centrosymmetric polyhedron are as follows: The centrosymmetric polytope is defined as:

[0063] in, is the center vector of the centrosymmetric polyhedron, is the Minkowski sum operation, Generate a matrix for a centrosymmetric polytope, for the number of rows, is the unit hypercube, is the dimension of the polyhedron.

[0064] The operation rules for centrosymmetric polytopes are:

[0065]

[0066] Among them, the matrix , represents the Minkowski sum, two sets and The Minkowski sum of is defined as , Represents a linear map, a centrosymmetric polyhedron With a matrix The linear mapping of can be obtained through the standard matrix product operation: .

[0067] The method for calculating the residual feasible set in this embodiment is: The residual feasible set is represented by a central symmetric polyhedron, and the central symmetric polyhedron of the system fault-free residual system is:

[0068] in, For trouble-free system Moment residual centrosymmetric polytope, For trouble-free system The center vector of the moment residual centrosymmetric polytope, For trouble-free system Generator matrix of the moment residual centrosymmetric polytope; For trouble-free system The central symmetric polytope of the state estimation error at the moment, For trouble-free system The center vector of the central symmetric polytope of the state estimation error at the moment, For trouble-free system The generator matrix of the central symmetric polytope of the state estimation error at the moment, For trouble-free system The central symmetric polytope of the state estimation error at the moment, For trouble-free system Centrosymmetric polytope that measures noise at every moment; The central symmetric polytope of the faulty residual system is:

[0069] in, For faulty systems The centrosymmetric polytope of the moment residual, For faulty systems The center vector of the moment residual centrosymmetric polytope, For faulty systems Generator matrix of the moment residual centrosymmetric polytope; For faulty systems The central symmetric polytope of the state estimation error at the moment, For faulty systems The center vector of the central symmetric polytope of the state estimation error at the moment, For faulty systems The generator matrix of the central symmetric polytope of the state estimation error at the moment, For faulty systems The central symmetric polytope of the state estimation error at the moment, For faulty systems Centrosymmetric polytope of moment failure.

[0070] S103, determining a minimum detectable fault index of the linear discrete time-invariant system, wherein the minimum detectable fault index is a distance between residual feasible sets of the linear discrete time-invariant system with faults and without faults that maximizes the sampling time.

[0071] The method for calculating the minimum detectable fault index in step S103 is: by calculating the fault state Centrosymmetric polytope of time residuals and fault-free state When the intersection of the centrosymmetric polytopes of the moment residuals is an empty set, The fault with the minimum value is recorded as the minimum detectable fault:

[0072] in, is the minimum value operator, The matrix is ​​the given weight matrix.

[0073] The optimization problem means that the minimum fault value is defined as the minimum fault value that is solved so that the feasible sets of residual vectors with and without faults have no intersection under the corresponding set membership fault detection method. .in The matrix is ​​a weighted matrix, which is used to adjust the solution weight relationship between different faults.

[0074] The intersection of the feasible sets of residual vectors with and without faults is empty, which means that the actual system residual can be judged to be in the membership set of the residual vector set with faults. Or the fault-free residual vector set It is used to determine whether a fault occurs, thereby achieving the purpose of fault detection.

[0075] The intersection of centrosymmetric polytopes is empty. Transformation theorem: Given a centrosymmetric polytope and ,but The necessary and sufficient conditions are:

[0076] Applying the above theorem, let , , we can get Convert the above formula into a mixed integer quadratic programming problem and solve it:

[0077]

[0078]

[0079] in, , , To solve the Lagrange multiplier, for No. elements, for No. elements, For transposition, , is the binary variable to be found, , are the vector and scalar to be determined, and for The number of rows and columns, , Known as The lower and upper bounds of , They are The center and generator matrix of the new polytope generated by applying the polytope intersection empty condition transformation theorem at any time; , ; Calculate the minimum detectable fault index of the fault detection system .

[0080] The quantitative evaluation problem of fault detection performance can be transformed into an optimization problem for solution.

[0081]

[0082] This embodiment calculates the minimum detectable fault index of different set membership fault detection methods. The smaller the minimum detectable fault index is, the smaller the fault that can be detected by the fault detection method is, and the performance of the set membership fault detection method is better. The minimum detectable fault index is related to the design parameters of the observer and the design of the control system. Therefore, by calculating the minimum detectable fault index, it can not only be used to guide the optimization of the observer design, but also can be applied to the control system design and the correction of the system model parameters to make the control system run more stably.

[0083] This embodiment realizes separation of real-time residual sets and solves the minimum detectable fault index; and compares and evaluates the performance of fault diagnosis methods under the set membership framework based on the minimum detectable fault index.

[0084] Compared with the traditional method of using transfer function to perform set membership method to design quantitative indicators for fault diagnosis performance evaluation, the advantages of this method include: 1. Through the fault diagnosis performance quantitative evaluation design method of the present invention, the fault diagnosis performance evaluation under the set membership method can be realized; 2. The fault diagnosis performance quantitative evaluation design method of the present invention can overcome the disadvantages of the invariant set method that loses process information and can only obtain the final minimum detectable fault in the steady state, and can evaluate each fault in the dynamic process of the system. Analyze and solve the minimum detectable fault at the moment; 3. Through the fault diagnosis performance quantitative evaluation design method of the present invention, the performance of the fault diagnosis method based on the set membership method proposed by the rotary steerable drilling tool can be quantitatively evaluated by applying the idea of ​​this method; 4. The evaluation index obtained by the present invention has better applicability for systems that need a long time to reach a steady state and only alarm in a dynamic process under certain circumstances.

[0085] like Figure 2 As shown, the second embodiment of the present invention provides a performance evaluation device 40 for a collective fault detection method, including a data acquisition module 401, a system model parameter identification module 402, a minimum detectable fault index calculation module 403, a communication decoding module 404, and a host computer storage analysis module 405.

[0086] The data acquisition module 401 is used to collect the measurement information output by the sensor of the system under test 50, and transmit the information to the system model parameter identification module 402 through the CAN bus. The sensor is a sensor used to measure various parameters in the system under test 50. The vector measured by the sensor constitutes the state vector of the system state equation, that is, the output vector of the system. The system model parameter identification module 402 is connected to the data acquisition module 401, and is used to identify the system model parameters in real time through the output information measured by the system sensor. The nominal system parameter matrix can be corrected by methods such as particle swarm parameter identification and least square parameter identification to meet the requirements of the minimum detectable fault index design method for the accuracy of model parameters. The minimum detectable fault index calculation module 403 is connected to the system model parameter identification module. After obtaining the parameters of the system model, the minimum detectable fault index at each moment is calculated according to the performance evaluation method of the set membership fault detection method, and transmitted to the communication decoding module 404 through the CAN bus; the communication decoding module 404 is connected to the minimum detectable fault index calculation module 403, and performs communication decoding through the communication decoding module 403 and outputs the index calculation result to the host computer storage analysis module 405. The communication decoding module 404 can be a CAN analyzer, a CAN to USB module, etc. The host computer storage analysis module 405 stores the measurement information output by the sensor, the identified model parameters and the minimum detectable fault index, and analyzes the stored results. Through the measurement information of the sensor, it analyzes whether the system has a fault, what kind of fault has occurred, and the size of the fault; through the identified model parameters, it analyzes whether the design of the observer can be improved; through the minimum detectable fault index, analyzes the quality of different observer design methods and can use it as one of the optimization design indicators of the observer to guide the observer design. The performance evaluation method of the set membership fault detection method is the same as the above embodiment, which will not be repeated here.

[0087] In practical applications, the disturbance situation in the drilling process is complex. There are periodic disturbances caused by the axial, circumferential and torsional vibrations of the drill bit, as well as trend disturbances that increase with drilling depth and pressure. The probabilistic characteristics of uncertainties such as noise are often difficult to obtain accurately, and it is more reasonable to model them as bounded signals.

[0088] In this embodiment, the system to be tested 50 may be a rotary steerable drilling tool, and the data acquisition module 401 is used to collect measurement information output by the rotary steerable drilling tool sensor and transmit the information to the system model parameter identification module 402 via the CAN bus to identify the mathematical model of the rotary steerable drilling tool.

[0089] In the present invention, the uncertainty of the model is characterized by a centrosymmetric polyhedron. There are many fault detection methods proposed under bounded uncertainty. How to select a better fault detection method and further guide the design and optimization of the fault detection method requires the establishment of appropriate quantitative indicators for fault detection performance evaluation. Due to the existence of noise, small faults are easily submerged in the noise signal and difficult to detect. The minimum detectable fault represents how large a fault can be detected in a specific system and under a specific method, and can thus be used as a direct indicator for the performance evaluation of the fault detection method.

[0090] This embodiment collects sensor data of the system to be tested, identifies system model parameters, and realizes dynamic automatic calculation of the minimum detectable fault of the rotary steerable drilling system fault detection method and outputs it to the host computer storage and analysis module 405.

[0091] like Figure 3 As shown, the third aspect of the present invention provides an application of the performance evaluation method of the integrated fault detection system in a rotary steerable drilling tool, comprising the following steps: S201, according to the physical and electrical characteristics of the rotary steerable drilling tool, establish a system model of the rotary steerable drilling tool, identify the model parameters of the rotary steerable drilling tool; combine the system state observer equation to obtain the residual system equations of the system fault-free state and faulty state. The model parameters of the rotary steerable drilling tool can be obtained by the output information obtained by the system sensor measurement through the model parameter identification module, or by mathematical modeling.

[0092] Specifically, the system model of the rotary steerable drilling tool stabilization platform is:

[0093]

[0094] in, for The state vector of the system at the moment, that is, the motor Shaft current , permanent magnet synchronous motor speed , stable platform speed and tool face angle , for The state vector of the system at time instant, , , , and They are System input vector, unknown input vector, output vector, noise vector and fault vector at each moment, is the singular matrix of the known singular system, , and are the known system matrix, input matrix and output matrix respectively, For the system The parameter uncertainty matrix of the matrix, is the unknown input distribution matrix, is the noise distribution matrix, is the fault distribution matrix. The system model parameters can be obtained through system parameter identification or mathematical modeling. In this embodiment, the data model of the rotary steerable drilling tool stabilization platform is obtained through mathematical modeling to initialize the model. The specific system parameters are: , , , , , , , , ,

[0095] in , and For The motor load torque, drill collar speed and Shaft current fault value, is the sampling time interval of the system, , , , , , and They are the system stator winding resistance, stator winding inductance, constant, torque constant, friction factor, total inertia and pole pair number, is the 4-dimensional identity matrix. and They are The perturbation value of stator winding resistance and torque constant parameters at time, for time Model uncertainty of shaft current, For the The current fault amplitude at the sampling point.

[0096] The observer used is of the form:

[0097] Among them, the observer parameter matrix , and Design parameters for the observer, and the observer parameters satisfy .

[0098] Time state estimation error , residual , noise variation , fault variation , the residual system of the rotary steerable drilling tool without fault and with fault is as follows, and the set initialization is performed:

[0099] .

[0100] S202, using a centrosymmetric polyhedron to characterize the measurement noise range of the rotary steerable drilling tool, and obtaining a centrosymmetric polyhedron of the residual system of the rotary steerable drilling tool system based on the computational properties of the centrosymmetric polyhedron.

[0101] A centrosymmetric polytope is defined as:

[0102] in, is the center vector of the centrosymmetric polyhedron, is the Minkowski sum operation, Generate a matrix for a centrosymmetric polytope, is the unit hypercube, is the dimension of the polyhedron.

[0103] Fault-free state estimation error centrosymmetric polytope , residual centrosymmetric polytope The iteration formula is as follows: Central Symmetric Polytope of State Estimation Error of Faulty System , residual centrosymmetric polytope The iteration is expressed as:

[0104] in, is the parameter matrix obtained by taking the maximum value of the matrix parameter model uncertainty, and Respectively Moment and The error in the system state estimation without any fault at any time, and Respectively Moment and There is always a fault system state estimation error, for The system state vector at time for The central symmetric polytope of the system state vector at time, for Generator matrix of the centrosymmetric polytope of the system state vector at time instant; for The noise variation at each moment, for The central symmetric polyhedron of the momentary noise variation, for The generator matrix of the centrosymmetric polytope of the momentary noise variation; for The noise vector at time instant, for The centrosymmetric polytope of the noise vector at time instant, for Generator matrix of the centrosymmetric polytope of the moment noise vector; for The change of fault at any moment, for The central symmetric polyhedron of the moment fault variation, for The center vector of the centrosymmetric polyhedron of the fault variation at each moment; for The moment fault vector, for The centrosymmetric polytope of moment faults, for The central vector of the centrosymmetric polytope of the moment fault vector; , Respectively The residuals of the fault-free system and the faulty system at time instant.

[0105]

[0106]

[0107] In this embodiment, , , is a constant and does not change with time, then , , .

[0108] Then the above formula is:

[0109]

[0110] in, The system is in a fault-free state The central vector of the centrosymmetric polytope of the moment error, The system is in a fault-free state The central vector of the moment error center symmetric polytope, The system is in a fault-free state The centrosymmetric polytope generation matrix of the moment error; The system is in a fault-free state The centrosymmetric polytope center vector of the moment residual, The system is in a fault-free state The center vector of the residual centrosymmetric polytope at the moment, The system is in a fault-free state The centrosymmetric polytope generator matrix of the moment residuals.

[0111] The system is in a fault state The central vector of the centrosymmetric polytope of the moment error, The system is in a fault state The central vector of the moment error center symmetric polytope, The system is in a fault state The centrosymmetric polytope generation matrix of the moment error; The system is in a fault state The centrosymmetric polytope center vector of the moment residual, The system is in a fault state The center vector of the residual centrosymmetric polytope at the moment, The system is in a fault-free state The centrosymmetric polytope generator matrix of the moment residuals.

[0112] , , , They are , To solve the problem of too high dimension caused by Minkowski sum operation, the matrix is ​​reduced in dimension. The dimension reduction process is as follows: consider Centrosymmetric polytope and integers , for dimensional hypercube, and satisfies ,definition For the general The matrix obtained by arranging the column vectors of in descending order of Euclidean norm is , and there are Established.

[0113] in, for dimensional hypercube, Depend on Before column vectors, defined for Remove part, and satisfy: Finally, the residual error of the system with and without fault is calculated. , the centrosymmetric polyhedron in the fault-free state , a centrosymmetric polyhedron with a fault state .

[0114] S203, taking the minimization of the weighted bi-norm of the vector composed of the current fault and its variation as the optimization target, maximizing the distance between the residual central symmetric polytopes in the faulty and non-faulty states of the rotary steerable drilling tool at the current moment, and obtaining the minimum detectable current fault index of the rotary steerable drilling tool system.

[0115] The calculation method of the minimum detectable current fault index is as follows:

[0116] The above formula is transformed into a mixed integer quadratic programming problem. The problem is solved by writing a solution program in MATLAB software using the YALMIP toolkit and using the CPLEX commercial solver:

[0117] in, , , To solve the Lagrange multiplier, for No. elements, for No. elements, , is the binary vector to be found, , are the vector and scalar to be determined, and for The number of rows and columns, , Known as The lower and upper bounds of , .

[0118] Solve the given mixed integer quadratic programming problem scalar , , the solution is the fault vector Amplitude, recalculate the central symmetric polyhedron in the fault-free state , Central symmetric polyhedron with fault state .

[0119] like Figure 4 As shown in the figure, the residual calculation method and fault alarm logic under the centrosymmetric polyhedral method are as follows: S301, model initialization, given the observer parameters designed according to a certain method, set initialization; S302: Calculate residual error when the system is faulty , Centrally symmetric polyhedron in fault-free state , Centrally symmetric polyhedron in faulty state ; S303, the central symmetric polyhedron in the fault-free state , Centrally symmetric polyhedron in faulty state Use the interval envelope to calculate the center of the residuals with and without faults , ; S304. Calculate the boundary of the fault-free residual polyhedron , the boundary of the faulty residual polytope ,in, Expressed as The first row of the array Column elements, is the upper boundary of the fault-free residual polytope, is the lower bound of the fault-free residual polytope, is the upper boundary of the faulty residual polytope, is the lower boundary of the faulty residual polytope, Generate a matrix for a centrosymmetric polytope with no fault residuals And the matrix generated by the centrosymmetric polytope with fault residual The number of columns; S305, calculation fault alarm signal .

[0120] Figure 6 For the ( )to ( ) and calculate the minimum detectable current fault result.

[0121] Figure 7This is the residual result diagram obtained after adding the calculated minimum detectable current fault.

[0122] Figure 8 It is the fault alarm result.

[0123] It can be seen from the test results that the minimum detectable fault designed by the proposed method can achieve the exact separation of the faulty centrally symmetric polyhedron and the fault-free centrally symmetric polyhedron. The centrally symmetric polyhedron can well contain the actual residual signal. The correct fault alarm can be achieved through the inclusion relationship between the actual residual signal and the centrally symmetric polyhedron interval, thus completing the design of the minimum detectable fault under the set membership framework.

[0124] It should be noted that the matrix dimensions mentioned in this patent have appropriate dimensions by default, and the above content is a detailed description made in conjunction with a specific rotary steerable drilling tool stable platform model, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions.

Claims

1. A performance evaluation method for a set membership fault detection method, characterized in that: The method evaluates the performance of the fault detection method according to the minimum detectable fault index of the fault detection method, wherein the minimum detectable fault index is the fault amplitude when the residual feasible set with fault and the residual feasible set without fault of the system to be detected are just separated, and the performance evaluation method includes: S101, for the system to be detected, combining the observer equation used, obtaining the residual system equations of the system to be detected in a non-fault state and a fault state; S102, using a geometric body to characterize the measurement noise range, and combining set membership operation to obtain a residual feasible set characterization of a fault-free state and a faulty state of the system to be detected; S103 , determining a minimum detectable fault index of the system to be detected, wherein the minimum detectable fault index is a fault amplitude when the residual feasible sets of the fault state and the non-fault state of the system to be detected are just separated at the sampling time.

2. The performance evaluation method of a set membership fault detection method according to claim 1, characterized in that: In step S101, the system to be detected is a linear discrete time-invariant system containing a fault, and the linear discrete time-invariant system containing a fault is: in, , , , , They are System state vector, system input vector, system output vector, measurement noise vector, sensor fault vector, for The system state vector at time instant, is the system matrix, is the input matrix, is the output matrix, is the noise distribution matrix; is the fault distribution matrix; The observer equation is: in, , They are The observed values ​​of the system state vector and the output vector at time instant, for The observed value of the system state vector at time is the observer parameter matrix; The state estimation error is: The residual is: The residual system equation of the system without fault is: in, and They are Moment and The error in the estimation of the fault-free state at time t; for The fault-free residual at time t; The residual system equation of the system with fault is: in, and They are Moment and The fault state estimation error at time t; for The faulty residual at time.

3. The performance evaluation method of a set membership fault detection method according to claim 2, characterized in that: In step S102, the method for calculating the residual feasible set is: The residual feasible set is represented by a centrosymmetric polyhedron, and the centrosymmetric polyhedron of the system fault-free residual system is: in, represents the Minkowski sum, represents a linear mapping, For trouble-free system Moment residual centrosymmetric polytope, For trouble-free system The center vector of the moment residual centrosymmetric polytope, For trouble-free system Generator matrix of the moment residual centrosymmetric polytope; For trouble-free system The central symmetric polytope of the state estimation error at the moment, For trouble-free system The center vector of the central symmetric polytope of the state estimation error at the moment, For trouble-free system The generator matrix of the central symmetric polytope of the state estimation error at the moment, For trouble-free system The central symmetric polytope of the state estimation error at the moment, for Centrosymmetric polytope that measures noise at every moment; The central symmetric polytope of the faulty residual system is: in, For faulty systems The centrosymmetric polytope of the moment residual, For faulty systems The center vector of the moment residual centrosymmetric polytope, For faulty systems Generator matrix of the moment residual centrosymmetric polytope; For faulty systems The central symmetric polytope of the state estimation error at the moment, For faulty systems The center vector of the central symmetric polytope of the state estimation error at the moment, For faulty systems The generator matrix of the central symmetric polytope of the state estimation error at the moment, For faulty systems The central symmetric polytope of the state estimation error at the moment, For faulty systems Centrosymmetric polytope of moment failure.

4. The performance evaluation method of a set membership fault detection method according to claim 3, characterized in that: In step S103, the minimum detectable fault index is calculated by calculating the fault state Centrosymmetric polytope of time residuals and fault-free state When the intersection of the centrosymmetric polytopes of the moment residuals is an empty set, The fault with the minimum value is recorded as the minimum detectable fault: in, is the minimum value operator, Matrix is ​​the given weight matrix; Convert the above formula into a mixed integer quadratic programming problem and solve it: in, , , To solve the Lagrange multiplier, for No. elements, for No. elements, For transposition, , is the binary variable to be found, , are the vector and scalar to be determined, and for The number of rows and columns, , Known as The lower and upper bounds of , They are The center and generator matrix of the new polytope generated by applying the polytope intersection empty condition transformation theorem at any time; , , For trouble-free system The generator matrix of the time residual centrosymmetric polytope, For faulty systems The generator matrix of the time residual centrosymmetric polytope, is the weight matrix; Calculate the minimum detectable fault index of the fault detection system .

5. An evaluation device, characterized in that: The evaluation device includes a data acquisition module, a system model parameter identification module, a minimum detectable fault index calculation module, a communication decoding module, and a host computer storage and analysis module; The data acquisition module is used to collect the measurement information output by the sensor of the system to be detected; The system model parameter identification module is connected to the data acquisition module and is used to identify the system model parameters in real time through the output information measured by the sensor, and to modify the nominal system parameter matrix using particle swarm parameter identification and least squares parameter identification methods; The minimum detectable fault index calculation module is connected to the system model parameter identification module, and after obtaining the parameters of the system model, the minimum detectable fault index at each moment is calculated according to the performance evaluation method described in any one of claims 1 to 4; The communication decoding module is connected to the minimum detectable fault index calculation module, performs communication decoding on the minimum detectable fault index and sends the data to the upper computer storage and analysis module; The host computer storage and analysis module stores the measurement information output by the sensor, the identified model parameters and the minimum detectable fault index, and analyzes the stored results.

6. Application of a performance evaluation method for a member fault detection method to a rotary steerable drilling tool, characterized in that: The following steps are involved: S201, establishing a system model of the rotary steerable drilling tool according to the physical and electrical characteristics of the rotary steerable drilling tool, identifying the model parameters of the rotary steerable drilling tool; combining the system state observer equation to obtain the residual system equations of the system in a fault-free state and a faulty state; S202, using a centrosymmetric polyhedron to characterize the measurement noise range of the rotary steerable drilling tool, and obtaining a residual centrosymmetric polyhedron of the rotary steerable drilling tool system based on the computational properties of the centrosymmetric polyhedron; S203. Taking the minimization of the weighted bi-norm of the vector composed of the current fault and its variation as the optimization target, the distance between the residual central symmetric polytopes in the faulty and non-faulty states of the rotary steerable drilling tool at the current moment is maximized to obtain the minimum detectable current fault index of the rotary steerable drilling tool system; the minimum detectable current fault index is the fault amplitude when the residual feasible sets of the faulty state and the non-faulty state of the system to be detected at the sampling moment are just separated.

7. The use according to claim 6, characterized in that: The system model of the rotary steerable drilling tool is: in, for The system state vector at the moment, that is, the motor Shaft current , permanent magnet synchronous motor speed , stable platform speed and tool face angle , for The system state vector at time instant, , , , and They are The system input vector, unknown input vector, system output vector, measurement noise vector and sensor fault vector at each moment, is the singular matrix of the known singular system, , and are the known system matrix, input matrix and output matrix respectively, For the system The parameter uncertainty matrix of the matrix, is the unknown input distribution matrix, is the noise distribution matrix, is the fault distribution matrix; The observer used is of the form: in, , is the observer parameter matrix; the observer parameter matrix satisfies ; is the 4-dimensional identity matrix; for The system output vector at the moment; is the observer parameter matrix; Time state estimation error , residual , noise variation , fault variation , , They are The noise vector and sensor fault vector are measured at the same time, and the residual system of the rotary steerable drilling tool with and without fault is as follows: in, for The error in the estimation of the fault-free state at time t; and They are Moment and The fault state estimation error at time t; for The faulty residual at time.

8. The use according to claim 7, characterized in that: Fault-free state estimation error centrosymmetric polytope , residual centrosymmetric polytope , the central symmetric polytope of the faulty system state estimation error , residual centrosymmetric polytope The iteration is expressed as: in, is the parameter model uncertainty matrix The parameter matrix obtained by taking the maximum value is and Respectively Moment and The error in the system state estimation without any fault at any time, For trouble-free system The center vector of the central symmetric polytope of the state estimation error at the moment, For trouble-free system +1 time state estimation error centrosymmetric polytope generator matrix, For trouble-free system The center vector of the central symmetric polytope of the state estimation error at the moment, For trouble-free system The generator matrix of the central symmetric polytope of the state estimation error at the moment, and Respectively Moment and There is always a fault system state estimation error, For faulty systems The center vector of the central symmetric polytope of the state estimation error at the moment, For faulty systems The generator matrix of the central symmetric polytope of the state estimation error at the moment, For faulty systems The center vector of the central symmetric polytope of the state estimation error at the moment, For faulty systems The generator matrix of the central symmetric polytope of the state estimation error at the moment, for The system state vector at time instant, for The central symmetric polytope of the system state vector at time, for Generator matrix of the centrosymmetric polytope of the system state vector at time instant; for The noise variation at each moment, for The central symmetric polyhedron of the momentary noise variation, for The generator matrix of the centrosymmetric polytope of the momentary noise variation; for The noise vector at time instant, for The centrosymmetric polytope of the noise vector at time instant, for Generator matrix of the centrosymmetric polytope of the moment noise vector; for The change of fault at each moment, for The central symmetric polyhedron of the moment fault variation, for The center vector of the centrosymmetric polyhedron of the fault variation at each moment; for The moment fault vector, for The centrosymmetric polytope of the fault vector at time instant, for The central vector of the centrosymmetric polytope of the moment fault vector; and Respectively The residuals of the fault-free system and the faulty system at time instant, For trouble-free system The center vector of the moment residual centrosymmetric polytope, For trouble-free system The generator matrix of the residual centrosymmetric polytope at time +1, For faulty systems The center vector of the moment residual centrosymmetric polytope, For faulty systems The generator matrix of the residual centrosymmetric polytope at time +1.

9. The use according to claim 8, characterized in that: The calculation method of the centrosymmetric polytope of the state estimation error and residual of the fault-free system is: The calculation method of the centrosymmetric polytope of the faulty system state estimation error and residual is: in, , , , They are , is the matrix after dimensionality reduction, and the dimensionality reduction process is as follows: against Centrosymmetric polytope and integers , and satisfies , is the center vector of the centrosymmetric polyhedron, is the Minkowski sum operation, Generate a matrix for a centrosymmetric polytope, for the number of rows, For the general The matrix obtained by arranging the column vectors of in descending order of Euclidean norm is , and there are Established, among which, for dimensional hypercube, Depend on Before column vectors, for Remove part, the matrix satisfy: , For the matrix No. Line Column elements.

10. The use according to claim 9, characterized in that: The calculation method of the minimum detectable current fault index is as follows: The above formula is transformed into a mixed integer quadratic programming problem. The problem is solved by writing a solution program in MATLAB software using the YALMIP toolkit and using the CPLEX commercial solver: in, , , To solve the Lagrange multiplier, for No. elements, for No. elements, For transposition, , is the binary variable to be found, , are the vector and scalar to be determined, and for The number of rows and columns, , Known as The lower and upper bounds of , .

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