BLDC motor architecture redundancy fault detection method and device

Abstract

The invention relates to the technical field of aircraft fault detection, and discloses a BLDC motor architecture redundancy fault detection method and device, and the method comprises the steps: building a mathematical model of a target motor system; performing rotating speed closed-loop control design on the mathematical model of the target motor system; respectively designing a Kalman filter model and a robust detector model which are used for carrying out fault detection on the target motor system, and combining the Kalman filter model and the robust detector model as fault detection redundancy design; threshold parameters of the Kalman filter model and the robust detector model are optimized based on reliability analysis. According to the method, a redundant fault detection method can be established for the BLDC motor to improve safety, single-point catastrophic faults are prevented, the fault detection threshold value is optimized, the false alarm rate and the omission ratio are effectively reduced, and therefore the overall performance of a detection module is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft fault detection, and for example, to a BLDC motor architecture redundant fault detection method and device. Background Art

[0002] Electric vertical take-off and landing (eVTOL) aircraft is an important equipment carrier for realizing advanced air traffic. eVTOL aircraft usually adopts a distributed power design, which can increase aircraft-level thrust / lift redundancy. As a key system for realizing distributed power, the safety and reliability of the thrust / lift unit are of vital importance.

[0003] Brushless Direct Current (BLDC) motors have the advantages of high efficiency, high torque and fast response, and are the core components for driving and controlling thrust / lift units. However, the complexity of the flight environment and the requirement for long-term operation make BLDC motors face a variety of potential failures, such as electronic component failure, thermal overload and mechanical wear. Therefore, in order to prevent motor failure from affecting flight safety, system redundancy methods are often used to ensure the fail-safe characteristics of the thrust / lift unit system. The conventional redundancy method is to increase hardware redundancy and determine whether the components are working properly by signal comparison. However, the main problems of this method are complex structure, large weight and volume, high cost, and the use of the same hardware may lead to common cause problems. In addition to hardware redundancy, one idea is to increase analytical redundancy signals instead of hardware redundancy. By comparing with the actual system to implement fault detection, the same function as hardware redundancy can be achieved while reducing the weight of the system. For example, in the design process of Airbus A350 and A380, in order to detect the oscillation failure case (OFC) of the electronic flight control system (EFCS), the analytical redundancy method was used to compare the actual deflection of the monitored control surface with the expected ideal functional model, detect the occurrence of actuator failure and evaluate the degree of failure, and support redundancy management decisions. This method provides complete OFC frequency detection coverage in the EFCS of the A380 and has passed the airworthiness review to meet the design safety requirements of transport aircraft.

[0004] From the above analysis, fault detection (FD) is an important means of resolving redundancy. At present, fault detection technology is mainly divided into two categories: model-based methods and data-driven methods. For motors, extensive research has been carried out based on these two fault detection methods at home and abroad. Mekki et al. proposed a fault diagnosis method based on sliding mode detector for estimating phase current. Moseler et al. proposed a model-based brushless DC motor fault detection method using parameter estimation, such as bearing damage or fracture may increase friction. Data-based fault detection is a method that uses monitoring data collected from the system operation process to identify and locate faults. Park et al. proposed a method that uses a database containing phase current and voltage data of BLDC motors under fault conditions. Hosseini et al. introduced a supervised data-based method to detect and classify faults in BLDC motors, namely stator turn-to-turn faults, rotor dynamic imbalance, rotor static imbalance, and different combinations of these faults.

[0005] For civil aircraft, system design needs to meet performance requirements, safety requirements and airworthiness standards. For eVTOL, EASA SC-VTOL.2510 stipulates: "The design and installation of equipment must ensure that: catastrophic failures are extremely unlikely and cannot be caused by a single failure." The clause aims to ensure that the safety of the entire aircraft meets the requirements by proposing strict safety indicators.

[0006] Therefore, for the eVTOL key safety system, in order to meet the airworthiness safety requirements, a redundant mechanism must be established to prevent single-point catastrophic failures from occurring, and the failure probability must meet the extremely unlikely requirement. Summary of the invention

[0007] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0008] In the prior art, there is a problem that a single detection method for eVTOL is difficult to meet safety requirements.

[0009] In some embodiments, a BLDC motor architecture redundant fault detection method is provided, the method comprising: establishing a mathematical model of a target motor system; performing a speed closed-loop control design on the mathematical model of the target motor system; respectively designing a Kalman filter model and a robust detector model for fault detection of the target motor system, and combining the Kalman filter model and the robust detector model as a fault detection redundant design; and optimizing threshold parameters of the Kalman filter model and the robust detector model based on reliability analysis.

[0010] In some embodiments, a BLDC motor architecture redundant fault detection device is disclosed, comprising:

[0011] A mathematical model building module is configured to build a mathematical model of a target motor system;

[0012] A speed closed-loop control design module is configured to perform speed closed-loop control design on a mathematical model of a target motor system;

[0013] A fault detection redundancy design module is configured to respectively design a Kalman filter model and a robust detector model for fault detection of a target motor system, and combine the Kalman filter model and the robust detector model as a fault detection redundancy design;

[0014] The threshold optimization module is configured to optimize threshold parameters of the Kalman filter model and the robust detector model based on reliability analysis.

[0015] The embodiments of the present disclosure provide a BLDC motor architecture redundant fault detection method and device, which can achieve the following technical effects:

[0016] A redundant fault detection method can be established for BLDC motors to improve safety, prevent single-point catastrophic failures, optimize fault detection thresholds, and effectively reduce false alarm and missed detection rates, thereby improving the overall performance of the detection module.

[0017] The foregoing general description and the following description are exemplary and explanatory only and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0019] Figure 1 is a flow chart of a BLDC motor architecture redundant fault detection method provided by an embodiment of the present disclosure;

[0020] Figure 2is a schematic diagram of a BLDC motor equivalent circuit configuration provided by an embodiment of the present disclosure;

[0021] Figure 3 is a schematic diagram of a speed PI control provided by an embodiment of the present disclosure;

[0022] Figure 4 is a schematic diagram of a closed-loop control simulation waveform provided by an embodiment of the present disclosure;

[0023] Figure 5 is a schematic diagram of a dual-redundancy motor architecture provided by an embodiment of the present disclosure;

[0024] Figure 6 is a schematic diagram of a BLDC motor system architecture provided by an embodiment of the present disclosure;

[0025] Figure 7 is a fault detection schematic diagram provided by an embodiment of the present disclosure;

[0026] Figure 8 is a schematic diagram of a robust detector control provided by an embodiment of the present disclosure;

[0027] Fig. 9 It is a schematic diagram of reliability of misdetection and omission detection of a Kalman filter provided by an embodiment of the present disclosure;

[0028] Fig.10 It is a schematic diagram of reliability of misdetection and omission detection of a robust detector provided by an embodiment of the present disclosure;

[0029] Fig.11 It is a schematic diagram of the reliability of misdetection and omission of voting detection of a Kalman filter or a robust detector provided by an embodiment of the present disclosure;

[0030] Fig.12 It is a schematic diagram of the reliability of misdetection and omission of voting detection of a Kalman filter and a robust detector provided by an embodiment of the present disclosure;

[0031] Fig.13 It is a schematic diagram of a BLDC motor architecture redundant fault detection device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0033] The following description and the accompanying drawings fully illustrate specific embodiments of the present invention so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The examples represent possible changes only. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of the present invention includes the entire scope of the claims, and all available equivalents of the claims. In the disclosed embodiments, each embodiment may be represented individually or collectively by the term "invention", which is only for convenience, and if more than one invention is actually disclosed, it is not intended to automatically limit the scope of the application to any single invention or inventive concept. In the disclosed embodiments, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, without requiring or implying any actual relationship or order between these entities or operations. Moreover, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method or electronic device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or electronic device. In the absence of further restrictions, the elements defined by the sentence "comprises a..." do not exclude the presence of other identical elements in the process, method or electronic device including the elements. The various embodiments of the present disclosure are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. For the methods, products, etc. disclosed in the embodiments, since they correspond to the method part disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0034] In the existing technology, for the key safety systems of eVTOL, in order to meet the airworthiness safety requirements, a redundant mechanism must be established to prevent single-point catastrophic failures from occurring, and the failure probability must meet the extremely unlikely requirement.

[0035] For eVTOL, there is a problem that a single detection method is difficult to meet safety requirements.

[0036] In order to solve the problems existing in the related technology, the embodiments of the present disclosure can establish a redundant fault detection method for the BLDC motor to improve safety, prevent single-point catastrophic failures, optimize the fault detection threshold, effectively reduce the false alarm rate and missed detection rate, thereby improving the overall performance of the detection module.

[0037] Combination Figure 1 As shown, the embodiment of the present disclosure provides a flow chart of a BLDC motor architecture redundant fault detection method, including:

[0038] S10, establishing a mathematical model of the target motor system.

[0039] It should be understood that the motor structure diagram of the actual driving lift / thrust unit of the present application is as follows Figure 2 As shown in the figure. The system consists of key components such as power supply, BLDC motor, pulse width modulation (PWM) input and electronic speed control (ESC). The PWM signal controls the voltage and frequency of the motor by adjusting the pulse width of the current, thereby achieving precise regulation of the motor speed and torque. The ESC monitors the operating status of the motor and adjusts the current supplied to the stator winding to ensure that the motor can operate efficiently and stably under various load conditions.

[0040] It should be noted that the motor or target motor in this application refers to a BLDC motor.

[0041] Specifically, assuming that the stator self-inductance varies with the rotor position and the mutual inductance between the stator windings is negligible, the electrical dynamics of the BLDC motor is:

[0042]

[0043] Among them, i a is the motor armature current, L a is the equivalent circuit armature inductance, R a is the equivalent resistance, e b is the motor back EMF, K e is the back EMF constant in V / (rad / s), r is the drive system gear ratio, V a is the voltage applied to the armature;

[0044] The coupled rotor-motor mechanical motion equation is:

[0045]

[0046] Where Ω is the rotor speed, I r is the rotor inertia, J is the inertia of the high-speed drive component, K m is the motor torque constant, Q A is the rotor aerodynamic torque with drive system, B is the linear representation of mechanical friction or viscous loss in the drive system, and the motor torque constant K m Through the relationship K m =cK e , and K eBack EMF constant correlation;

[0047] Combining equations (1) and (2), the basic dynamic response of the coupled rotor-motor direct drive target motor system is obtained:

[0048]

[0049] Transform formula (3) into formula (4):

[0050]

[0051] The time constant of the motor is:

[0052]

[0053] After linearization at the equilibrium point, the input-output transfer function is as follows:

[0054]

[0055] S20, designing a speed closed-loop control for a mathematical model of a target motor system.

[0056] It should be understood that a closed-loop controller is used to make the actual speed of the motor track the speed command. The brushless DC motor control system has high requirements for dynamic response and steady-state response. Therefore, a PI controller is used for the target motor system.

[0057] Specifically, see Figure 3 ,Based on the mathematical model of the target motor system, a closed-loop controller is adopted to make the actual speed of the motor track the speed command;

[0058] The relationship between the given speed and the actual speed is:

[0059]

[0060] Select appropriate control parameters for simulation and give a ramp command with a target speed of 100 rad / s, such as Figure 4 As shown in the figure, when the command signal is stable, the actual speed can reach the set speed command value within 0.08 seconds.

[0061] S30, respectively designing a Kalman filter model and a robust detector model for fault detection of the target motor system, and combining the Kalman filter model and the robust detector model as a fault detection redundancy design.

[0062] It should be understood that for the eVTOL lift / thrust unit being studied, the use of dual motors is more advantageous than a single motor with the same power capacity. If a motor fails, the actuator based on a single motor will stop driving. However, the actuator based on dual motors will not stop and will continue to operate even if the available power decreases. Therefore, the system architecture diagram of using dual redundant motors to drive the propeller is as follows Figure 5 shown.

[0063] exist Figure 5 Based on the architecture, this application uses Kalman filter / robust detector for fault detection, which improves the reliability of detection results while ensuring adaptability and robustness. By comparing different detection methods, the robustness of fault detection can be effectively improved, ensuring that faults can still be accurately identified and diagnosed in complex and changing flight environments, thereby ensuring flight safety.

[0064] like Figure 6 As shown in the figure, the fault detection module uses two independent fault detection methods for analysis, each method obtains a fault judgment result, and the final fault judgment is obtained through logical voting. If a fault is detected in the target motor system, the speed command drive will switch to the backup robust detector. Through this redundant fault detection method, the high reliability and safety of the target motor system are ensured.

[0065] The Kalman filter model and the robust detector model are combined as a fault detection redundancy design. In the fault detection logic, different voting detection strategies can also be used to determine the fault and compare them.

[0066] For example, the fault detection mechanism using the Kalman filter and the robust detector can be detected in both “AND” and “OR” modes. In the “AND” mode, the target motor system is considered to have detected a fault only when the Kalman filter and the robust detector alarm at the same time, thereby greatly reducing the probability of false alarms and improving the detection accuracy and fault tolerance of the system. In the “OR” mode, the system is considered to have detected a fault and triggered an alarm as long as one of the Kalman filter or the robust detector alarms, which improves the sensitivity and response speed of fault detection and ensures that potential problems can be discovered and handled in a timely manner.

[0067] The control block diagram of the single fault detection model is as follows: Figure 7 As shown, the detector outputs the expected result according to the mathematical model and the control instruction, generates a residual by comparing it with the actual output of the target motor system, and generates a fault detection result by analyzing and evaluating the residual signal.

[0068] In the fault detection process, Kalman filters and robust detectors are used to estimate the system state to improve the accuracy and reliability of the diagnostic results. Kalman filters optimize the system noise and measurement noise to obtain the optimal estimate of the target motor system state and are widely used in practical applications; while robust detectors can reduce uncertainty and external interference and enhance the stability and robustness of the system. By combining the two, the system state can be accurately estimated in real time, potential faults can be detected more effectively, and the safe operation of the system can be ensured.

[0069] Design a Kalman filter model for fault detection of the target motor system, including:

[0070] The Kalman filter is used for state estimation. The state of the target motor system is optimally estimated through the two processes of prediction and update. In the prediction stage, the next state and error covariance matrix of the target motor system are calculated. In the update stage, the state estimate and error covariance matrix are updated through the actual measurement values.

[0071] Let Q be the process noise covariance matrix, R be the observation noise covariance matrix;

[0072] The following is the Kalman filter model:

[0073] K k =P k∣k-1 H T (HP k∣k-1 H T +R) -1 (7)

[0074] In the formula is the prior estimate of the state at time k at time k-1, is the optimal estimate of the state at time k-1. k|k-1 is the prior estimate of the covariance matrix at time k-1 for time k, P k-1|k-1 is the optimal estimate of the covariance matrix at time k-1, K k is the Kalman gain, which represents the ratio of model prediction error to measurement error, Z k is the measured observation value, and H is the state observation matrix.

[0075] Design a robust detector model for fault detection on the target motor system, including:

[0076] The robust H-infinity control is used for state estimation, that is, to calculate the optimal feedback K to stabilize the closed-loop control system while minimizing the closed-loop transfer function ||T zw (s)|| ∞ norm;

[0077] The robust detector continuously optimizes the detector gain matrix during the state estimation process to ensure that the target motor system can still operate stably under uncertainty and disturbances. Figure 8 .

[0078] The input and output relationship of the control model is expressed as:

[0079]

[0080] Where u is the control signal, y is the observed quantity, w is the external input quantity, z is the controlled quantity, K(s) is the controller, and P(s) is the augmented control object;

[0081] Divide P(s) into blocks so that it corresponds to the dimensions of the input signals w and u, that is:

[0082]

[0083] The relationship between the target motor system perturbation signal w and the target motor system performance evaluation signal z is obtained as follows:

[0084] z=[P 11 +P 12 K(IP 22 K) -1 P 21 ]w (10)

[0085] Transfer function T zw The expression is:

[0086] T zw (s)=[P 11 +P 12 K(IP 22 K) -1 P 21 ] (11).

[0087] S40, optimizing threshold parameters of the Kalman filter model and the robust detector model based on reliability analysis.

[0088] Reliability analysis refers to the false alarm rate and missed detection rate of the model detection results. The model performance can be evaluated through the confusion matrix, and the model reliability can be analyzed by calculating the probabilities of various failure modes.

[0089] Reliability analysis, including:

[0090] Design calculation formulas for false alarm rate and missed detection rate;

[0091] Based on the calculation formula of false alarm rate and missed detection rate, the threshold parameters of Kalman filter model and robust detector model are optimized.

[0092] Specifically, the calculation formulas for the false alarm rate and missed detection rate are designed, including: defining N as the number of time steps corresponding to the entire finite time window of the fault detection performance specification, N d After a fault occurs, the number of time steps corresponding to the time window when the logic determines that a fault exists;

[0093] θ(k) is the actual state of the model, and θ(k) = 1 means that the model actually fails at time step k; d(k) is the FD logic judgment state, and d(k) = 1 means that the logic judgment model fails at time step k;

[0094] The false alarm and missed detection cases are analyzed. The false alarm rate is PF(N), which is defined as the probability of occurrence when θ(k)=0, k∈[1,N], d(k)=1; the missed detection rate is PM(N), which is defined as the probability of occurrence when k∈[1,N], θ(k)=1. The probability of d(k)=0 occurring.

[0095] See the table below for a table of fault detection and diagnostic logic and performance.

[0096]

[0097] When calculating the false alarm rate, the residual data without faults is analyzed to generate the ARMA model:

[0098]

[0099] Where A is the state matrix, B is the input matrix, C is the output matrix, D is the direct transfer matrix of the input, n(k) is the overall external interference vector, and r(k) is the residual vector;

[0100] The state covariance matrix Σ is obtained by solving the Lyapunov formula x :

[0101] Σ x =AΣ x A T +BΣB T (13)

[0102] Obtain the residual covariance matrix Σ through the Toeplitz matrix N :

[0103]

[0104] Find the probability density function f of the residual N (R N )for:

[0105]

[0106] Where RN =[r(1) r(2) r(3) … r(N)] T is the residual vector of N time steps;

[0107] False alarm rate P FA It is considered as the probability that the residual is greater than the threshold, that is:

[0108]

[0109] When calculating the missed detection rate, a fixed threshold is set, the minimum fault level detected under the threshold is estimated, and the probability of occurrence of a fault level below the threshold is calculated as the missed detection rate;

[0110] Assume the probability of motor failure is F SA For uniform distribution:

[0111] F SA =U(-5 rad / s, 5 rad / s) (17)

[0112] Missed detection rate P MD is equal to the probability that the fault magnitude f is less than the threshold, calculated as:

[0113]

[0114] Based on the calculation formula of false alarm rate and missed detection rate, the threshold parameters of Kalman filter model and robust detector model are optimized, including:

[0115] The reliability analysis calculation based on single Kalman filter fault detection is as follows (19):

[0116]

[0117] See also Fig. 9 , a schematic diagram of the reliability of Kalman filter detection of false detection and omission.

[0118] The reliability analysis calculation based on the fault detection of a single robust detector is as follows (20):

[0119]

[0120] See also Fig.10 , a schematic diagram of the reliability of robust detectors for detecting false detections and omissions.

[0121] Based on the combination of Kalman filter and robust detector, the reliability analysis calculation of the alarm detection mechanism of Kalman filter and robust detector is as follows (21)

[0122]

[0123] See also Fig.11, a schematic diagram of the reliability of false detection and omission detection by voting of Kalman filter or robust detector.

[0124] Based on the combination of Kalman filter and robust detector, the reliability analysis calculation of the simultaneous alarm detection mechanism of Kalman filter and robust detector is as follows (22)

[0125]

[0126] See also Fig.12 , a schematic diagram of the reliability of false detection and omission detection by voting with Kalman filter and robust detector.

[0127] Furthermore, the method further comprises:

[0128] Short runs are preferred to optimize the threshold parameters of the Kalman filter model and the robust detector model.

[0129] It can accurately identify faults in a short time, reduce subsequent losses, and ensure the safety of the target motor system. In the simulation, the speed of the detection results can be analyzed by observing the actual speed, estimated speed, residual and other images of the motor system.

[0130] Fig.13 A BLDC motor architecture redundant fault detection device according to an embodiment of the present invention is shown, the device comprising:

[0131] A mathematical model building module is configured to build a mathematical model of a target motor system;

[0132] A speed closed-loop control design module is configured to perform speed closed-loop control design on a mathematical model of a target motor system;

[0133] A fault detection redundancy design module is configured to respectively design a Kalman filter model and a robust detector model for fault detection of a target motor system, and combine the Kalman filter model and the robust detector model as a fault detection redundancy design;

[0134] The threshold optimization module is configured to optimize threshold parameters of the Kalman filter model and the robust detector model based on reliability analysis.

[0135] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent possible changes only. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in the present invention are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in the present invention refers to any and all possible combinations of one or more associated listings. In addition, when used in the present invention, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the presence of other identical elements in the process, method or electronic device including the elements. In the embodiments of the present disclosure, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, the relevant parts may refer to the description of the method part.

[0136] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present disclosure can be implemented with electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the above-described devices, devices and units may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0137] The flowchart and block diagram in the accompanying drawings show the possible architecture, functions and operations of the device, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by dedicated hardware-based devices that perform the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A BLDC motor architecture redundant fault detection method, characterized in that: include: Establish a mathematical model of the target motor system; Design speed closed-loop control for the mathematical model of the target motor system; A Kalman filter model and a robust detector model are designed for fault detection of a target motor system respectively, and the Kalman filter model and the robust detector model are combined as a fault detection redundancy design; The threshold parameters of the Kalman filter model and the robust detector model are optimized based on reliability analysis.

2. The BLDC motor architecture redundancy fault detection method according to claim 1, characterized in that: Build a mathematical model of the target motor system, including: Assuming that the stator self-inductance varies with the rotor position and the mutual inductance between the stator windings is negligible, the electrical dynamics of the BLDC motor is: Among them, i a is the motor armature current, L a is the equivalent circuit armature inductance, R a is the equivalent resistance, e b is the motor back EMF, K e is the back EMF constant in V / (rad / s), r is the drive system gear ratio, V a is the voltage applied to the armature; The coupled rotor-motor mechanical motion equation is: Where Ω is the rotor speed, I r is the rotor inertia, J is the inertia of the high-speed drive component, K m is the motor torque constant, Q A is the rotor aerodynamic torque with drive system, B is the linear representation of mechanical friction or viscous loss in the drive system, and the motor torque constant K m Through the relationship K m =cK e , and K e Back EMF constant correlation; Combining equations (1) and (2), the basic dynamic response of the coupled rotor-motor direct drive target motor system is obtained: Transform formula (3) into formula (4): The time constant of the motor is: After linearization at the equilibrium point, the input-output transfer function is as follows:

3. The BLDC motor architecture redundancy fault detection method according to claim 2, characterized in that: Design speed closed-loop control for the mathematical model of the target motor system, including: Based on the mathematical model of the target motor system, a closed-loop controller is used to make the actual speed of the motor track the speed command; The relationship between the given speed and the actual speed is: Select appropriate control parameters for simulation and give a ramp command for the target speed.

4. The BLDC motor architecture redundancy fault detection method according to claim 3, characterized in that: Design a Kalman filter model for fault detection of the target motor system, including: The Kalman filter is used for state estimation. The state of the target motor system is optimally estimated through the two processes of prediction and update. The next state and error covariance matrix of the target motor system are calculated in the prediction stage. The state estimation and error covariance matrix are updated by the actual measurement values ​​in the update stage. Let Q be the process noise covariance matrix, R be the observation noise covariance matrix; The following is the Kalman filter model: K k =P k∣k-1 H T (HP k∣k-1 H T +R) -1 (7) In the formula is the prior estimate of the state at time k at time k-1, is the optimal estimate of the state at time k-1; P k|k-1 is the prior estimate of the covariance matrix at time k-1 for time k, P k-1|k-1 is the optimal estimate of the covariance matrix at time k-1, K k is the Kalman gain, which represents the ratio of model prediction error to measurement error, Z k is the measured observation value, and H is the state observation matrix.

5. The BLDC motor architecture redundancy fault detection method according to claim 3, characterized in that: Design a robust detector model for fault detection on the target motor system, including: The robust H-infinity control is used for state estimation, that is, to calculate the optimal feedback K to stabilize the closed-loop control system while minimizing the closed-loop transfer function ||T zw (s)|| ∞ norm; The robust detector continuously optimizes the detector gain matrix during the state estimation process to ensure that the target motor system can still operate stably under uncertainty and disturbance; The input and output relationship of the control model is expressed as: Where u is the control signal, y is the observed quantity, w is the external input quantity, z is the controlled quantity, K(s) is the controller, and P(s) is the augmented control object; Divide P(s) into blocks so that it corresponds to the dimensions of the input signals w and u, that is: The relationship between the target motor system perturbation signal w and the target motor system performance evaluation signal z is obtained as follows: z=[P 11 +P 12 K(I-P 22 K) -1 P 21 ]w (10) Transfer function T zw The expression is: T zw (s)=[P 11 +P 12 K(I-P 22 K) -1 P 21 ] (11)。 6. The BLDC motor architecture redundancy fault detection method according to claim 5, characterized in that: Reliability analysis, including: Design calculation formulas for false alarm rate and missed detection rate; Based on the calculation formula of false alarm rate and missed detection rate, the threshold parameters of Kalman filter model and robust detector model are optimized.

7. The BLDC motor architecture redundancy fault detection method according to claim 6, characterized in that: Design the calculation formula of false alarm rate and missed detection rate, including: Define N as the number of time steps corresponding to the entire finite time window of the fault detection performance specification, N d After a fault occurs, the number of time steps corresponding to the time window when the logic determines that a fault exists; θ(k) is the actual state of the model, and θ(k) = 1 means that the model actually fails at time step k; d(k) is the FD logic judgment state, and d(k) = 1 means that the logic judgment model fails at time step k; The false alarm and missed detection cases are analyzed. The false alarm rate is PF(N), which is defined as the probability of occurrence when θ(k)=0, k∈[1,N], d(k)=1; the missed detection rate is PM(N), which is defined as the probability of occurrence when k∈[1,N], θ(k)=1. The probability of d(k) = 0 occurring; When calculating the false alarm rate, the residual data without faults is analyzed to generate the ARMA model: Where A is the state matrix, B is the input matrix, C is the output matrix, D is the direct transfer matrix of the input, n(k) is the overall external interference vector, and r(k) is the residual vector; The state covariance matrix Σ is obtained by solving the Lyapunov formula x : S x =AS x A T +BΣB T (13) Obtain the residual covariance matrix Σ through the Toeplitz matrix N : Find the probability density function f of the residual N (R N )for: Where R N =[r(1) r(2) r(3) … r(N)] T is the residual vector of N time steps; False alarm rate P FA It is considered as the probability that the residual is greater than the threshold, that is: When calculating the missed detection rate, a fixed threshold is set, the minimum fault level detected under the threshold is estimated, and the probability of occurrence of a fault level below the threshold is calculated as the missed detection rate; Assume the probability of motor failure is F SA For uniform distribution: F SA =U(-5 rad / s,5 rad / s) (17) Missed detection rate P MD is equal to the probability that the fault magnitude f is less than the threshold, calculated as:

8. The BLDC motor architecture redundancy fault detection method according to claim 7, characterized in that: Based on the calculation formula of false alarm rate and missed detection rate, the threshold parameters of Kalman filter model and robust detector model are optimized, including: The reliability analysis calculation based on single Kalman filter fault detection is as follows (19): The reliability analysis calculation based on the fault detection of a single robust detector is as follows (20): Based on the combination of Kalman filter and robust detector, the reliability analysis calculation of the alarm detection mechanism of Kalman filter and robust detector is as follows (21) Based on the combination of Kalman filter and robust detector, the reliability analysis calculation of the simultaneous alarm detection mechanism of Kalman filter and robust detector is as follows (22) 9. The BLDC motor architecture redundancy fault detection method according to claim 1, characterized in that: The method further comprises: Short runs are preferred to optimize the threshold parameters of the Kalman filter model and the robust detector model.

10. A BLDC motor architecture redundant fault detection device, characterized in that: include: A mathematical model building module is configured to build a mathematical model of a target motor system; A speed closed-loop control design module is configured to perform speed closed-loop control design on a mathematical model of a target motor system; a fault detection redundancy design module configured to respectively design a Kalman filter model and a robust detector model for fault detection of a target motor system, and combine the Kalman filter model and the robust detector model as a fault detection redundancy design; The threshold optimization module is configured to optimize threshold parameters of the Kalman filter model and the robust detector model based on reliability analysis.

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