Demagnetization diagnosis method and system based on multi-cascade expansion observer

Abstract

The invention discloses a demagnetization diagnosis method and system based on a multi-cascade expansion observer, and relates to the technical field of permanent magnet motor control. The method comprises a data acquisition step, a coordinate conversion step, a motor model construction step, a state equation definition step, an ESO model construction step, an inductance identification step and a demagnetization fault diagnosis step. The system is simplified while high observation precision is guaranteed, the requirement for parameter adjustment is lowered, and the method is worthy of deep research.

Description

Technical Field

[0001] This invention relates to the field of permanent magnet motor control technology, and in particular to a demagnetization diagnosis method and system based on a multi-cascaded extended observer. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs), with their high power density, excellent dynamic response, and low loss characteristics, have become the core power source for industrial robot joint drives, service robot chassis, and precision surgical robotic arms. However, under high-frequency start-stop, overload operation, and complex working conditions, the permanent magnets of PMSMs face a severe risk of demagnetization, directly threatening system reliability. Experimental studies have shown that 50% local demagnetization can lead to an increase in motor torque pulsation of over 30%, causing robot trajectory deviation, increased vibration, and even loss of control.

[0003] To address the fault diagnosis problem of permanent magnet synchronous motors (PMSMs), traditional methods largely focus on online monitoring technologies. The first approach is based on current signal analysis. For example, Liu Chaohua et al., in Chinese patent CN111398811A ("PMSM Demagnetization Fault Diagnosis Method Based on Terminal Current Cost-Sensitive Learning"), disclose a method that collects fault current signals, constructs a cost-sensitive large-interval stepper motor model, and uses fault information for training and testing to diagnose motor faults. The second approach is based on artificial intelligence. For instance, Wu Qinmu et al., in Chinese patent CN117269754B ("IPMSM Rotor Demagnetization and Eccentricity Fault Diagnosis Method Based on Convolutional Neural Network Technology"), disclose a method that constructs models of normal and demagnetized motors, builds a GCNN model based on skip connections and dilated convolutional spatial pooling pyramids, and then diagnoses motor faults. While these methods achieve motor fault diagnosis to some extent, they are limited by the internal spatial structure and computing power of robots, making it difficult to meet the actual needs of robots.

[0004] Therefore, proposing a demagnetization diagnosis method and system based on a multi-cascaded extended observer to solve the problems existing in the prior art is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, and addressing the limitations of traditional flux linkage observers in practical robot applications, this invention provides a demagnetization diagnosis method and system based on a multi-cascaded extended observer. First, a motor model is established to address permanent magnet demagnetization and inductor parameter mismatch disturbances. Then, a multi-cascaded distributed online parameter identification model based on an extended state observer is designed to identify inductance and flux linkage. Finally, the extracted flux linkage disturbance is used to calculate the real-time flux linkage, enabling demagnetization fault determination and quantification of the demagnetization degree. This invention simplifies the system and reduces the need for parameter adjustment while maintaining high observation accuracy, making it worthy of further investigation.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: An online demagnetization fault diagnosis method for permanent magnet synchronous motors used in robots based on multi-cascaded extended observers includes the following steps: S1 Data Acquisition Steps: Measure and acquire data from the permanent magnet synchronous motor. a , b , c Three-phase current is denoted as i a , i b , i c Electric angular velocity of the motor oh e and rotor position i ; S2 coordinate transformation steps: Based on rotor position i The three-phase current i a 、i b and i c Convert to d axis, q shaft current i d , i q ; S3 motor model construction steps: using d axis, q shaft current i d , i q and d axis, q Shaft control voltage u d , u q and the electric angular velocity of the motor oh e Construct a permanent magnet synchronous motor model that includes disturbances; S4 State Equation Definition Steps: Redefine the state equations based on the constructed permanent magnet synchronous motor model containing disturbances. d - q Axis state equations; S5 ESO model construction steps: Based on the redefined d - q Constructing the ESO model using the axis state equations; S6 q Shaft inductor identification steps: Perform q Axial inductance identification; S7 d Axis inductance identification steps: Obtain q The shaft inductance is updated in the ESO model to achieve... d Identification of axial inductance and magnetic flux; S8 Demagnetization Fault Diagnosis Steps: Utilize the permanent magnet flux disturbance Δ caused by motor parameter mismatch. ψ f and the rotor permanent magnet flux of the motor ψ f0 Calculate the degree of demagnetization.

[0007] The above method, optionally, includes the following specific steps for S2 coordinate transformation: According to rotor position i The three-phase current i a 、i b and i c Convert to d axis, q shaft current i d , i q The conversion formula is as follows: .

[0008] Optionally, the permanent magnet motor model containing the disturbance in S3, as described above, is as follows:

[0009] in, u d , u q For motor d axis 、q Shaft voltage; i d , i q For motor d axis 、q shaft current;R s The stator windings of the motor, L d0 , L q0 They are respectively d axis 、q Shaft inductance; ψ f0 For the rotor permanent magnet flux linkage of the motor, Δ L d Δ L q and Δ ψ f These represent the d-axis inductance disturbance, q-axis inductance disturbance, and permanent magnet flux disturbance caused by motor parameter mismatch, respectively.

[0010] The above method can optionally be redefined in S4. d - q The shaft state equations are as follows: ;

[0011] in, F 1d and F 1q It is a known interference. F 2d and F 2q They are dq Unknown disturbances in the axis extended state observer.

[0012] The above method, optionally, allows for the construction of the following ESO model in S5:

[0013] in, z 1dq , z 2dq They are respectively i dq , F 2dq The estimated value, β 1. β 2 represents the gain coefficient. 1dq for F 1dq The estimated value, , They are respectively z 1dq , z 2dq The minute components.

[0014] The above method, optionally, includes the following specific content for S6: q Shaft inductance identification is shown in the following formula: ; When the motor is running in a stable state, d With the shaft current differential term set to 0, the equation simplifies to the following: .

[0015] The above method, optionally, will yield the following in S7: q The shaft inductance is updated in the ESO model to achieve... d The specific steps for identifying shaft inductance and magnetic flux are as follows: Inject two different d Shaft reference current i d1 and i d2 Solve the simultaneous equations to find Δ L d and Δ ψ f , ; .

[0016] Optionally, the degree of demagnetization in S8 can be calculated using the following formula: ; The above formula can be used to determine the degree of demagnetization of a permanent magnet synchronous motor.

[0017] An online demagnetization fault diagnosis system for a permanent magnet synchronous motor for a robot based on a multi-cascaded extended observer, which executes an online demagnetization fault diagnosis method for a permanent magnet synchronous motor for a robot based on a multi-cascaded extended observer as described above, including: a data acquisition module, a coordinate transformation module, a motor model construction module, a state equation definition module, an ESO model construction module, an inductor identification module, and a demagnetization fault diagnosis module. The data acquisition module is used to measure and acquire the data of the permanent magnet synchronous motor. a , b , c Three-phase current is denoted as i a , i b , i c Electric angular velocity of the motor oh e and rotor position i ; The coordinate transformation module is used to transform the rotor position. i The three-phase current i a 、i b and i c Convert to d axis, q shaft current i d , i q ; Motor model building module, used to utilize d axis, q shaft current i d , i q and d axis, q Shaft control voltage u d , u q and the electric angular velocity of the motor oh e Construct a permanent magnet synchronous motor model that includes disturbances; The state equation definition module is used to redefine the state equations based on the constructed permanent magnet synchronous motor model containing disturbances. d - q Axis state equations; ESO model building module, used to build upon the redefined d - q Constructing the ESO model using the axis state equations; Inductor identification module, used for performing q Axis inductance identification, the obtained q The shaft inductance is updated in the ESO model to achieve... d Identification of axial inductance and magnetic flux; The demagnetization fault diagnosis module is used to detect the permanent magnet flux disturbance Δ caused by motor parameter mismatch. ψ f and the rotor permanent magnet flux of the motor ψ f0 Calculate the degree of demagnetization.

[0018] As can be seen from the above technical solution, compared with the prior art, the present invention provides a demagnetization diagnosis method and system based on a multi-cascaded extended observer, which has the following beneficial effects: This invention is based on a multi-cascaded extended state observer. By updating the motor inductance parameters in real time, it effectively eliminates the influence of motor parameter mismatch on flux observation, thereby realizing online demagnetization fault diagnosis of the motor. Compared with traditional methods, this invention ensures observation accuracy while only requiring stator voltage and current signals, without the need for additional sensors. It avoids the problems of current sensor failure at high temperatures and signal-to-noise ratio degradation of vibration sensors under strong electromagnetic interference, making it particularly suitable for extreme working conditions. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart of an online demagnetization fault diagnosis method for permanent magnet synchronous motors used in robots based on a multi-cascaded extended observer, as disclosed in this invention. Figure 2 This is a block diagram of an online demagnetization fault diagnosis system for permanent magnet synchronous motors used in robots based on a multi-cascaded extended observer, as disclosed in this invention. Figure 3 This is a schematic diagram of an online demagnetization fault diagnosis system for permanent magnet synchronous motors used in robots based on a multi-cascaded extended observer, as disclosed in this invention. Figure 4 This is a structural diagram of the multi-cascaded observer disclosed in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0023] Permanent magnet synchronous motors (PMSMs) are widely used in robot joints, but they suffer from demagnetization faults. Traditional methods primarily rely on adding extra sensors to acquire flux linkage information. However, these methods are limited by the structure and algorithms of robot joints, making it difficult to achieve high-precision flux linkage observation in extreme operating conditions. This invention proposes an online demagnetization fault diagnosis method for PSMs used in robots based on a multi-cascaded extended observer. First, a motor model is established to address permanent magnet demagnetization and inductance parameter mismatch disturbances. Then, a multi-cascaded distributed online parameter identification model based on an extended state observer is designed to identify inductance and flux linkage. Finally, the extracted flux linkage disturbance is used to calculate the real-time flux linkage, enabling demagnetization fault determination and quantification of the demagnetization degree. The specific technical solution is as follows: See Figure 1 As shown, this invention discloses an online demagnetization fault diagnosis method for permanent magnet synchronous motors used in robots based on a multi-cascaded extended observer, comprising the following steps: S1 Data Acquisition Steps: Measure and acquire data from the permanent magnet synchronous motor. a , b , c Three-phase current is denoted as i a , i b , i c Electric angular velocity of the motor oh e and rotor position i ; S2 coordinate transformation steps: Based on rotor position i The three-phase current i a 、i b and i c Convert to d axis, q shaft current id , i q ; S3 motor model construction steps: using d axis, q shaft current i d , i q and d axis, q Shaft control voltage u d , u q and the electric angular velocity of the motor oh e Construct a permanent magnet synchronous motor model that includes disturbances; S4 State Equation Definition Steps: Redefine the state equations based on the constructed permanent magnet synchronous motor model containing disturbances. d - q Axis state equations; S5 ESO model construction steps: Based on the redefined d - q Constructing the ESO model using the axis state equations; S6 q Shaft inductor identification steps: Perform q Axial inductance identification; S7 d Axis inductance identification steps: Obtain q The shaft inductance is updated in the ESO model to achieve... d Identification of axial inductance and magnetic flux; S8 Demagnetization Fault Diagnosis Steps: Utilize the permanent magnet flux disturbance Δ caused by motor parameter mismatch. ψ f and the rotor permanent magnet flux of the motor ψ f0 Calculate the degree of demagnetization.

[0024] Furthermore, the specific details of the S2 coordinate transformation steps are as follows: According to rotor position i The three-phase current i a 、i b and i c Convert to d axis, q shaft current i d , i q The conversion formula is as follows: .

[0025] Furthermore, the permanent magnet motor model containing perturbations in S3 is as follows: ; in, u d , u q For motor d axis 、q Shaft voltage; i d , i q For motor d axis 、q shaft current; R s The stator windings of the motor, L d0 , L q0 They are respectively d axis 、q Shaft inductance; ψ f0 For the rotor permanent magnet flux linkage of the motor, Δ L d Δ L q and Δ ψ f These represent the d-axis inductance disturbance, q-axis inductance disturbance, and permanent magnet flux disturbance caused by motor parameter mismatch, respectively.

[0026] Furthermore, in S4, it is redefined d - q The shaft state equations are as follows: ; ; in, F 1d and F 1q It is a known interference. F 2d and F 2q They are dq Unknown disturbances in the axis extended state observer.

[0027] Furthermore, S5 can build such as Figure 4 The ESO model shown below has the following formula: ; in, z 1dq , z 2dq They are respectivelyi dq , F 2dq The estimated value, β 1. β 2 represents the gain coefficient. 1dq for F 1dq The estimated value, , They are respectively z 1dq , z 2dq The minute components.

[0028] Furthermore, the specific details of S6 are as follows: q Shaft inductance identification is shown in the following formula: ; When the motor is running in a stable state, it can d The differential term of the shaft current is approximately 0, which simplifies to the following equation: .

[0029] Furthermore, what will be obtained in S7 q The shaft inductance is updated in the ESO model to achieve... d The specific steps for identifying shaft inductance and magnetic flux are as follows: Inject two different d Shaft reference current i d1 and i d2 Solve the simultaneous equations to find Δ L d and Δ ψ f , ; .

[0030] Furthermore, the degree of demagnetization in S8 is calculated using the following formula: ; The above formula can be used to determine the degree of demagnetization of a permanent magnet synchronous motor.

[0031] and Figure 1 Corresponding to the method shown, this invention also discloses an online demagnetization fault diagnosis system for permanent magnet synchronous motors used in robots based on a multi-cascaded extended observer, which performs the following... Figure 1 The online demagnetization fault diagnosis method for permanent magnet synchronous motors used in robots based on multi-cascaded extended observers is illustrated in the following system block diagram: Figure 2 As shown, the schematic diagram is as follows: Figure 3 As shown, it includes: a data acquisition module, a coordinate transformation module, a motor model construction module, a state equation definition module, an ESO model construction module, an inductor identification module, and a demagnetization fault diagnosis module; The data acquisition module is used to measure and acquire the data of the permanent magnet synchronous motor. a , b , c Three-phase current is denoted as i a , i b , i c Electric angular velocity of the motor oh e and rotor position i ; The coordinate transformation module is used to transform the rotor position. i The three-phase current i a 、i b and i c Convert to d axis, q shaft current i d , i q ; Motor model building module, used to utilize d axis, q shaft current i d , i q and d axis, q Shaft control voltage u d , u q and the electric angular velocity of the motor oh e Construct a permanent magnet synchronous motor model that includes disturbances; The state equation definition module is used to redefine the state equations based on the constructed permanent magnet synchronous motor model containing disturbances. d - q Axis state equations; ESO model building module, used to build upon the redefined d - q Constructing the ESO model using the axis state equations; Inductor identification module, used for performing q Axis inductance identification, the obtained q The shaft inductance is updated in the ESO model to achieve... d Identification of axial inductance and magnetic flux; The demagnetization fault diagnosis module is used to detect the permanent magnet flux disturbance Δ of the motor rotor permanent magnet caused by motor parameter mismatch. ψ f and the rotor permanent magnet flux of the motor ψ f0 Calculate the degree of demagnetization.

[0032] In one specific embodiment, the electrical parameters of the permanent magnet synchronous motor are as follows: Inductance is L d0 =0.00157mH, L q0 =0.001837mH, resistance is R s =0.6Ω, rotor flux is ψ f0 =0.18Wb, the number of permanent magnet pairs is p =4, rated speed is 1000 rpm, control cycle is T s =0.0002s, moment of inertia of the motor J =0.0001, coefficient of friction B =0.0022.

[0033] Experimental selection d With shaft currents of -1A and -4A, the results are as follows: I d1 =-1A, i q1 =-3.15A; I d2 =-4A, i q2 =-3.36A; The final demagnetization degree was calculated to be 27% according to the demagnetization degree formula in S8.

[0034] The above embodiments are only one application embodiment of the present invention and are not the only protection scope of the present invention. Other embodiments will not be described in detail here.

[0035] For the system or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and relevant details can be found in the description of the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0036] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for online demagnetization fault diagnosis of permanent magnet synchronous motors for robots based on multi-cascaded extended observers, characterized in that, Includes the following steps: S1 Data Acquisition Steps: Measure and acquire data from the permanent magnet synchronous motor. a , b , c Three-phase current is denoted as i a , i b , i c Electric angular velocity of the motor ω e and rotor position θ ; S2 coordinate transformation steps: Based on rotor position θ The three-phase current i a 、i b and i c Convert to d axis, q shaft current i d , i q ; S3 motor model construction steps: using d axis, q shaft current i d , i q and d axis, q Shaft control voltage u d , u q and the electric angular velocity of the motor ω e Construct a permanent magnet synchronous motor model that includes disturbances; S4 State Equation Definition Steps: Redefine the state equations based on the constructed permanent magnet synchronous motor model containing disturbances. d - q Axis state equations; S5 ESO model construction steps: Based on the redefined d - q Constructing the ESO model using the axis state equations; S6 q Shaft inductor identification steps: Perform q Axial inductance identification; S7 d Axis inductance identification steps: Obtain q The shaft inductance is updated in the ESO model to achieve... d Identification of axial inductance and magnetic flux; S8 Demagnetization Fault Diagnosis Steps: Utilize the permanent magnet flux disturbance Δ caused by motor parameter mismatch. ψ f and the rotor permanent magnet flux of the motor ψ f0 Calculate the degree of demagnetization.

2. The method for online demagnetization fault diagnosis of permanent magnet synchronous motors for robots based on multi-cascaded extended observers according to claim 1, characterized in that, The specific steps of the S2 coordinate transformation are as follows: According to rotor position θ The three-phase current i a 、i b and i c Convert to d axis, q shaft current i d , i q The conversion formula is as follows: 。 3. The method for online demagnetization fault diagnosis of permanent magnet synchronous motors for robots based on multi-cascaded extended observers according to claim 2, characterized in that, The permanent magnet motor model containing disturbances in S3 is as follows: ； in, u d , u q For motor d axis 、q Shaft voltage; i d , i q For motor d axis 、q shaft current; R s The stator windings of the motor, L d0 , L q0 They are respectively d axis 、q Shaft inductance; ψ f0 For the rotor permanent magnet flux linkage of the motor, Δ L d Δ L q and Δ ψ f These are caused by mismatch in motor parameters. d Shaft inductance disturbance, q Shaft inductance disturbance and permanent magnet flux disturbance.

4. The method for online demagnetization fault diagnosis of permanent magnet synchronous motors for robots based on multi-cascaded extended observers according to claim 3, characterized in that, Redefining in S4 d - q The shaft state equations are as follows: ； ； in, F 1d and F 1q It is a known interference. F 2d and F 2q They are dq Unknown disturbances in the axis extended state observer.

5. The online demagnetization fault diagnosis method for permanent magnet synchronous motors in robots based on a multi-cascaded extended observer according to claim 4, characterized in that, The ESO model built in S5 is as follows: ； in, z 1dq , z 2dq They are respectively i dq , F 2dq The estimated value, β 1. β 2 represents the gain coefficient. 1dq for F 1dq The estimated value, , They are respectively z 1dq , z 2dq The minute components.

6. The method for online demagnetization fault diagnosis of permanent magnet synchronous motors for robots based on multi-cascaded extended observers according to claim 5, characterized in that, The specific content of S6 is as follows: q Shaft inductance identification is shown in the following formula: ； When the motor is running in a stable state, d With the shaft current differential term set to 0, the equation simplifies to the following: 。 7. The method for online demagnetization fault diagnosis of permanent magnet synchronous motors for robots based on multi-cascaded extended observers according to claim 6, characterized in that, S7 will receive q The shaft inductance is updated in the ESO model to achieve... d The specific steps for identifying shaft inductance and flux linkage are as follows: Inject two different ones in sequence d Shaft reference current i d1 and i d2 Solve the simultaneous equations to find Δ L d and Δ ψ f , ； 。 8. The method for online demagnetization fault diagnosis of permanent magnet synchronous motors for robots based on multi-cascaded extended observers according to claim 7, characterized in that, The degree of demagnetization in S8 is calculated using the following formula: ； The above formula can be used to determine the degree of demagnetization of a permanent magnet synchronous motor.

9. An online demagnetization fault diagnosis system for permanent magnet synchronous motors used in robots based on multi-cascaded extended observers, characterized in that, The method for online demagnetization fault diagnosis of permanent magnet synchronous motor for robots based on multi-cascaded extended observers as described in any one of claims 1-8 includes: a data acquisition module, a coordinate transformation module, a motor model construction module, a state equation definition module, an ESO model construction module, an inductor identification module, and a demagnetization fault diagnosis module. The data acquisition module is used to measure and acquire the data of the permanent magnet synchronous motor. a , b , c Three-phase current is denoted as i a , i b , i c Electric angular velocity of the motor ω e and rotor position θ ; The coordinate transformation module is used to transform the rotor position. θ The three-phase current i a 、i b and i c Convert to d axis, q shaft current i d , i q ; Motor model building module, used to utilize d axis, q shaft current i d , i q and d axis, q Shaft control voltage u d , u q and the electric angular velocity of the motor ω e Construct a permanent magnet synchronous motor model that includes disturbances; The state equation definition module is used to redefine the state equations based on the constructed permanent magnet synchronous motor model containing disturbances. d - q Axis state equations; ESO model building module, used to build upon the redefined d - q Constructing the ESO model using the axis state equations; Inductor identification module, used for performing q Axis inductance identification, the obtained q The shaft inductance is updated in the ESO model to achieve... d Identification of axial inductance and magnetic flux; The demagnetization fault diagnosis module is used to detect the permanent magnet flux disturbance Δ caused by motor parameter mismatch. ψ f and the rotor permanent magnet flux of the motor ψ f0 Calculate the degree of demagnetization.

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