Fault judgment method and device of rolling bearing, electronic equipment and storage medium

Abstract

The invention provides a fault judgment method and device for a rolling bearing, electronic equipment and a storage medium, and relates to the technical field of motors. The fault judgment method for the rolling bearing comprises the following steps: in response to a condition that a to-be-detected motor reaches a stable state, generating a target voltage vector according to a radial force injection voltage vector and a reference voltage vector; inputting the target voltage vector into a vector pulse width modulation module of a driving motor; collecting a radial vibration signal at a bearing seat of the rolling bearing, and extracting a radial stiffness time-varying spectrum of the rolling bearing from the radial vibration signal; extracting the kurtosis of the radial stiffness time-varying characteristic frequency band and the characteristic frequency corresponding to the peak value from the radial stiffness time-varying spectrum; and determining the fault condition of the rolling bearing according to the kurtosis and the characteristic frequency. According to the invention, the early slight fault of the rolling bearing can be detected efficiently and accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of motors, and in particular, to a method, device, electronic device and storage medium for fault discrimination of rolling bearings. Background Art

[0002] As a key component in a rotating machinery system, a rolling bearing plays a crucial role in the field of mechanical engineering. However, due to various reasons, a rolling bearing may fail prematurely, and its common fault modes include single-point faults and distributed faults. Early minor faults of rolling bearings are often difficult to diagnose, and may deteriorate rapidly under harsh operating conditions, leading to system collapse.

[0003] Currently, most of the bearing condition monitoring and fault diagnosis are based on vibration signals. For single-point faults, the bearing characteristic frequency (BCF) is usually used for diagnosis, and fault features are extracted through methods such as frequency domain analysis, Hilbert-Huang transform (HHT) and resonance demodulation. For distributed faults, it is mainly diagnosed by monitoring the change of band energy. However, the existing solutions have obvious limitations: on the one hand, since early minor defects of bearings hardly introduce obvious fault impacts or band energy changes, the diagnosis effect of minor bearing faults in a strong noise background is poor; on the other hand, there is currently a lack of a comprehensive diagnosis method that can reliably detect and distinguish single-point faults and distributed faults of bearings at the same time.

[0004] Therefore, how to efficiently and accurately detect early minor faults of rolling bearings is a technical problem to be solved urgently. Summary of the Invention

[0005] The present invention provides a method, device, electronic device and storage medium for fault discrimination of rolling bearings, so as to solve the defect that early minor faults of rolling bearings cannot be accurately detected in the prior art, and achieve efficient and accurate detection of early minor faults of rolling bearings.

[0006] The present invention provides a method for fault discrimination of rolling bearings, including the following steps.

[0007] In response to the motor to be detected reaching a steady state, a target voltage vector is generated according to the radial force injection voltage vector and the reference voltage vector; wherein, the radial force injection voltage vector is generated by using a PI controller according to the target injection current of the motor; the amplitude and frequency of the target injection current are determined according to the frequency and duration of the target radial force to be applied to the rolling bearing of the motor; the reference voltage vector is a voltage command signal generated to drive the motor to rotate according to the target speed; the target voltage vector is input into the vector pulse width modulation module for driving the motor, so that the vector pulse width modulation module applies the target radial force to the rolling bearing of the motor while driving the motor to rotate according to the target speed; the radial vibration signal at the bearing housing of the rolling bearing is collected, and the time-varying spectrum of the radial stiffness of the rolling bearing is extracted from the radial vibration signal; the kurtosis and the characteristic frequency corresponding to the peak value of the time-varying characteristic frequency band of the radial stiffness are extracted from the time-varying spectrum of the radial stiffness; wherein, the time-varying characteristic frequency band of the radial stiffness is the performance region in the frequency domain of the time-varying characteristics of the radial stiffness related to the bearing fault of the rolling bearing; the fault condition of the rolling bearing is determined according to the kurtosis and the characteristic frequency.

[0008] According to a method for discriminating the fault of a rolling bearing provided by the present invention, the radial force injection voltage vector is generated in the following manner: the three-phase current for driving the motor is collected; by using a preset harmonic separation algorithm and a dq transformation algorithm, the harmonic component corresponding to the frequency of the target injection current in the three-phase current is converted into the dq-axis current in the rotating coordinate system with the same frequency as the target injection current, and the feedback dq-axis current is obtained; the PI controller is used to process the amplitude difference between the target injection current and the feedback dq-axis current to obtain the dq-axis voltage to be injected; the preset inverse transformation algorithm is used to convert the dq-axis voltage to be injected into the radial force injection voltage vector.

[0009] According to a method for discriminating the fault of a rolling bearing provided by the present invention, the reference voltage vector is generated in the following manner: according to the current position information and the reference angular velocity of the motor, the reference q-axis current is generated by using a PI controller; according to the three-phase current of the motor, the feedback dq-axis current is obtained by using the synchronous rotating coordinate transformation; wherein, the feedback dq-axis current includes the feedback d-axis current and the feedback q-axis current; according to the target injection current, the compensation q-axis current is generated by using the torque compensation transformation algorithm; based on the reference q-axis current, the feedback q-axis current and the compensation q-axis current, the q-axis voltage command is generated by using a PI controller; based on the d-axis current reference value of the motor and the feedback d-axis current, the d-axis voltage command is generated by using a PI controller; based on the d-axis voltage command and the q-axis voltage command, the reference voltage vector is generated by using the rotation transformation.

[0010] A method for fault discrimination of a rolling bearing provided by the present invention, the extracting of the time-varying spectrum of the radial stiffness of the rolling bearing from the radial vibration signal includes: performing band-pass filtering on the radial vibration signal according to the frequency of the target radial force; extracting the signal envelope of the filtered radial vibration signal by using a preset signal extraction algorithm; obtaining the time-varying spectrum of the radial stiffness of the rolling bearing according to the signal envelope, the target rotational speed of the motor, and the amplitude of the radial force currently borne by the rolling bearing; wherein, the amplitude of the radial force currently borne by the rolling bearing is determined by collecting the three-phase current driving the motor.

[0011] A method for fault discrimination of a rolling bearing provided by the present invention, the extracting of the kurtosis of the time-varying characteristic frequency band of the radial stiffness from the time-varying spectrum of the radial stiffness includes: extracting the kurtosis of the time-varying characteristic frequency band of the radial stiffness by using the following formula: wherein, E( ) is the expectation, and X is the amplitude of the time-varying characteristic frequency band of the radial stiffness.

[0012] A method for fault discrimination of a rolling bearing provided by the present invention, the determining of the fault condition of the rolling bearing according to the kurtosis and the characteristic frequency includes: comparing the kurtosis with a preset kurtosis threshold; if the kurtosis is less than the kurtosis threshold, it is determined that the rolling bearing has a distribution fault; if the kurtosis is not less than the kurtosis threshold, it is determined whether the characteristic frequency is within the range of the single-point fault frequency; wherein, the single-point fault frequency reflects the change of the time-varying frequency of the radial stiffness of the bearing system caused by the defect area during the rotation of the rolling bearing; if the characteristic frequency is within the range of the single-point fault frequency, it is determined that the rolling bearing has a single-point fault.

[0013] The present invention also provides a fault discrimination device for a rolling bearing, including the following modules: a generation module, configured to generate a target voltage vector according to a radial force injection voltage vector and a reference voltage vector in response to the motor to be detected reaching a stable state; wherein, the radial force injection voltage vector is generated by using a PI controller according to a target injection current of the motor; the amplitude and frequency of the target injection current are determined according to the frequency and duration of a target radial force to be applied to the rolling bearing of the motor; the reference voltage vector is a voltage command signal generated to drive the motor to rotate in accordance with a target speed; a driving module, configured to input the target voltage vector into a vector pulse width modulation module for driving the motor, so that while the vector pulse width modulation module drives the motor to rotate in accordance with the target speed, the target radial force is applied to the rolling bearing of the motor; a first extraction module, configured to collect a radial vibration signal at a bearing housing of the rolling bearing and extract a time-varying spectrum of the radial stiffness of the rolling bearing from the radial vibration signal; a second extraction module, configured to extract the kurtosis and the characteristic frequency corresponding to the peak value of a time-varying characteristic frequency band of the radial stiffness from the time-varying spectrum of the radial stiffness; wherein, the time-varying characteristic frequency band of the radial stiffness is a performance region in the frequency domain of the time-varying characteristics of the radial stiffness related to the bearing fault of the rolling bearing; a determination module, configured to determine the fault condition of the rolling bearing according to the kurtosis and the characteristic frequency.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the computer program, the fault discrimination method for the rolling bearing as described in any one of the above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the fault discrimination method for the rolling bearing as described in any one of the above is implemented.

[0016] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the fault discrimination method for the rolling bearing as described in any one of the above is implemented.

[0017] The fault discrimination method, device, electronic device and storage medium of the rolling bearing provided by the present invention generate a target voltage vector by using a radial force injection voltage vector and a reference voltage vector after the motor to be detected reaches a stable state, and input it into the vector pulse width modulation module for driving the motor. Since while the driving motor rotates at a target speed, a target radial force can be applied to the rolling bearing of the motor, thereby exciting the time-varying characteristics of the radial stiffness of the rolling bearing. Subsequently, the radial vibration signal at the bearing housing of the rolling bearing is collected, and the time-varying spectrum of the radial stiffness of the rolling bearing that can accurately reflect the fault of the rolling bearing is extracted therefrom. Further, the kurtosis and the characteristic frequency corresponding to the peak value of the time-varying characteristic frequency band of the radial stiffness are extracted from the time-varying spectrum of the radial stiffness. Finally, according to the kurtosis and the characteristic frequency, the fault condition of the rolling bearing can be accurately determined, effectively overcoming the problem in the prior art that it is difficult to diagnose the slight fault of the rolling bearing in time and accurately due to the lack of impact or frequency band characteristics, and realizing the online extraction and fault diagnosis of the time-varying characteristics of the radial stiffness of the rolling bearing. Thus, the early slight fault of the rolling bearing can be detected efficiently and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic flowchart of the fault discrimination method for the rolling bearing provided by the present invention.

[0020] Figure 2 It is a schematic flowchart of the method for generating the radial force injection voltage vector provided by the present invention.

[0021] Figure 3 It is a schematic flowchart of the method for generating the reference voltage vector provided by the present invention.

[0022] Figure 4 It is a schematic diagram of the process of the fault discrimination method for the rolling bearing provided by the present invention.

[0023] Figure 5 It is a schematic diagram of the motor radial force injection scheme provided by the present invention.

[0024] Figure 6 It is a schematic diagram of the acquisition process of obtaining the time-varying spectrum of the radial stiffness and the fault discrimination process of the rolling bearing provided by the present invention.

[0025] Figure 7 It is a schematic structural diagram of the fault discrimination device for the rolling bearing provided by the present invention.

[0026] Figure 8 It is a schematic structural diagram of the electronic device provided by the present invention. Specific embodiments

[0027] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0028] The following will be described in conjunction with Figures 1 - 6 a method for fault discrimination of the rolling bearing of the present invention.

[0029] Figure 1 It is a schematic flow diagram of the method for fault discrimination of the rolling bearing provided by the present invention. As Figure 1 shown, the method includes the following: Step 101, in response to the motor to be detected reaching a stable state, generate a target voltage vector according to the radial force injection voltage vector and the reference voltage vector.

[0030] The radial force injection voltage vector is a voltage command signal generated to generate a specific radial force on the rolling bearing of the motor. The radial force injection voltage vector includes the voltage component in the α-axis direction in the α-β coordinate system of the three-phase voltage (such as Figure 5 shown in ) and the voltage component in the β-axis direction (such as Figure 5 shown in ) In the specific implementation process, the radial force injection voltage vector is generated by using a PI controller according to the target injection current of the motor (such as Figure 5 shown in and ); the amplitude and frequency of the target injection current can be determined according to the frequency and duration of the target radial force to be applied to the rolling bearing of the motor.

[0031] The PI controller (proportional-integral controller) is a commonly used feedback controller. It realizes precise regulation of the system output by combining the proportional (P) and integral (I) control actions. The proportional term (P) can quickly respond to the error, while the integral term (I) can eliminate the steady-state error.

[0032] For specific embodiments of generating the radial force injection voltage vector, refer to Figure 2 the relevant content therein, which will not be elaborated here.

[0033] The reference voltage vector is a voltage command signal generated to drive the motor to rotate according to the target speed. The reference voltage vector includes the voltage component in the α-axis direction in the α-β coordinate system of the three-phase voltage (such as Figure 5 shown in ), and the voltage component in the β-axis direction (such as Figure 5 shown in ). For the specific embodiments of generating the reference voltage vector, refer to the relevant content in Figure 3 , which will not be elaborated here.

[0034] In the specific implementation process, the radial force injection voltage vector and the reference voltage vector can be superimposed to generate the target voltage vector. As shown in Figure 5 , the radial force injection voltage vector ( and ) and the reference voltage vector ( and ) are respectively used as inputs and added through an adder module to generate the target voltage vector. The target voltage vector is then used to control the SVPWM (Space Vector Pulse Width Modulation) module to apply a radial force to the rolling bearing while achieving precise control of the motor. The adder module realizes the addition operation of the radial force injection voltage vector and the reference voltage vector in the α-β plane.

[0035] Step 102: Input the target voltage vector into the vector pulse width modulation module for driving the motor, so that the vector pulse width modulation module applies a target radial force to the rolling bearing of the motor while driving the motor to rotate according to the target speed.

[0036] After the target voltage vector is input into the vector pulse width modulation (SVPWM) module, the module generates corresponding pulse width modulation signals according to the input voltage vector. These pulse width modulation signals are used to control the drive circuit of the motor to make the motor rotate at the target speed. During the rotation of the motor, a specific radial force is generated by controlling the magnetic field and current distribution inside the motor. This radial force is transmitted to the rolling bearing through the electromagnetic design and mechanical structure of the motor, thereby realizing the application of the radial force to the rolling bearing.

[0037] Step 103: Collect the radial vibration signal at the bearing housing of the rolling bearing, and extract the time-varying spectrum of the radial stiffness of the rolling bearing from the radial vibration signal.

[0038] In the specific implementation process, a sensor can be used to collect the radial vibration signal at the bearing housing of the rolling bearing.

[0039] In the specific implementation process, the time-varying spectrum of the radial stiffness of the rolling bearing can be extracted from the radial vibration signal in the following way: Band-pass filter the radial vibration signal according to the frequency of the target radial force; use a preset signal extraction algorithm (e.g., Hilbert transform) to extract the signal envelope of the filtered radial vibration signal.

[0040] Based on the signal envelope, as well as the target rotational speed of the motor and the amplitude of the radial force currently borne by the rolling bearing, obtain the time-varying spectrum of the radial stiffness of the rolling bearing.

[0041] In some embodiments, the signal envelope can be subjected to a fast Fourier transform, and frequency normalization and amplitude normalization are sequentially performed according to the target rotational speed of the motor and the amplitude of the radial force currently borne by the rolling bearing to obtain the time-varying spectrum of the radial stiffness. Among them, as Figure 4 shown, the amplitude of the radial force currently borne by the rolling bearing can be determined by collecting the three-phase current of the drive motor.

[0042] Step 104: Extract the kurtosis and the characteristic frequency corresponding to the peak value of the time-varying characteristic frequency band of the radial stiffness from the time-varying spectrum of the radial stiffness.

[0043] The time-varying characteristic frequency band of the radial stiffness is the performance region in the frequency domain of the time-varying characteristics of the radial stiffness related to the bearing fault of the rolling bearing.

[0044] In some embodiments, the following formula can be used to extract the kurtosis of the time-varying characteristic frequency band of the radial stiffness: (1) where E( ) is the expectation and X is the amplitude of the time-varying characteristic frequency band of the radial stiffness.

[0045] In the specific implementation process, the peak value of the time-varying characteristic frequency band of the radial stiffness can be identified through various algorithms (e.g., local maximum search algorithm), which is not limited by the description in this specification.

[0046] Step 105: Determine the fault condition of the rolling bearing according to the kurtosis and the characteristic frequency.

[0047] In the specific implementation process, as Figure 6 shown, the kurtosis can be compared with a preset kurtosis threshold; if the kurtosis is less than the kurtosis threshold, it is determined that the rolling bearing has a distributed fault.

[0048] A distributed fault is manifested as a whole-circle wear on the inner raceway or outer raceway of the rolling bearing. Existing research has paid little attention to this type of fault, but it is also an important form of rolling bearing fault. Existing research believes that different from single-point faults, distributed faults do not show obvious fault characteristic frequencies in the vibration signal, but produce unpredictable energy changes within a certain frequency band, so the kurtosis can be used to determine whether the rolling bearing has this type of fault.

[0049] If the kurtosis is not less than the kurtosis threshold, determine whether the characteristic frequency is within the range of the single-point fault frequency; wherein, the single-point fault frequency reflects the change in the frequency of the defect area and the time-varying frequency of the radial stiffness of the bearing system during the rotation of the rolling bearing.

[0050] If the characteristic frequency is within the range of the single-point fault frequency, it is determined that the rolling bearing has a single-point fault. A single-point fault can be divided into an outer-race fault, an inner-race fault, a rolling-element fault, and a cage fault according to the position of the defect.

[0051] If the kurtosis is not less than the kurtosis threshold, determine whether the characteristic frequency is within the range of the single-point fault frequency.

[0052] If the kurtosis is not less than the kurtosis threshold and the characteristic frequency is not within the range of the single-point fault frequency, it can be determined that the rolling bearing has not failed.

[0053] Figure 2 It is a schematic flowchart of the method for generating the radial force injection voltage vector provided by the present invention. As Figure 2 shown, the method includes the following: Step 201, collect the three-phase current of the drive motor.

[0054] Step 202, use a preset harmonic separation algorithm and dq transformation algorithm to convert the harmonic components corresponding to the frequency of the target injection current in the three-phase current into dq-axis currents in the rotating coordinate system with the same frequency as the target injection current, and obtain the feedback dq-axis currents.

[0055] In the specific implementation process, the Fourier transform (FFT) can be performed on the current signals of each phase collected. The FFT converts the time-domain signal into a frequency-domain signal, thereby obtaining the amplitude and phase of each frequency component in the current. In the obtained spectrum, the amplitude and phase corresponding to the frequency of the target injection current (such as 50 Hz) are extracted. Then, according to the extracted amplitude and phase information, a three-phase harmonic current corresponding to the target frequency is constructed. The Park transform is used to convert the three-phase harmonic current into dq-axis currents, that is, the feedback dq-axis currents composed of ld_in and lq_in as Figure 5 shown are obtained, realizing a harmonic current extraction module as Figure 5 shown.

[0056] Step 203, use a PI controller to process the amplitude difference between the target injection current and the feedback dq-axis currents to obtain the dq-axis voltage to be injected.

[0057] This step realizes a harmonic current feedback module as Figure 5 shown.

[0058] In the specific implementation process, the error of the d-axis current is obtained by calculating the difference between the d-axis current component in the target injection current and the d-axis current component in the feedback dq-axis current (ΔId = Id_in_ref - Id_in); the error of the q-axis current is obtained by calculating the difference between the q-axis current component in the target injection current and the q-axis current component in the feedback dq-axis current (ΔIq = Iq_in_ref - Iq_in).

[0059] The error ΔId of the d-axis current is input into the PI controller of the d-axis; the error ΔIq of the q-axis current is input into the PI controller of the q-axis.

[0060] The PI controller performs proportional (P) and integral (I) operations on the input error to generate corresponding voltage compensation values: the PI controller of the d-axis outputs the voltage compensation value of the d-axis ; the PI controller of the q-axis outputs the voltage compensation value of the q-axis . Finally, the voltage compensation value of the d-axis and the voltage compensation value of the q-axis are combined to obtain the dq-axis voltage to be injected.

[0061] Step 204: Use a preset inverse transformation algorithm to convert the dq-axis voltage to be injected into a radial force injection voltage vector.

[0062] This step realizes the part of generating the radial force injection voltage vector in the reference voltage superposition module as shown in Figure 5 .

[0063] In the specific implementation process, the dq-axis voltage to be injected and are sent into a 2r / 2s(θ_inj) converter (which can be implemented using the Clarke transformation algorithm). The function of this converter is to convert the dq-axis voltage into α-axis and β-axis voltages, that is, to obtain the radial force injection voltage vector and .

[0064] Figure 3 is a schematic flow diagram of the method for generating the reference voltage vector provided by the present invention. As shown in Figure 3 , this method includes the following:[[]]END]] Step 301: Generate a reference q-axis current using a PI controller according to the current position information and reference angular velocity of the motor.

[0065] In the specific implementation process, the current position information of the motor can be obtained through the encoder of the motor system. In the motor control system, the encoder monitors the rotation of the motor shaft and converts the mechanical motion into an electrical signal, thereby realizing the accurate measurement and feedback of the current position of the motor.

[0066] By differentiating the position information output by the encoder with respect to time or calculating the number of pulses per unit time, the actual angular velocity of the motor can be estimated.

[0067] As Figure 5 shown, the actual angular velocity of the motor and the reference angular velocity are input into a PI controller to generate a reference q-axis current .

[0068] Step 302: Based on the three-phase current of the motor, use synchronous rotation coordinate transformation to obtain the feedback dq-axis current.

[0069] The feedback dq-axis current includes a feedback d-axis current and a feedback q-axis current.

[0070] The synchronous rotation coordinate transformation is implemented based on the Park transformation, which converts the current in the three-phase stationary coordinate system (abc coordinate system) to the synchronous rotation coordinate system (dq coordinate system). In the synchronous rotation coordinate transformation, the d-axis is aligned with the magnetic pole direction of the rotor, and the q-axis is perpendicular to the d-axis. Through the synchronous rotation coordinate transformation, the three-phase alternating current is converted into two direct current components, namely the d-axis current and the q-axis current.

[0071] As Figure 5 shown, the three-phase current of the motor collected is used with the synchronous rotation coordinate transformation 3s / 2r(θ_r) to obtain the feedback dq-axis current and .

[0072] Step 303: Based on the target injection current, use the torque compensation transformation algorithm to generate a compensated q-axis current.

[0073] In motor control, the dq-axis current represents the current components of the motor on the d-axis (direct axis) and the q-axis (quadrature axis). These current components are directly related to the torque and magnetic field strength of the motor.

[0074] The radial force injection voltage vector obtained from the target injection current (see the relevant content in Figure 2 ) will affect the torque of the motor. Therefore, a compensated q-axis current needs to be generated to offset this effect.

[0075] The torque compensation transformation algorithm calculates the torque value to be compensated represented by the q-axis current based on the angle of the target injection current and the rotation angle of the motor. For example, the torque deviation can be obtained by comparing the angle of the target injection current and the rotation angle of the motor; according to the magnitude and direction of the torque deviation, a compensation strategy is determined, and the current value to be compensated is calculated.

[0076] As Figure 5As shown, the target injection current is applied, and the synchronous rotation coordinate transformation 2r(θ_inj) / 2r(θ_r) is used to obtain the compensated q-axis current .

[0077] Step 304: Based on the reference q-axis current, the feedback q-axis current, and the compensated q-axis current, a PI controller is used to generate a q-axis voltage command.

[0078] As Figure 5 shown, the reference q-axis current , the feedback q-axis current , and the compensated q-axis current are input into the PI controller to obtain the generated q-axis voltage command .

[0079] Step 305: Based on the d-axis current reference value of the motor and the feedback d-axis current, a PI controller is used to generate a d-axis voltage command.

[0080] As Figure 5 shown, the d-axis current reference value of the motor and the feedback d-axis current are used with a PI controller to generate a d-axis voltage command .

[0081] Step 306: Based on the d-axis voltage command and the q-axis voltage command, a rotation transformation is used to generate a reference voltage vector.

[0082] Rotation transformations (e.g., Clarke transformation, Park transformation) are used to convert the d-axis and q-axis voltage signals into voltage signals in the α-β coordinate system. This process is based on the rotation characteristics of the motor and is achieved through rotating coordinate systems.

[0083] As Figure 5 shown, by performing the rotation transformation 2r / 2r(θ_r) on the d-axis voltage command and the q-axis voltage command , a reference voltage vector is generated. The reference voltage vector includes the voltage components on the α-axis and β-axis: and , and these two values together form a voltage vector in the α-β plane, representing the voltage direction and magnitude required for motor control.

[0084] Next, the fault discrimination device for rolling bearings provided by the present invention will be described. The fault discrimination device for rolling bearings described below can be mutually corresponded and referred to with the fault discrimination method for rolling bearings described above.

[0085] Figure 7 is a schematic structural diagram of the fault discrimination device for rolling bearings provided by the present invention. As Figure 7As shown, the device 700 includes the following modules.

[0086] A generating module 710, configured to generate a target voltage vector according to a radial force injection voltage vector and a reference voltage vector in response to the motor to be detected reaching a steady state; wherein, the radial force injection voltage vector is generated by using a PI controller according to a target injection current of the motor; an amplitude and a frequency of the target injection current are determined according to a frequency and a duration of a target radial force to be applied to a rolling bearing of the motor; the reference voltage vector is a voltage command signal generated to drive the motor to rotate in accordance with a target speed; A driving module 720, configured to input the target voltage vector into a vector pulse width modulation module for driving the motor, so that the vector pulse width modulation module applies the target radial force to the rolling bearing of the motor while driving the motor to rotate in accordance with the target speed; A first extraction module 730, configured to collect a radial vibration signal at a bearing housing of the rolling bearing, and extract a time-varying spectrum of the radial stiffness of the rolling bearing from the radial vibration signal; A second extraction module 740, configured to extract a kurtosis of a time-varying characteristic frequency band of the radial stiffness and a characteristic frequency corresponding to a peak value from the time-varying spectrum of the radial stiffness; wherein, the time-varying characteristic frequency band of the radial stiffness is a performance region in a frequency domain of a time-varying characteristic of the radial stiffness related to a bearing fault of the rolling bearing; A determination module 750, configured to determine a fault condition of the rolling bearing according to the kurtosis and the characteristic frequency.

[0087] Figure 8 An entity structure diagram of an electronic device is exemplified, as Figure 8As shown, the electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communication bus 840. Among them, the processor 810, the communications interface 820, and the memory 830 complete mutual communication through the communication bus 840. The processor 810 may call logic instructions in the memory 830 to execute a method for fault discrimination of a rolling bearing. The method includes: in response to the motor to be detected reaching a stable state, generating a target voltage vector according to a radial force injection voltage vector and a reference voltage vector; wherein, the radial force injection voltage vector is generated by using a PI controller according to the target injection current of the motor; the amplitude and frequency of the target injection current are determined according to the frequency and duration of the target radial force to be applied to the rolling bearing of the motor; the reference voltage vector is a voltage command signal generated to drive the motor to rotate according to a target speed; inputting the target voltage vector into a vector pulse width modulation module for driving the motor, so that the vector pulse width modulation module applies the target radial force to the rolling bearing of the motor while driving the motor to rotate according to the target speed; collecting a radial vibration signal at the bearing housing of the rolling bearing, and extracting a time-varying spectrum of the radial stiffness of the rolling bearing from the radial vibration signal; extracting the kurtosis and the characteristic frequency corresponding to the peak value of the time-varying characteristic frequency band of the radial stiffness from the time-varying spectrum of the radial stiffness; wherein, the time-varying characteristic frequency band of the radial stiffness is a performance area in the frequency domain of the time-varying characteristics of the radial stiffness related to the bearing fault of the rolling bearing; determining the fault condition of the rolling bearing according to the kurtosis and the characteristic frequency.

[0088] In addition, when the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. And the aforementioned storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program codes.

[0089] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the fault discrimination method of the rolling bearing provided by the above-mentioned various methods. The method includes: in response to the motor to be detected reaching a stable state, generating a target voltage vector according to the radial force injection voltage vector and the reference voltage vector; wherein, the radial force injection voltage vector is generated by using a PI controller according to the target injection current of the motor; the amplitude and frequency of the target injection current are determined according to the frequency and duration of the target radial force to be applied to the rolling bearing of the motor; the reference voltage vector is a voltage command signal generated to drive the motor to rotate according to the target speed; inputting the target voltage vector into the vector pulse width modulation module that drives the motor, so that the vector pulse width modulation module applies the target radial force to the rolling bearing of the motor while driving the motor to rotate according to the target speed; collecting the radial vibration signal at the bearing housing of the rolling bearing, and extracting the time-varying spectrum of the radial stiffness of the rolling bearing from the radial vibration signal; extracting the kurtosis and the characteristic frequency corresponding to the peak value of the radial stiffness time-varying characteristic frequency band from the time-varying spectrum of the radial stiffness; wherein, the radial stiffness time-varying characteristic frequency band is the performance region in the frequency domain of the time-varying characteristics of the radial stiffness related to the bearing fault of the rolling bearing; determining the fault condition of the rolling bearing according to the kurtosis and the characteristic frequency.

[0090] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a fault discrimination method for a rolling bearing provided by the above-mentioned various methods. The method includes: in response to the motor to be detected reaching a stable state, generating a target voltage vector according to a radial force injection voltage vector and a reference voltage vector; wherein, the radial force injection voltage vector is generated by using a PI controller according to a target injection current of the motor; the amplitude and frequency of the target injection current are determined according to the frequency and duration of a target radial force to be applied to the rolling bearing of the motor; the reference voltage vector is a voltage command signal generated to drive the motor to rotate in accordance with a target speed; inputting the target voltage vector into a vector pulse width modulation module for driving the motor, so that while the vector pulse width modulation module drives the motor to rotate in accordance with the target speed, applying the target radial force to the rolling bearing of the motor; collecting a radial vibration signal at a bearing housing of the rolling bearing, and extracting a time-varying spectrum of the radial stiffness of the rolling bearing from the radial vibration signal; extracting the kurtosis and the characteristic frequency corresponding to the peak value of a time-varying characteristic frequency band of the radial stiffness from the time-varying spectrum of the radial stiffness; wherein, the time-varying characteristic frequency band of the radial stiffness is a performance region in the frequency domain of a time-varying characteristic of the radial stiffness related to a bearing fault of the rolling bearing; and determining the fault condition of the rolling bearing according to the kurtosis and the characteristic frequency.

[0091] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0092] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for fault discrimination of a rolling bearing, characterized in that, Including: In response to the motor to be detected reaching a steady state, a target voltage vector is generated according to a radial force injection voltage vector and a reference voltage vector; wherein, the radial force injection voltage vector is generated by using a PI controller according to a target injection current of the motor; the amplitude and frequency of the target injection current are determined according to the frequency and duration of a target radial force to be applied to a rolling bearing of the motor; the reference voltage vector is a voltage command signal generated to drive the motor to rotate according to a target speed; The target voltage vector is input into a vector pulse width modulation module for driving the motor, so that the vector pulse width modulation module applies the target radial force to the rolling bearing of the motor while driving the motor to rotate according to the target speed; Collect a radial vibration signal at a bearing housing of the rolling bearing, and extract a time-varying spectrum of the radial stiffness of the rolling bearing from the radial vibration signal; Extract the kurtosis and the characteristic frequency corresponding to the peak value of a time-varying characteristic frequency band of the radial stiffness from the time-varying spectrum of the radial stiffness; wherein, the time-varying characteristic frequency band of the radial stiffness is a performance region in the frequency domain of the time-varying characteristics of the radial stiffness related to a bearing fault of the rolling bearing; Determine the fault condition of the rolling bearing according to the kurtosis and the characteristic frequency.

2. The fault discrimination method of the rolling bearing according to claim 1, wherein The radial force injection voltage vector is generated in the following manner: Collect three-phase currents for driving the motor; Using a preset harmonic separation algorithm and a dq transformation algorithm, convert a harmonic component corresponding to the frequency of the target injection current in the three-phase currents into dq-axis currents in a rotating coordinate system with the same frequency as the target injection current, to obtain feedback dq-axis currents; Use a PI controller to process the amplitude difference between the target injection current and the feedback dq-axis currents, to obtain a dq-axis voltage to be injected; Using a preset inverse transformation algorithm, convert the dq-axis voltage to be injected into the radial force injection voltage vector.

3. The fault discrimination method of the rolling bearing according to claim 2, characterized in that, The reference voltage vector is generated in the following manner: According to the current position information and a reference angular velocity of the motor, use a PI controller to generate a reference q-axis current; According to the three-phase currents of the motor, use a synchronous rotating coordinate transformation to obtain feedback dq-axis currents; wherein, the feedback dq-axis currents include a feedback d-axis current and a feedback q-axis current; According to the target injection current, use a torque compensation transformation algorithm to generate a compensation q-axis current; Based on the reference q-axis current, the feedback q-axis current, and the compensation q-axis current, use a PI controller to generate a q-axis voltage command; Based on a d-axis current reference value of the motor and the feedback d-axis current, use a PI controller to generate a d-axis voltage command; Based on the d-axis voltage command and the q-axis voltage command, use a rotation transformation to generate the reference voltage vector.

4. The fault discrimination method of the rolling bearing according to claim 1, characterized in that, The extracting the time-varying spectrum of the radial stiffness of the rolling bearing from the radial vibration signal includes: Perform band-pass filtering on the radial vibration signal according to the frequency of the target radial force; Use a preset signal extraction algorithm to extract the signal envelope of the filtered radial vibration signal; Based on the signal envelope, as well as the target rotational speed of the motor and the amplitude of the radial force currently borne by the rolling bearing, a time-varying spectrum of the radial stiffness of the rolling bearing is obtained; wherein, the amplitude of the radial force currently borne by the rolling bearing is determined by collecting the three-phase current driving the motor.

5. The fault discrimination method of the rolling bearing according to claim 1, wherein The extracting the kurtosis of the time-varying characteristic frequency band of the radial stiffness from the time-varying spectrum of the radial stiffness includes: Using the following formula to extract the kurtosis of the time-varying characteristic frequency band of the radial stiffness: where E( ) is the expectation, and X is the amplitude of the time-varying characteristic frequency band of the radial stiffness.

6. The method for fault discrimination of a rolling bearing according to claim 1, characterized in that, The determining the fault condition of the rolling bearing according to the kurtosis and the characteristic frequency includes: Comparing the kurtosis with a preset kurtosis threshold; If the kurtosis is less than the kurtosis threshold, it is determined that the rolling bearing has a distributed fault; If the kurtosis is not less than the kurtosis threshold, it is determined whether the characteristic frequency is within the range of the single-point fault frequency; wherein, the single-point fault frequency reflects the change in the time-varying frequency of the radial stiffness of the bearing system caused by the defect area during the rotation of the rolling bearing; If the characteristic frequency is within the range of the single-point fault frequency, it is determined that the rolling bearing has a single-point fault.

7. A fault discrimination device for a rolling bearing, characterized in that, including: A generating module, configured to generate a target voltage vector according to a radial force injection voltage vector and a reference voltage vector in response to the motor to be detected reaching a stable state; wherein, the radial force injection voltage vector is generated by using a PI controller according to the target injection current of the motor; the amplitude and frequency of the target injection current are determined according to the frequency and duration of the target radial force to be applied to the rolling bearing of the motor; the reference voltage vector is a voltage command signal generated to drive the motor to rotate according to the target rotational speed; A driving module, configured to input the target voltage vector into a vector pulse width modulation module for driving the motor, so that the vector pulse width modulation module applies the target radial force to the rolling bearing of the motor while driving the motor to rotate according to the target rotational speed; A first extraction module, configured to collect a radial vibration signal at the bearing housing of the rolling bearing and extract the time-varying spectrum of the radial stiffness of the rolling bearing from the radial vibration signal; A second extraction module, configured to extract the kurtosis of the time-varying characteristic frequency band of the radial stiffness and the characteristic frequency corresponding to the peak value from the time-varying spectrum of the radial stiffness; wherein, the time-varying characteristic frequency band of the radial stiffness is the performance region in the frequency domain of the time-varying characteristics of the radial stiffness related to the bearing fault of the rolling bearing; A determining module, configured to determine the fault condition of the rolling bearing according to the kurtosis and the characteristic frequency.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the fault discrimination method of the rolling bearing according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the fault discrimination method of the rolling bearing according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the fault discrimination method of the rolling bearing according to any one of claims 1 to 6.

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