Industrial motor bearing fault monitoring method and device and medium

Abstract

The invention relates to an industrial motor bearing fault monitoring method and device and a medium, and belongs to the technical field of industrial motor fault monitoring. The method comprises the following steps: establishing a voltage equation, a flux linkage equation, a torque equation and a mechanical kinetic equation of the industrial motor; calculating an inductance matrix of the motor by adopting a winding function correction method; establishing a feature model of the industrial motor bearing fault; and obtaining a fault category according to the established feature model of the industrial motor bearing fault. According to the method and the device, the induction motor with the bearing fault can be dynamically simulated, the stator current spectrum can be calculated to identify the corresponding fault characteristics, and the severity of the bearing fault can be quantified by monitoring the amplitude of the corresponding fault current component.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial motor fault monitoring, and in particular relates to an industrial motor bearing fault monitoring method, device and medium. Background Art

[0002] Induction motors are widely used in many industrial applications and factories. In the era of the Internet of Things, condition monitoring and condition-based maintenance of machines are becoming increasingly important and desirable. Induction motors may experience various mechanical and electrical faults as they may operate under adverse conditions such as heat, humidity, and overload; therefore, it is valuable to be able to detect faults before the machine fails. Among all fault types, bearing faults account for about one-third of all faults in induction motors. Therefore, bearing fault analysis and detection has been an active research area, and sensing mechanisms using various signals such as vibration, noise, and stator current have been proposed. Bearing faults affect the mechanical response of the motor system and are directly reflected in vibration signals and high-frequency acoustic noise. By building a mechanical model, the fault characteristic frequencies in the vibration and acoustic signals can be identified, and the monitoring signals can be subjected to frequency analysis to identify the corresponding faults. However, their accuracy is often affected by the influence of background noise in factory settings, and the sensitivity depends largely on the sensor installation location.

[0003] Motor current signature analysis is an alternative method for fault detection, which collects and analyzes the stator current signal of the motor. It has the natural advantages of simple implementation and cost saving, because no additional sensors are required for fault detection. Bearing faults can cause rotor center offset and change the air gap profile of the motor, resulting in changes in air gap permeability and magnetic flux, which are ultimately reflected in the stator current signal. The stator current frequency components caused by different types of bearing faults can be identified by bearing geometry and motor speed information. However, bearing fault detection using motor current signature analysis methods is mostly qualitative, and the severity of the fault is difficult to quantify due to the complexity of the motor model. Summary of the invention

[0004] The present disclosure provides an industrial motor bearing fault monitoring method, device and medium to solve the above-mentioned problems.

[0005] According to a first aspect of the present disclosure, a method for quantifying industrial motor bearing faults based on an improved winding function method is provided, the method comprising: establishing a voltage equation, a flux equation, a torque equation and a mechanical dynamics equation of the industrial motor; calculating the inductance matrix of the motor using a modified winding function method; establishing a characteristic model of industrial motor bearing faults; and obtaining a fault category based on the established characteristic model of industrial motor bearing faults.

[0006] In some embodiments, the establishing of the voltage equation and flux equation of the industrial motor includes: for a three-phase squirrel cage induction motor with a rotor bar number R, the stator voltage and flux are described by equations, which are: , ,in, , represents the stator voltage, , represents the rotor current, , represents the stator flux, , represents a vector with a length of R+1, describing the rotor loop current and the end ring current respectively, represents a 3×3 stator resistance matrix, represents the 3×3 stator inductance matrix, represents the 3×(R+1) matrix containing the mutual inductance between the stator phase and the rotor loop; for the rotor side, the voltage equation and flux equation are , ,in, , Represents the rotor circuit Self-inductance matrix; for a squirrel cage rotor, the rotor voltage is zero: .

[0007] In some embodiments, the step of establishing the torque equation and the mechanical dynamics equation of the industrial motor includes: for a three-phase induction motor with a Y connection, the input signal is the line voltage, and the voltage equation is converted into the line voltage input, that is, , the torque equation of the induction motor is expressed as: ,in, represents the mechanical angle of the rotor; the mechanical dynamics of the motor are: , ,in, Indicates the mechanical speed, represents the load torque, Represents the rotor inertia.

[0008] In some embodiments, the calculation of the inductance matrix of the motor by using the modified winding function method includes: for winding i and winding j, the inductance is expressed as: ,in, represents the vacuum permeability, represents the air gap radius, represents the stack length, represents the winding turns function of winding i, Represents the modified winding The winding function is calculated as ,in, , .

[0009] In some embodiments, the establishment of a characteristic model of industrial motor bearing failure includes: a cage defect hitting the outer race: , where D is the bearing diameter measured between the raceway centers, d is the diameter of each ball in the bearing, Indicates the contact angle between the ball and the raceway; the cage notches the inner race: ; Outer ring defect hits the ball: ; Inner ring defect hits the ball: ; Ball defects are concentrated in two ring seats: .

[0010] In some embodiments, according to the formula , describes the motor's stator angle in the presence of a bearing fault. and the air gap profile at time t, where represents the nominal air gap length, represents the Carter coefficient, Indicates the level of eccentricity caused by bearing failure, Indicates the defect location.

[0011] According to the second aspect of the present disclosure, an industrial motor bearing fault monitoring device is also provided, including: a first establishment module, used to establish the voltage equation, magnetic flux equation, torque equation and mechanical dynamics equation of the industrial motor; a calculation module, used to calculate the inductance matrix of the motor using the modified winding function method; a second establishment module, used to establish a characteristic model of the industrial motor bearing fault; an acquisition module, used to obtain the fault category according to the established characteristic model of the industrial motor bearing fault.

[0012] According to a third aspect of the present disclosure, an industrial motor bearing fault monitoring device is also provided, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute the industrial motor bearing fault monitoring method as described above based on instructions stored in the memory.

[0013] According to a fourth aspect of the present disclosure, a computer storable medium is also provided, on which computer program instructions are stored, and when the instructions are executed by a processor, the industrial motor bearing fault monitoring method as described above is implemented.

[0014] The beneficial effects of the present disclosure are as follows: the induction motor with bearing fault can be dynamically simulated, and the stator current spectrum can be calculated to identify the corresponding fault characteristics. The method helps to quantify the severity of the bearing fault by monitoring the amplitude of the corresponding fault current component.

[0015] Other features and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0017] Figure 1 It is a flow chart of the industrial motor bearing fault monitoring method.

[0018] Figure 2 It is one of the schematic diagrams of industrial motor bearings.

[0019] Figure 3 This is the second schematic diagram of industrial motor bearings.

[0020] Figure 4 This is a schematic diagram of a bearing failure.

[0021] Figure 5 Schematic diagram of stator current waveform of an industrial motor with healthy / faulty bearings.

[0022] Figure 6 Schematic diagram of the stator current spectrum of an industrial motor with healthy / faulty bearings. DETAILED DESCRIPTION

[0023] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0024] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0025] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0026] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0027] like Figure 1 As shown, the industrial motor bearing fault quantification method based on the improved winding function method includes the following steps:

[0028] First, the voltage, flux, torque and mechanical dynamics equations of the industrial motor are established.

[0029] Then, the inductance matrix of the motor is calculated using the modified winding function method.

[0030] Furthermore, a characteristic model of industrial motor bearing faults is established.

[0031] Finally, the fault category is obtained based on the established characteristic model of industrial motor bearing fault.

[0032] Establishing the voltage and flux equations for industrial motors: For a three-phase squirrel cage induction motor with R rotor bars, the stator voltage and flux are described by the equations, …… (1), ... (2), where: is the stator voltage, is the rotor current, is the stator flux, is a vector with a length of R+1, describing the rotor loop current and the end ring current respectively. s is a 3×3 stator resistance matrix, is the 3×3 stator inductance matrix, is a 3×(R+1) matrix containing the mutual inductances between the stator phases and the rotor loop.

[0033] For the rotor side, the voltage equation and flux equation are …… (3), ……(4), where , is the (R+1)×(R+1) self-inductance matrix of the rotor circuit. Please note that for a squirrel cage rotor, the rotor voltage is zero: .

[0034] The torque and mechanical dynamics equations of the industrial motor are established: For a three-phase induction motor with Y connection, the input signal is the line voltage. The stator voltage equation (1) is converted to the line voltage input, that is, ……(5), the torque equation of the induction motor is expressed as ... (6), among which, is the mechanical angle of the rotor. The mechanical dynamics of the motor are …… (7), ……(8), among which, refers to the mechanical speed, is the load torque, is the rotor inertia.

[0035] It can be seen from the model that the inductance components in equations (2) and (4) and the inductance derivative in equation (6) are crucial.

[0036] The modified winding function method is used to calculate the inductance matrix of the motor: The modified winding function method is used to calculate the inductance matrix of the motor. The inductance between a pair of windings at each step can be calculated by integrating the product of the two winding functions and the air gap permeability function over all stator angles. For winding i and winding j, the inductance is expressed as, ... (9), among which, represents the vacuum permeability, represents the air gap radius, represents the stack length, Indicates winding The function of the number of winding turns is Represents the modified winding The winding function is calculated as ... (10), where …… (11), ... (12), For the dynamic simulation of an induction motor, the inductance is updated at each time step.

[0037] The inductance matrix of the motor is calculated using the modified winding function method: Figure 2 and Figure 3 As shown, for a typical ball bearing, it has an outer race mounted on the machine tool, an inner race that fixes the motor shaft, balls to reduce friction between rotating and stationary parts, and a cage that limits the relative position of the rolling elements; when defects occur in certain positions of the bearing, the rotation will no longer be smooth, and periodic vibration pulses will be generated when the motor rotates; according to the specific location of the fault, the corresponding characteristic frequency can be identified according to the geometry of the bearing and the speed of the motor .

[0038] Establishing the characteristic frequencies of industrial motor bearing faults: Common fault types and their corresponding characteristic frequencies are shown below, where D is the bearing diameter measured between the raceway centers; d is the diameter of each ball in the bearing; Describes the contact angle between the ball and the raceway.

[0039] Cage defect hitting the outer race: ; Cage defect hits the inner race: ; Outer ring defect hits the ball: ; Inner ring defect hits the ball: ; Ball defects are concentrated in two ring seats: When a point defect contacts another bearing element, it causes radial displacement or eccentricity of the rotor center; The air gap profile described by is changed due to the defect, so the magnetic permeability, which is inversely proportional to the inverse of the air gap, is also modified. The mutual inductance between the stator winding and the rotor loop is affected according to equation (9). In the dynamic model of the induction machine, due to the fault in the stator current spectrum There will be additional frequency components at , where is the fundamental frequency.

[0040] Establishing the air gap profile of industrial motor bearing faults: The periodic modification of the air gap length caused by the eccentricity due to the bearing fault is modeled by a series of Dirac delta functions commonly used in vibration analysis. In the presence of a bearing fault, the motor moves at the stator angle and time The air gap profile under is described by the formula: ……(13), among which, represents the nominal air gap length, represents the Carter coefficient, which is used to account for the increase in effective air gap length due to the slotting effect, Indicates the level of eccentricity caused by bearing failure, represents the defect location. In the actual implementation of this model, a rectangular pulse with finite width is used.

[0041] The two bearings of the induction motor used in the simulation analysis are installed on the load side and the opposite side respectively. Each bearing has 8 balls, and the characteristic frequency of the inner ring defect is about , and the characteristic frequency of the outer ring defect is .

[0042] Using the air gap profiles and the established model for each condition, the motor operation is dynamically simulated. An induction motor with a healthy bearing under no-load conditions and a faulty bearing with an inner race fault are simulated. For the fault case, the eccentricity level resulting from the gap variation is assumed to be ,like Figure 4 At a line voltage of 200V and The phase currents as a function of time are simulated and plotted at a line frequency of Figure 5 As shown, the time domain signal is dominated by the fundamental component, but there are small features in each curve and there appear to be more components in the fault case.

[0043] Apply Fourier transform to obtain the frequency spectrum of these two working conditions, such as Figure 6 As shown, since the characteristic frequency is The inner race fault is observed in the current spectrum. and In the case of a healthy bearing, these features are missing.

[0044] The present disclosure also provides an industrial motor bearing fault monitoring device, including: a first establishment module, used to establish the voltage equation, magnetic flux equation, torque equation and mechanical dynamics equation of the industrial motor; a calculation module, used to calculate the inductance matrix of the motor using a modified winding function method; a second establishment module, used to establish a characteristic model of the industrial motor bearing fault; an acquisition module, used to obtain the fault category according to the established characteristic model of the industrial motor bearing fault.

[0045] The present disclosure also provides an industrial motor bearing fault monitoring device, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute the industrial motor bearing fault monitoring method as described above based on instructions stored in the memory.

[0046] The present disclosure also provides a computer storable medium having computer program instructions stored thereon, which implement the above-mentioned industrial motor bearing fault monitoring method when the instructions are executed by a processor.

[0047] Although some specific embodiments of the present disclosure have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for monitoring bearing faults of industrial motors, characterized in that: The method comprises: Establish voltage equations, flux equations, torque equations and mechanical dynamics equations for industrial motors; The inductance matrix of the motor is calculated using the modified winding function method; Establish characteristic models of industrial motor bearing failures; The fault category is obtained based on the established characteristic model of industrial motor bearing fault.

2. The industrial motor bearing fault monitoring method according to claim 1, characterized in that: The voltage equation and flux equation of the industrial motor are established, including: For a three-phase squirrel cage induction motor with R rotor bars, the stator voltage and flux are described by the equations: , ,in, , represents the stator voltage, , represents the rotor current, , represents the stator flux, , represents a vector with a length of R+1, describing the rotor loop current and the end ring current respectively, represents a 3×3 stator resistance matrix, represents the 3×3 stator inductance matrix, represents the 3×(R+1) matrix containing the mutual inductance between the stator phase and the rotor loop; For the rotor side, the voltage equation and flux equation are , ,in, , Represents the rotor circuit Self-inductance matrix; for a squirrel cage rotor, the rotor voltage is zero: .

3. The industrial motor bearing fault monitoring method according to claim 2 is characterized in that: The torque equation and mechanical dynamics equation of the industrial motor are established, including: For a three-phase induction motor with Y connection, the input signal is the line voltage, and the voltage equation is converted to the line voltage input, that is , the torque equation of the induction motor is expressed as: ,in, represents the mechanical angle of the rotor; The mechanical dynamics of the motor are: , ,in, Indicates the mechanical speed, represents the load torque, Represents the rotor inertia.

4. The industrial motor bearing fault monitoring method according to claim 3 is characterized in that: The method of calculating the inductance matrix of the motor by using the modified winding function method includes: For winding i and winding j, the inductance is expressed as: ,in, represents the vacuum permeability, represents the air gap radius, represents the stack length, represents the winding turns function of winding i, Represents the modified winding The winding function is calculated as ,in, , .

5. The industrial motor bearing fault monitoring method according to claim 4, characterized in that: The method of establishing a characteristic model of industrial motor bearing failure includes: Cage defect hitting the outer race: , where D is the bearing diameter measured between the raceway centers, d is the diameter of each ball in the bearing, Indicates the contact angle between the ball and the raceway; The retainer is notched and hits the inner race: ; Outer ring defect hits the ball: ; Inner ring defect hits the ball: ; Ball defects are concentrated in two ring seats: .

6. The industrial motor bearing fault monitoring method according to claim 5, characterized in that: According to the formula , describes the motor's stator angle in the presence of a bearing fault. and the air gap profile at time t, where represents the nominal air gap length, represents the Carter coefficient, Indicates the level of eccentricity caused by bearing failure, Indicates the defect location.

7. An industrial motor bearing fault monitoring device, characterized in that: include: The first building module is used to build the voltage equation, flux equation, torque equation and mechanical dynamics equation of the industrial motor; A calculation module, used for calculating the inductance matrix of the motor by using a modified winding function method; The second module is to establish the characteristic model of industrial motor bearing failure; The acquisition module is used to acquire the fault category according to the established characteristic model of industrial motor bearing fault.

8. An industrial motor bearing fault monitoring device, characterized in that: include: Memory; as well as A processor coupled to the memory, wherein the processor is configured to execute the industrial motor bearing fault monitoring method according to any one of claims 1 to 6 based on instructions stored in the memory.

9. A computer storable medium, characterized in that: Computer program instructions are stored thereon, and when the instructions are executed by a processor, the industrial motor bearing fault monitoring method according to any one of claims 1 to 6 is implemented.

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