Fault diagnosis method and system for dual three-phase permanent magnet synchronous motor

Abstract

The invention relates to the technical field of motor detection, and discloses a fault diagnosis method and system for a dual three-phase permanent magnet synchronous motor, and the system comprises the steps: arranging vibration sensors at a bearing pedestal and a motor housing of a to-be-detected motor, collecting the size information of the to-be-detected motor, and determining the number of the vibration sensors; the to-be-detected motor is operated at the highest rotating speed, and the sampling frequency is determined to collect vibration data of all the vibration sensors; fourier transform is carried out on the vibration data to obtain a vibration amplitude, and the vibration amplitude is analyzed to determine a fault type; obtaining a detection evaluation value of the to-be-detected motor according to the rotor imbalance score and the bearing wear score; historical maintenance information of the to-be-detected motor is collected to determine a final evaluation value, and risk level early warning is carried out according to the final evaluation value. The timeliness, accuracy and reliability of motor mechanical fault diagnosis are improved, the service life of the motor is prolonged, and the fault risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of motor detection, and in particular to a fault diagnosis method and system for a dual three-phase permanent magnet synchronous motor. Background Art

[0002] In motor drive systems, dual three-phase permanent magnet synchronous motors (PMSMs) have been widely used in electric vehicles, industrial automation, wind power generation and other fields due to their high efficiency, high power density and low noise. However, as the requirements for motor stability and reliability in application scenarios continue to increase, mechanical failures that may occur in long-term operation of motors, especially problems such as rotor imbalance and bearing wear, have become key factors affecting their performance and service life. If these mechanical failures cannot be detected and handled in a timely and effective manner, they may cause the performance of the motor to deteriorate, and in severe cases may even cause downtime or equipment damage.

[0003] However, traditional fault diagnosis methods mainly rely on changes in external motor parameters (such as current, voltage, etc.) to infer internal mechanical faults. However, such methods have low sensitivity when detecting mechanical faults (such as bearing wear and rotor imbalance), and it is difficult to accurately locate the type and extent of the fault.

[0004] Therefore, it is necessary to design a fault diagnosis method and system for a dual three-phase permanent magnet synchronous motor to solve the problems existing in the current technology. Summary of the invention

[0005] In view of this, the present invention proposes a fault diagnosis method and system for a dual three-phase permanent magnet synchronous motor, aiming to solve the problem that mechanical fault detection of the current dual three-phase permanent magnet synchronous motor during operation relies on external parameters, resulting in low detection accuracy and inability to distinguish fault types.

[0006] In one aspect, the present invention provides a fault diagnosis method for a dual three-phase permanent magnet synchronous motor, comprising:

[0007] Vibration sensors are arranged at the bearing seat and the motor housing of the motor to be tested, the size information of the motor to be tested is collected, and the number of the vibration sensors is determined according to the size information;

[0008] Running the motor to be detected at a maximum speed, determining a sampling frequency to collect vibration data of all the vibration sensors, wherein the sampling frequency is ten times higher than the maximum frequency of the motor to be detected;

[0009] Performing Fourier transformation on the vibration data to obtain a vibration amplitude, and analyzing the vibration amplitude to determine a fault type, wherein the fault type includes rotor imbalance and bearing wear;

[0010] Obtaining a detection evaluation value of the motor to be detected according to the rotor imbalance score and the bearing wear score;

[0011] Collect historical maintenance information of the motor to be detected, and determine whether to adjust the detection evaluation value based on the historical maintenance information, determine a final evaluation value, and issue a risk level warning based on the final evaluation value, wherein the risk level is proportional to the final evaluation value.

[0012] Further, when the number of the vibration sensors is determined according to the size information, the number of the vibration sensors is calculated by the following formula:

[0013]

[0014] Wherein, Ns represents the number of vibration sensors, L represents the length of the motor, D represents the diameter of the motor, a represents the detection radius of the vibration sensor, and b represents the vibration sensitivity coefficient.

[0015] Further, when the vibration data is subjected to Fourier transformation to obtain the vibration amplitude, it includes:

[0016]

[0017] Wherein, X(k) represents the frequency domain signal sequence, x(n) represents the time domain signal sequence, N represents the number of sampling points of the signal, j represents the imaginary unit, k represents the frequency index, n represents the time domain index, k=0, 1, 2, ..., N-1;

[0018] The vibration amplitude is |X(k)|.

[0019] Further, analyzing the vibration amplitude to determine the fault type includes:

[0020] analyzing the frequency of the vibration amplitude;

[0021] When the vibration amplitude of the 1X component in the frequency spectrum is greater than the amplitude threshold, it is determined that the rotor imbalance fault exists;

[0022] When the amplitude of the outer ring pass frequency, the amplitude of the inner ring pass frequency, the amplitude of the rolling element fault frequency or the amplitude of the cage pass frequency in the frequency spectrum is greater than the amplitude threshold, it is determined that a bearing wear fault exists.

[0023] Further, when it is determined that the rotor imbalance fault exists, the method includes:

[0024] The rotor imbalance score is determined according to the vibration amplitude at the 1X frequency and the amplitude threshold, and the rotor imbalance score is calculated by the following formula:

[0025]

[0026] Where Gz is the rotor unbalance score, Zp is the vibration amplitude at 1X frequency, and Zmax is the amplitude threshold.

[0027] Furthermore, the outer ring passing frequency, inner ring passing frequency, rolling element failure frequency and cage passing frequency are calculated by the following formulas:

[0028]

[0029] Among them, fw is the outer ring passing frequency, fn is the inner ring passing frequency, fg is the rolling element failure frequency, fb is the cage passing frequency, ng is the number of rolling elements, d is the diameter of the rolling element, Dz is the bearing pitch circle diameter, and α is the contact angle between the rolling element and the bearing track.

[0030] Further, when it is determined that there is a bearing wear fault, the bearing wear score is calculated by the following formula:

[0031]

[0032] Among them, Gc is the bearing wear score, Zw is the amplitude of the outer ring passing frequency, Zn is the amplitude of the inner ring passing frequency, Zg is the amplitude of the rolling element failure frequency, and Zb is the cage passing frequency.

[0033] Further, when obtaining the detection evaluation value of the motor to be detected according to the rotor imbalance score and the bearing wear score, it includes:

[0034] J = Gz × t1 + Gc × t2;

[0035] Among them, J represents the detection evaluation value, Gz represents the rotor imbalance score, Gc represents the bearing wear score, and t1 and t2 represent weight coefficients respectively.

[0036] Further, judging whether to adjust the detection evaluation value according to the historical maintenance information and determining the final evaluation value includes:

[0037] Obtaining a historical maintenance number according to the historical maintenance information, and determining whether to adjust the detection evaluation value according to the historical maintenance number;

[0038] When the number of historical maintenance times is 0, it is determined that the detection evaluation value is not to be adjusted, and the detection evaluation value is used as the final evaluation value;

[0039] When the number of historical maintenance times is not 0, it is determined that the detection evaluation value is adjusted, and an adjustment coefficient is determined according to the number of historical maintenance times to adjust the detection evaluation value. The adjustment coefficient is proportional to the number of historical maintenance times, and the adjustment coefficient is greater than 1.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows: by rationally arranging vibration sensors at the bearing seat and housing of the motor, the number of sensors is dynamically determined based on the motor size information to ensure comprehensive acquisition of fault signals. Combined with a sampling frequency that is ten times higher than the highest frequency of the motor, the complete capture of high-frequency components in the vibration signal is ensured, effectively improving the detection accuracy. By analyzing the vibration amplitude through Fourier transform, it is possible to distinguish between two mechanical faults, rotor imbalance and bearing wear, and to quantitatively evaluate the faults based on the scoring system. The evaluation value is adjusted in combination with historical maintenance information to ensure the accuracy and reliability of the diagnostic results. Finally, real-time risk warnings are carried out based on the evaluation values, which improves the timeliness, accuracy and reliability of motor mechanical fault diagnosis, and is conducive to extending the service life of the motor and reducing the risk of failure.

[0041] On the other hand, the present application also provides a dual three-phase permanent magnet synchronous motor fault diagnosis system, which is used to apply the dual three-phase permanent magnet synchronous motor fault diagnosis method, including:

[0042] A vibration sensor is arranged at the bearing seat and the motor housing of the motor to be tested;

[0043] Detection module, including collection unit, analysis unit, processing unit and early warning unit;

[0044] The acquisition unit is configured to operate the motor to be detected at a maximum speed, determine a sampling frequency to acquire vibration data of all the vibration sensors, and the sampling frequency is higher than ten times the maximum frequency of the motor to be detected;

[0045] The analysis unit is configured to perform Fourier transformation on the vibration data to obtain a vibration amplitude, and analyze the vibration amplitude to determine a fault type, wherein the fault type includes rotor imbalance and bearing wear;

[0046] The processing unit is configured to obtain a detection evaluation value of the motor to be detected according to the rotor imbalance score and the bearing wear score;

[0047] The early warning unit is configured to collect historical maintenance information of the motor to be detected, and determine whether to adjust the detection evaluation value based on the historical maintenance information, determine a final evaluation value, and issue a risk level early warning based on the final evaluation value, wherein the risk level is proportional to the final evaluation value.

[0048] It is understandable that the above-mentioned dual three-phase permanent magnet synchronous motor fault diagnosis method and system have the same beneficial effects, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0050] Figure 1 A flow chart of a fault diagnosis method for a dual three-phase permanent magnet synchronous motor provided by an embodiment of the present invention;

[0051] Figure 2 A structural block diagram of a fault diagnosis system for a dual three-phase permanent magnet synchronous motor provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0053] In some embodiments of the present application, see Figure 1 As shown, a fault diagnosis method for a dual three-phase permanent magnet synchronous motor includes:

[0054] S100: Vibration sensors are arranged at the bearing seat and the motor housing of the motor to be tested, size information of the motor to be tested is collected, and the number of vibration sensors is determined according to the size information.

[0055] S200: operating the motor to be detected at a maximum speed, determining a sampling frequency to collect vibration data of all vibration sensors, and the sampling frequency is higher than ten times the maximum frequency of the motor to be detected.

[0056] S300: Perform Fourier transform on the vibration data to obtain a vibration amplitude, and analyze the vibration amplitude to determine a fault type, where the fault type includes rotor imbalance and bearing wear.

[0057] S400: Obtaining a detection evaluation value of the motor to be detected according to the rotor imbalance score and the bearing wear score.

[0058] S500: Collect historical maintenance information of the motor to be tested, and determine whether to adjust the test evaluation value based on the historical maintenance information, determine the final evaluation value, and issue a risk level warning based on the final evaluation value. The risk level is proportional to the final evaluation value.

[0059] Specifically, in order to directly obtain mechanical vibration signals and facilitate accurate diagnosis of mechanical faults in S100, vibration sensors are set at the bearing seat and motor housing of the dual three-phase permanent magnet synchronous motor to be tested. After collecting the size information of the motor (such as the size of the motor, the diameter of the bearing seat, etc.), the number of sensors is reasonably determined according to the size. Reasonable planning of the number and layout of sensors helps to ensure the comprehensiveness and accuracy of data collection and avoid missing key vibration information. In S200, when the motor runs at the highest speed, various mechanical faults (such as rotor imbalance and bearing wear) are more likely to show obvious vibration characteristics. By setting the sampling frequency to ten times the maximum operating frequency of the motor, it is ensured that all high-frequency component vibration data can be captured even when the motor runs at high speed. The high sampling rate avoids signal distortion or loss of high-frequency information caused by insufficient sampling. The vibration data collected in S300 is usually a time domain signal, which is converted into a frequency domain signal through Fourier transform to obtain the vibration amplitude of different frequency components. Different fault types are distinguished by analyzing the amplitude characteristics in the frequency domain. Specifically, rotor imbalance causes low-frequency large-amplitude vibration, while bearing wear causes high-frequency component vibration. Through spectrum analysis, the vibration characteristics of rotor imbalance and bearing wear can be accurately identified. In S400, rotor imbalance and bearing wear are scored according to the fault characteristics, and these scores reflect the severity of various faults. By combining the scores of the two, the overall detection evaluation value of the motor to be tested is obtained, and the evaluation value is used to reflect the health status of the motor. Higher scores indicate more serious mechanical faults. In S500, the historical maintenance records of the motor (such as previous fault repairs, component replacements, etc.) are used as an important reference for evaluating the current fault. If the motor has been maintained, it will affect the accuracy of the fault evaluation value. Therefore, the evaluation value is adjusted as necessary based on historical information to ensure that the final evaluation value accurately reflects the actual status of the current motor. The final evaluation value is used to generate a risk level warning. The risk level is proportional to the evaluation value, which helps users to monitor and prevent the potential failure risks of the motor in real time.

[0060] It is understandable that by properly arranging vibration sensors on the motor bearing seat and housing, comprehensive acquisition of fault signals is ensured. Combined with high sampling rate and Fourier transform, it can accurately distinguish mechanical faults such as rotor imbalance and bearing wear, and quantitatively evaluate the degree of fault through the scoring system, adjust the evaluation value in combination with historical maintenance information, and generate real-time risk warning. It improves the accuracy and reliability of fault diagnosis, and also makes risk warning more timely and accurate, which helps users take effective preventive measures in the early stage of faults, avoid more serious equipment damage or downtime, extend the service life of the motor and reduce operating costs.

[0061] In some embodiments of the present application, when the number of vibration sensors is determined according to the size information, the number of vibration sensors is calculated by the following formula:

[0062]

[0063] Wherein, Ns represents the number of vibration sensors, L represents the length of the motor, D represents the diameter of the motor, a represents the detection radius of the vibration sensor, and b represents the vibration sensitivity coefficient.

[0064] It is understandable that the vibration sensitivity coefficient reflects the influence of the motor mass on the vibration attenuation. Motors with larger mass will cause the vibration amplitude to decay faster, and the number of sensors needs to be increased, and b is preferably 1-2. When the calculation result is not an integer, an upward value is used. Through a quantitative calculation formula, the geometric dimensions of the motor are accurately linked to the number of vibration sensors, avoiding the situation where too many or too few sensors are arranged. A reasonable number of sensors saves costs and ensures comprehensive monitoring of motor vibration. At the same time, taking into account the monitoring radius and sensitivity coefficient of the sensor, the monitoring effect is further optimized, and the accuracy and reliability of fault detection are improved. It is conducive to improving the accuracy and efficiency of motor fault diagnosis.

[0065] In some embodiments of the present application, when performing Fourier transform on vibration data to obtain vibration amplitude, it includes:

[0066]

[0067] Among them, X(k) represents the frequency domain signal sequence, x(n) represents the time domain signal sequence, N represents the number of sampling points of the signal, j represents the imaginary unit, k represents the frequency index, n represents the time domain index, k=0, 1, 2,..., N-1.

[0068] The vibration amplitude is |X(k)|.

[0069] It can be understood that the complex time domain vibration signal is converted into a frequency domain signal through Fourier transform, so that the vibration amplitude of different frequency components can be clearly analyzed. By extracting the vibration amplitude, the fault type (such as rotor imbalance and bearing wear) can be identified more accurately, and the nature of the fault can be distinguished according to different frequency characteristics. The accuracy of fault analysis is improved, and complex vibration data can be processed, which improves the effect and efficiency of motor fault detection.

[0070] In some embodiments of the present application, analyzing the vibration amplitude to determine the fault type includes: analyzing the frequency of the vibration amplitude.

[0071] Specifically, when the vibration amplitude of the 1X component in the spectrum is greater than the amplitude threshold, it is determined that a rotor imbalance fault exists. When the amplitude of the outer ring pass frequency, the amplitude of the inner ring pass frequency, the amplitude of the rolling element fault frequency, or the amplitude of the cage pass frequency in the spectrum is greater than the amplitude threshold, it is determined that a bearing wear fault exists.

[0072] In some embodiments of the present application, when it is determined that a rotor imbalance fault exists, the process includes:

[0073] The rotor unbalance score is determined based on the vibration amplitude at the 1X frequency and the amplitude threshold. The rotor unbalance score is calculated using the following formula:

[0074]

[0075] Where Gz is the rotor unbalance score, Zp is the vibration amplitude at 1X frequency, and Zmax is the amplitude threshold.

[0076] It is understandable that by introducing the quantitative index of rotor imbalance scoring, a standard is provided to evaluate the degree of rotor imbalance. The establishment of the scoring system enables users to understand the operating status of the motor more intuitively. Compared with simple fault judgment, this scoring method makes fault diagnosis more flexible and helps to identify potential safety hazards in a timely manner, thereby improving the operating safety and reliability of the motor system and reducing the risk of failure and maintenance costs caused by rotor imbalance.

[0077] In some embodiments of the present application, the outer ring passing frequency, the inner ring passing frequency, the rolling element failure frequency, and the cage passing frequency are calculated by the following formulas:

[0078]

[0079] Among them, fw is the outer ring passing frequency, fn is the inner ring passing frequency, fg is the rolling element failure frequency, fb is the cage passing frequency, ng is the number of rolling elements, d is the diameter of the rolling element, Dz is the bearing pitch circle diameter, and α is the contact angle between the rolling element and the bearing track.

[0080] In some embodiments of the present application, when it is determined that a bearing wear fault exists, the bearing wear score is calculated by the following formula:

[0081]

[0082] Among them, Gc is the bearing wear score, Zw is the amplitude of the outer ring passing frequency, Zn is the amplitude of the inner ring passing frequency, Zg is the amplitude of the rolling element failure frequency, and Zb is the cage passing frequency.

[0083] In some embodiments of the present application, obtaining the detection evaluation value of the motor to be detected according to the rotor imbalance score and the bearing wear score includes:

[0084] J = Gz × t1 + Gc × t2;

[0085] Among them, J represents the detection evaluation value, Gz represents the rotor imbalance score, Gc represents the bearing wear score, and t1 and t2 represent weight coefficients respectively.

[0086] It is understandable that the determination of characteristic frequencies and the calculation of detection scores provide a specific quantitative basis for fault diagnosis. Systematizing the fault assessment of rotor imbalance and bearing wear not only makes motor fault diagnosis more accurate, but also intuitively shows the severity of the fault in the form of scores. The introduction of comprehensive evaluation values ​​not only helps to quickly identify potential fault risks, but also provides data support for maintenance and repair decisions, which is conducive to improving the safety and reliability of motors.

[0087] In some embodiments of the present application, judging whether to adjust the detection evaluation value based on historical maintenance information and determining the final evaluation value includes: obtaining the number of historical maintenance times based on the historical maintenance information, and judging whether to adjust the detection evaluation value based on the historical maintenance times.

[0088] Specifically, when the number of historical maintenance times is 0, it is determined that the detection evaluation value is not adjusted, and the detection evaluation value is used as the final evaluation value. When the number of historical maintenance times is not 0, it is determined that the detection evaluation value is adjusted, and the adjustment coefficient is determined according to the number of historical maintenance times to adjust the detection evaluation value. The adjustment coefficient is proportional to the number of historical maintenance times, and the adjustment coefficient is greater than 1.

[0089] It is understandable that by introducing historical maintenance information, dynamic adjustment of the motor detection evaluation value is achieved, making the fault diagnosis process more accurate and close to reality. Not only the current operating status of the motor is taken into account, but also the maintenance history of the motor is comprehensively reflected, which improves the reliability and accuracy of the evaluation value. Through this systematic adjustment mechanism, potential fault risks can be identified and responded to more promptly, thereby optimizing maintenance strategies, reducing equipment failure rates, and helping to improve the overall operating safety and economy of the motor.

[0090] In the above embodiment, vibration sensors are reasonably arranged at the bearing seat and housing of the motor, and the number of sensors is dynamically determined according to the motor size information to ensure the comprehensive collection of fault signals. Combined with a sampling frequency that is ten times higher than the highest frequency of the motor, the complete capture of high-frequency components in the vibration signal is ensured, effectively improving the detection accuracy. By analyzing the vibration amplitude through Fourier transform, it is possible to distinguish between two mechanical faults, rotor imbalance and bearing wear, and quantitatively evaluate the faults based on the scoring system. The evaluation value is adjusted in combination with historical maintenance information to ensure the accuracy and reliability of the diagnostic results. Finally, real-time risk warnings are carried out based on the evaluation values, which improves the timeliness, accuracy and reliability of the diagnosis of motor mechanical faults, and is conducive to extending the service life of the motor and reducing the risk of failure.

[0091] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a dual three-phase permanent magnet synchronous motor fault diagnosis system, which is used to apply the dual three-phase permanent magnet synchronous motor fault diagnosis method, including:

[0092] A vibration sensor is arranged at the bearing seat and the motor housing of the motor to be tested;

[0093] Detection module, including collection unit, analysis unit, processing unit and early warning unit;

[0094] The collection unit is configured to run the motor to be detected at a maximum speed, determine a sampling frequency to collect vibration data of all vibration sensors, and the sampling frequency is higher than ten times the maximum frequency of the motor to be detected;

[0095] The analysis unit is configured to perform Fourier transform on the vibration data to obtain a vibration amplitude, and analyze the vibration amplitude to determine a fault type, wherein the fault type includes rotor imbalance and bearing wear;

[0096] The processing unit is configured to obtain a detection evaluation value of the motor to be detected according to the rotor imbalance score and the bearing wear score;

[0097] The early warning unit is configured to collect historical maintenance information of the motor to be tested, and determine whether to adjust the detection evaluation value based on the historical maintenance information, determine the final evaluation value, and issue a risk level early warning based on the final evaluation value. The risk level is proportional to the final evaluation value.

[0098] It is understandable that by rationally arranging vibration sensors at the bearing seat and housing of the motor, the number of sensors can be dynamically determined based on the motor size information to ensure comprehensive acquisition of fault signals. Combined with a sampling frequency that is ten times higher than the highest frequency of the motor, the high-frequency components of the vibration signal are fully captured, effectively improving the detection accuracy. By analyzing the vibration amplitude through Fourier transform, it is possible to distinguish between two mechanical faults: rotor imbalance and bearing wear, and to quantitatively evaluate the faults based on the scoring system. The evaluation value is adjusted in combination with historical maintenance information to ensure the accuracy and reliability of the diagnostic results. Finally, real-time risk warnings are carried out based on the evaluation values, which improves the timeliness, accuracy and reliability of motor mechanical fault diagnosis, and is conducive to extending the service life of the motor and reducing the risk of failure.

[0099] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0100] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0101] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0103] 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A fault diagnosis method for a dual three-phase permanent magnet synchronous motor, characterized in that: include: Vibration sensors are arranged at the bearing seat and the motor housing of the motor to be tested, the size information of the motor to be tested is collected, and the number of the vibration sensors is determined according to the size information; Running the motor to be detected at a maximum speed, determining a sampling frequency to collect vibration data of all the vibration sensors, wherein the sampling frequency is ten times higher than the maximum frequency of the motor to be detected; Performing Fourier transformation on the vibration data to obtain a vibration amplitude, and analyzing the vibration amplitude to determine a fault type, wherein the fault type includes rotor imbalance and bearing wear; Obtaining a detection evaluation value of the motor to be detected according to the rotor imbalance score and the bearing wear score; Collect historical maintenance information of the motor to be detected, and determine whether to adjust the detection evaluation value based on the historical maintenance information, determine a final evaluation value, and issue a risk level warning based on the final evaluation value, wherein the risk level is proportional to the final evaluation value.

2. The fault diagnosis method for a dual three-phase permanent magnet synchronous motor according to claim 1, characterized in that: When the number of the vibration sensors is determined according to the size information, the number of the vibration sensors is calculated by the following formula: Wherein, Ns represents the number of vibration sensors, L represents the length of the motor, D represents the diameter of the motor, a represents the detection radius of the vibration sensor, and b represents the vibration sensitivity coefficient.

3. The fault diagnosis method for a dual three-phase permanent magnet synchronous motor according to claim 1, characterized in that: When the vibration data is subjected to Fourier transformation to obtain the vibration amplitude, it includes: Wherein, X(k) represents the frequency domain signal sequence, x(n) represents the time domain signal sequence, N represents the number of sampling points of the signal, j represents the imaginary unit, k represents the frequency index, n represents the time domain index, k=0, 1, 2, ..., N-1; The vibration amplitude is |X(k)|.

4. The fault diagnosis method for a dual three-phase permanent magnet synchronous motor according to claim 1, characterized in that: When analyzing the vibration amplitude to determine the fault type, it includes: analyzing the frequency of the vibration amplitude; When the vibration amplitude of the 1X component in the frequency spectrum is greater than the amplitude threshold, it is determined that the rotor imbalance fault exists; When the amplitude of the outer ring pass frequency, the amplitude of the inner ring pass frequency, the amplitude of the rolling element fault frequency or the amplitude of the cage pass frequency in the frequency spectrum is greater than the amplitude threshold, it is determined that a bearing wear fault exists.

5. The fault diagnosis method for a dual three-phase permanent magnet synchronous motor according to claim 4, characterized in that: When it is determined that the rotor imbalance fault exists, the method includes: The rotor imbalance score is determined according to the vibration amplitude at the 1X frequency and the amplitude threshold, and the rotor imbalance score is calculated by the following formula: Where Gz is the rotor unbalance score, Zp is the vibration amplitude at 1X frequency, and Zmax is the amplitude threshold.

6. The fault diagnosis method for a dual three-phase permanent magnet synchronous motor according to claim 5, characterized in that: The outer ring passing frequency, inner ring passing frequency, rolling element failure frequency and cage passing frequency are calculated by the following formulas: Among them, fw is the outer ring passing frequency, fn is the inner ring passing frequency, fg is the rolling element failure frequency, fb is the cage passing frequency, ng is the number of rolling elements, d is the diameter of the rolling element, Dz is the bearing pitch circle diameter, and α is the contact angle between the rolling element and the bearing track.

7. The fault diagnosis method for a dual three-phase permanent magnet synchronous motor according to claim 6, characterized in that: When it is determined that there is a bearing wear fault, the bearing wear score is calculated by the following formula: Among them, Gc is the bearing wear score, Zw is the amplitude of the outer ring passing frequency, Zn is the amplitude of the inner ring passing frequency, Zg is the amplitude of the rolling element failure frequency, and Zb is the cage passing frequency.

8. The fault diagnosis method for a dual three-phase permanent magnet synchronous motor according to claim 7, characterized in that: When obtaining the detection evaluation value of the motor to be detected according to the rotor imbalance score and the bearing wear score, it includes: J = Gz × t1 + Gc × t2; Among them, J represents the detection evaluation value, Gz represents the rotor imbalance score, Gc represents the bearing wear score, and t1 and t2 represent weight coefficients respectively.

9. The fault diagnosis method for a dual three-phase permanent magnet synchronous motor according to claim 8, characterized in that: Determining whether to adjust the detection evaluation value according to the historical maintenance information and determining the final evaluation value includes: Obtaining a historical maintenance number according to the historical maintenance information, and determining whether to adjust the detection evaluation value according to the historical maintenance number; When the number of historical maintenance times is 0, it is determined that the detection evaluation value is not to be adjusted, and the detection evaluation value is used as the final evaluation value; When the number of historical maintenance times is not 0, it is determined that the detection evaluation value is adjusted, and an adjustment coefficient is determined according to the number of historical maintenance times to adjust the detection evaluation value. The adjustment coefficient is proportional to the number of historical maintenance times, and the adjustment coefficient is greater than 1.

10. A fault diagnosis system for a dual three-phase permanent magnet synchronous motor, used for applying the fault diagnosis method for a dual three-phase permanent magnet synchronous motor as claimed in any one of claims 1 to 9, characterized in that: include: A vibration sensor is arranged at the bearing seat and the motor housing of the motor to be tested; Detection module, including collection unit, analysis unit, processing unit and early warning unit; The acquisition unit is configured to operate the motor to be detected at a maximum speed, determine a sampling frequency to acquire vibration data of all the vibration sensors, and the sampling frequency is higher than ten times the maximum frequency of the motor to be detected; The analysis unit is configured to perform Fourier transformation on the vibration data to obtain a vibration amplitude, and analyze the vibration amplitude to determine a fault type, wherein the fault type includes rotor imbalance and bearing wear; The processing unit is configured to obtain a detection evaluation value of the motor to be detected according to the rotor imbalance score and the bearing wear score; The early warning unit is configured to collect historical maintenance information of the motor to be detected, and determine whether to adjust the detection evaluation value based on the historical maintenance information, determine a final evaluation value, and issue a risk level early warning based on the final evaluation value, wherein the risk level is proportional to the final evaluation value.

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