A method and system for diagnosing rotor cage faults of an asynchronous motor by fusing electrical mechanism models, and a medium

Abstract

The application relates to an asynchronous motor rotor cage fault diagnosis method and system based on a fusion electrical mechanism model, and a medium, the method comprising: collecting three-phase stator voltage, three-phase stator current and running speed of an asynchronous motor to be monitored; preprocessing the three-phase stator voltage, three-phase stator current and running speed; and establishing a vibration fault characteristic frequency set; using a preset strategy to calculate vibration fault characteristic prediction values corresponding to all frequency points in the vibration fault characteristic frequency set, wherein the vibration fault characteristic prediction values comprise vibration displacement prediction values of a measuring point at each frequency point in the characteristic frequency set, and a vibration base value generated by motor main magnetic flux; calculating a predicted waveform coefficient of the cage fault according to the vibration displacement prediction values and the vibration base value; constructing a fault characteristic vector; comparing the fault characteristic vector with a preset fault region; and determining whether the motor has a fault according to a comparison result. The application improves the accuracy and robustness of motor fault diagnosis.

Description

Technical Field

[0001] This invention relates to the field of motor condition monitoring and fault diagnosis technology, and in particular to a method, system and medium for fault diagnosis of asynchronous motor rotor squirrel cage that integrates electrical mechanism models. Background Technology

[0002] As one of the most commonly used and critical power devices in industrial systems, asynchronous motors are widely used in power, chemical, and manufacturing industries. The stability and reliability of their operation directly affect the safe operation and economic benefits of production systems. With the continuous improvement of industrial scale, continuity, and automation, motors operate under variable loads, variable operating conditions, and complex power grid environments for extended periods. Internal faults, especially rotor faults, are characterized by strong concealment, weak early signs, and slow development. If they are not identified in time, they can easily trigger cascading failures or even equipment shutdown accidents.

[0003] Currently, the diagnosis of motor rotor faults mainly employs vibration-based diagnostic methods. This typically involves placing diagnostic sensors at the motor bearing housing or casing to analyze the time-domain, frequency-domain, or envelope characteristics of the vibration signal, thereby identifying rotor-related faults. This type of method is relatively sensitive to changes in the mechanical structure and has certain advantages in distinguishing fault types. However, in practical applications, vibration signals are highly susceptible to influences such as installation location, structural transmission paths, load variations, and external environmental noise. Especially under complex operating conditions, it is often difficult to distinguish whether changes in vibration amplitude are caused by a fault or by operational disturbances, thus affecting the reliability of the diagnostic results.

[0004] Furthermore, while some existing studies attempt to analyze the mapping relationship between signals and fault characteristics from the perspective of constructing motor models, most of these efforts remain at the level of theoretical modeling or offline data simulation, making it difficult to effectively integrate with online monitoring systems. Moreover, these models typically do not consider practical factors such as changes in motor parameters and operating states, making them unsuitable as direct diagnostic criteria for engineering sites.

[0005] Therefore, there is an urgent need for a rotor fault diagnosis method that can reasonably establish the mapping relationship between electrical quantities and vibration response and integrate electrical mechanisms, so as to further improve the accuracy and robustness of rotor fault diagnosis. Summary of the Invention

[0006] This invention proposes a method, system, and medium for fault diagnosis of asynchronous motor rotor squirrel cage that integrates electrical mechanism models, aiming to improve the accuracy and robustness of fault diagnosis.

[0007] This invention provides a method for diagnosing rotor squirrel cage faults in asynchronous motors that integrates electrical mechanism models. The method includes the following steps:

[0008] Step S10: Collect the three-phase stator voltage, three-phase stator current and operating speed of the asynchronous motor to be monitored; preprocess the three-phase stator voltage, three-phase stator current and operating speed to establish a vibration fault characteristic frequency set;

[0009] Step S20: Calculate the vibration fault feature prediction values ​​corresponding to all frequency points in the vibration fault feature frequency set using a preset strategy. The vibration fault feature prediction values ​​include the vibration displacement prediction values ​​of the measuring point at each frequency point in the feature frequency set, and the vibration base value generated by the main magnetic flux of the motor.

[0010] Step S30: Calculate the predicted waveform coefficient of the squirrel cage fault based on the predicted vibration displacement value and the vibration baseline value; calculate the measured waveform coefficient of the squirrel cage fault based on the fundamental frequency vibration extracted from the actual measured vibration spectrum and the vibration characteristic frequency components extracted from the frequencies included in the vibration fault characteristic frequency set; calculate the fault waveform coefficient based on the offset between the predicted waveform coefficient and the measured waveform coefficient, construct a fault feature vector, compare the fault feature vector with a preset fault area, and determine whether the motor has a fault based on the comparison result.

[0011] A further technical solution of the present invention is that step S10 includes:

[0012] Step S101, Electrical quantity reading and processing: During motor operation, the three-phase stator voltages (a, b, c) of the asynchronous motor to be monitored are collected. , , With three-phase stator current , , Extract the mapping of each frequency component in the complex frequency domain. , , and , , It is decomposed into positive and negative zero sequence voltages. , , and positive and negative zero sequence currents , , :

[0013] ;

[0014] ;

[0015] in ,in The imaginary unit, The base of the natural logarithm, subscript , , These represent the motors. Mutually, Mutually, Phase, subscript , , These represent the positive-sequence, negative-sequence, and zero-sequence components of voltage and current, respectively.

[0016] Step S102, Reading and processing operating parameters: While reading electrical quantities, simultaneously acquire the motor operating speed. And calculate the frequency. and slippage rate s :

[0017] ;

[0018] ;

[0019] ;

[0020] in For the fundamental frequency, p This represents the number of pole pairs of the motor. Synchronous speed;

[0021] Step S103: Establish a set of characteristic frequencies for vibration faults. :

[0022] ;

[0023] in The characteristic frequency of vibration, f 1 represents the fundamental frequency.

[0024] A further technical solution of the present invention is that step S20 includes:

[0025] Step S201, Calculation of excitation current components: Based on the positive and negative sequence voltage spectra , With the positive and negative sequence spectrum of current , Calculate the excitation electromotive force , With excitation current , :

[0026] ;

[0027] ;

[0028] ;

[0029] in and These are the stator winding resistance and the rotor winding resistance. and These are the stator winding leakage inductance and the rotor winding leakage inductance, respectively. and The frequencies are respectively f The slip rates of the magnetic field in positive and negative sequences are calculated using the following formulas:

[0030] ;

[0031] Step S202, Calculation of magnetic field components: Based on the excitation current components, calculate the number of pole pairs, rotation frequency, amplitude, and phase of each harmonic magnetic field to form a set of magnetic field harmonic components. :

[0032] ;

[0033] in Represents the record in the magnetic field set. k One magnetic field harmonic , , and These represent the pole pairs, rotation frequency, amplitude, and phase of the harmonic component, respectively. It is in phase with the current. and The calculation formula is:

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] in This represents the number of pole pairs of the motor. =0,1,2,3,…, For magnetizing inductance, and The frequencies are respectively f The positive-sequence excitation current and the negative-sequence excitation current. and Generated by positive sequence current and negative sequence current respectively The amplitude of the secondary magnetic field harmonic components and Generated by positive sequence current and negative sequence current respectively The rotation frequency of the secondary magnetic field harmonic components; and Generated by positive sequence current and negative sequence current respectively Phase of the secondary magnetic field harmonic components for The winding coefficients of the polar magnetic field harmonics, The winding coefficient of the main magnetic field;

[0039] Step S203: Calculate the spatial order, amplitude, rotational frequency, and phase of the electromagnetic force components within the motor based on the magnetic field, thus forming a set of records for the harmonic components of the magnetic field. F :

[0040] ;

[0041] in Represents the record of the electromagnetic force set. n One magnetic field harmonic , , and These are the spatial order, frequency, amplitude, and phase of the harmonic component, respectively.

[0042] when By magnetic field When it is generated, , , and The calculation formula is:

[0043] ;

[0044] when By magnetic field and When it is generated, , , and The calculation formula is:

[0045] ;

[0046] in Permeability of free space;

[0047] Step S204, Filtering frequency Included in the vibration fault frequency set The electromagnetic force components, and according to frequency With spatial order r Calculate the composite electromagnetic force :

[0048] ;

[0049] ;

[0050] in and These represent the amplitude and phase of the electromagnetic force, respectively. , , and These are electromagnetic force harmonic components. Spatial order, frequency, amplitude, and phase;

[0051] Step S205: Calculate electromagnetic vibrations under different spatial modes:

[0052] ;

[0053] ;

[0054] ;

[0055] in and They are respectively r The vibration frequency in the first vibration mode is The amplitude and phase of the vibration displacement component. For the motor stator at a frequency of stator of time motor r The vibration transfer function of the first vibration mode. for The phase;

[0056] Step S206: Calculate the measurement point at the characteristic frequency point. Vibration prediction value:

[0057] The measurement point is at the characteristic frequency point. vibration displacement The calculation formulas are as follows:

[0058] ;

[0059] ;

[0060] in The angle between the measuring point and the axis of the motor's A-phase winding;

[0061] Step S207: Calculate the vibration caused by the main magnetic flux of the motor as the vibration baseline value. :

[0062] ;

[0063] ;

[0064] ;

[0065] in The amplitude of the main magnetic field. and These represent the electromagnetic force generated by the main magnetic field and the amplitude of the vibration, respectively.

[0066] A further technical solution of the present invention is that step S30 includes:

[0067] Step S301, Calculate the predicted waveform coefficients: Read the vibration characteristic frequency set and the vibration prediction value at the corresponding fault frequency point Vibration prediction baseline Calculate the predicted waveform coefficients for squirrel cage faults:

[0068] ;

[0069] ;

[0070] in , and Frequency points , and Vibration prediction value, and These represent the 0th and 1st order predicted waveform coefficients, respectively;

[0071] Step S302, calculate the measured waveform coefficients: extract the fundamental frequency vibration from the vibration spectrum. and in Extract vibration characteristic frequency components from the included frequencies Calculate the measured waveform coefficients of the squirrel cage fault:

[0072] ;

[0073] ;

[0074] in , and Frequency points , and The measured vibration values, and These represent the measured waveform coefficients of the 0th and 1st order vibrations, respectively.

[0075] Step S303, Construct the fault feature vector: Based on the measured waveform coefficients , With predicted waveform coefficients , Calculate the offset between the 0th and 1st order fault waveform coefficients. , And construct fault feature vectors ,Right now:

[0076] ;

[0077] ;

[0078] ;

[0079] Step S304, Motor health status detection: When Located in a safe area If no fault occurs within the specified time, it is determined that no fault has occurred; when Located in the fault area If the motor malfunctions, an alarm signal will be issued.

[0080] A further technical solution of the present invention is that, in step S202, the stator resistance is obtained through motor test parameters or online parameter identification technology. Rotor resistance Iron loss resistance stator leakage Rotor leakage inductance Magnetizing inductor Extreme logarithms p .

[0081] A further technical solution of the present invention is that, in step S10, the speed of the motor is obtained by a tachometer or by a sensorless state identification technology.

[0082] A further technical solution of the present invention is that step S301 further includes:

[0083] Obtain the predicted value of vibration velocity Compared with measured values Or the predicted value of vibration acceleration Compared with measured values ; and The calculation formula is:

[0084] ;

[0085] .

[0086] A further technical solution of the present invention is that, in step S303, a fault feature vector is constructed. R When introducing weighting coefficients:

[0087] ;

[0088] in for k The weighting coefficients corresponding to the fault waveform coefficients.

[0089] To achieve the above objectives, the present invention also proposes an asynchronous motor rotor squirrel cage fault diagnosis system that integrates an electrical mechanism model. The system includes a memory, a processor, and an asynchronous motor rotor squirrel cage fault diagnosis program that integrates an electrical mechanism model stored on the processor. When the asynchronous motor rotor squirrel cage fault diagnosis program that integrates an electrical mechanism model is run by the processor, it executes the steps of the method described above.

[0090] To achieve the above objectives, the present invention also proposes a computer-readable storage medium storing an asynchronous motor rotor squirrel-cage fault diagnosis program that integrates an electrical mechanism model. When the asynchronous motor rotor squirrel-cage fault diagnosis program that integrates an electrical mechanism model is run by a processor, the steps of the method described above are executed.

[0091] This invention, which integrates an electrical mechanism model, provides a method, system, and medium for diagnosing rotor squirrel cage faults in asynchronous motors. Through an innovative architecture combining "data preprocessing + vibration fault characteristic prediction calculation + fusion diagnosis of calculation and measurement," it addresses the shortcomings of existing technologies, such as reliance on a single signal, weak anti-interference capabilities, and insufficient reliability, achieving the following beneficial effects:

[0092] 1. A deep integration mechanism for electrical quantities and vibration quantities was established. Through the electromagnetic force-vibration response mapping model, the characteristics of electrical faults were correlated and verified with the characteristics of mechanical vibration, thus solving the problem that single signal sources are easily interfered with.

[0093] 2. By calculating the predicted values ​​of vibration fault characteristics, a dynamic benchmark matching the operating conditions is constructed, effectively distinguishing the impact of non-fault factors such as load fluctuations and power grid disturbances from the actual fault.

[0094] 3. By combining the predicted values ​​of vibration fault characteristics based on electrical quantities with the measured values ​​of vibration monitoring, the difference between theory and measurement can be used to effectively eliminate algorithm interference through the difference method.

[0095] 4. Through high-order sideband harmonic expansion and weighting coefficient adjustment, it can be adapted to special working conditions such as variable frequency drive and pole changing operation, and has good technical scalability and multi-working-condition adaptability. Attached Figure Description

[0096] Figure 1 This is a flowchart illustrating a preferred embodiment of the asynchronous motor rotor squirrel cage fault diagnosis method integrating an electrical mechanism model according to the present invention.

[0097] Figure 2This is a schematic diagram illustrating the information transmission between the algorithm modules in a preferred embodiment of the asynchronous motor rotor squirrel cage fault diagnosis method integrating electrical mechanism model of the present invention;

[0098] Figure 3 This is a data processing flowchart of a fault diagnosis algorithm proposed in one embodiment of the present invention;

[0099] Figure 4 This is a hardware architecture diagram of the asynchronous motor rotor squirrel cage fault diagnosis system that integrates electrical mechanism models according to the present invention. Detailed Implementation

[0100] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0101] This invention proposes a fault diagnosis method for the rotor squirrel cage of an asynchronous motor that integrates an electrical mechanism model, such as... Figures 1 to 3 As shown, a preferred embodiment of the asynchronous motor rotor squirrel cage fault diagnosis method integrating electrical mechanism model of the present invention includes the following steps:

[0102] Step S10: Collect the three-phase stator voltage, three-phase stator current, and operating speed of the asynchronous motor to be monitored; preprocess the three-phase stator voltage, three-phase stator current, and operating speed to establish a vibration fault characteristic frequency set.

[0103] In this embodiment, the hardware architecture involved in the asynchronous motor rotor squirrel cage fault diagnosis method that integrates electrical mechanism models includes a data preprocessing module, a vibration fault feature prediction value calculation module, and a rotor squirrel cage fault diagnosis and judgment module.

[0104] The operating speed of the motor can be obtained through a tachometer or calculated using sensorless state identification technology.

[0105] The data preprocessing module is used to complete motor data acquisition, data processing, and characteristic frequency set calculation, providing standardized input for subsequent characteristic modules.

[0106] Step S10 specifically includes:

[0107] Step S101, Electrical quantity reading and processing: During motor operation, the three-phase stator voltages (a, b, c) of the asynchronous motor to be monitored are collected. , , With three-phase stator current , , The mapping of each frequency component in the complex frequency domain is extracted by using the Discrete Fourier Transform (DFT). , , and , , It is decomposed into positive and negative zero sequence voltages. , , and positive and negative zero sequence currents , , :

[0108] ;

[0109] ;

[0110] in ,in The imaginary unit, The base of the natural logarithm, subscript , , These represent the motors. Mutually, Mutually, Phase, subscript , , These represent the positive-sequence, negative-sequence, and zero-sequence components of voltage and current, respectively.

[0111] Step S102, Reading and processing operating parameters: While reading electrical quantities, simultaneously acquire the motor operating speed. And calculate the frequency. and slippage rate s :

[0112] ;

[0113] ;

[0114] ;

[0115] in For the fundamental frequency, p This represents the number of pole pairs of the motor. This is the synchronous speed.

[0116] Step S103: Establish a set of characteristic frequencies for vibration faults. :

[0117] ;

[0118] in The characteristic frequency of vibration, f 1 represents the fundamental frequency.

[0119] For example, for k=0~5th order vibration characteristic frequency And establish a set of characteristic frequencies of vibration faults. The formula is:

[0120] ;

[0121] ;

[0122] in and They are respectively The right band frequency and the left band frequency of the vibration characteristics. f 1 represents the fundamental frequency.

[0123] Step S20: Calculate the vibration fault feature prediction values ​​corresponding to all frequency points in the vibration fault feature frequency set using a preset strategy. The vibration fault feature prediction values ​​include the vibration displacement prediction values ​​of the measuring points at each frequency point in the feature frequency set, and the vibration base values ​​generated by the main magnetic flux of the motor.

[0124] In this embodiment, the vibration fault feature prediction value is calculated by the vibration fault feature prediction value calculation module.

[0125] Step S20 specifically includes the following steps:

[0126] Step S201, Calculation of excitation current components: Based on the positive and negative sequence voltage spectra , With the positive and negative sequence spectrum of current , Calculate the excitation electromotive force , With excitation current , :

[0127] ;

[0128] ;

[0129] ;

[0130] in and The resistances are those of the stator winding and the rotor winding (referred to the stator side). and These are the stator winding leakage inductance and the rotor winding leakage inductance (referred to the stator side), respectively. and The frequencies are respectively f The slip rates of the magnetic field in positive and negative sequences are calculated using the following formulas:

[0131] .

[0132] Step S202, Calculation of magnetic field components: Based on the excitation current components, calculate the number of pole pairs, rotation frequency, amplitude, and phase of each harmonic magnetic field to form a set of magnetic field harmonic components. :

[0133] ;

[0134] in Represents the record in the magnetic field set. k One magnetic field harmonic , , and These represent the pole pairs, rotation frequency, amplitude, and phase of the harmonic component, respectively. It is in phase with the current. and The calculation formula is:

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] in This represents the number of pole pairs of the motor. =0,1,2,3,…, For magnetizing inductance, and The frequencies are respectively f The positive-sequence excitation current and the negative-sequence excitation current. and Generated by positive sequence current and negative sequence current respectively The amplitude of the secondary magnetic field harmonic components and Generated by positive sequence current and negative sequence current respectively The rotation frequency of the secondary magnetic field harmonic components; and Generated by positive sequence current and negative sequence current respectively Phase of the secondary magnetic field harmonic components for The winding coefficients of the polar magnetic field harmonics, The winding coefficient of the main magnetic field.

[0140] In step S202, the stator resistance can be obtained through motor test parameters or online parameter identification technology. Rotor resistance Iron loss resistance stator leakage Rotor leakage inductance Magnetizing inductor Extreme logarithms p .

[0141] Step S203, Calculate the electromagnetic force: Calculate the spatial order, amplitude, rotational frequency, and phase of the electromagnetic force components within the motor based on the magnetic field, forming a set of records of the magnetic field harmonic components. F :

[0142] ;

[0143] in Represents the record of the electromagnetic force set. n One magnetic field harmonic , , and These represent the spatial order, frequency, amplitude, and phase of the harmonic component, respectively.

[0144] In this embodiment, the magnetic field that generates electromagnetic force is divided into two types based on its source: generated by a single magnetic field and generated by the interaction of two magnetic fields.

[0145] when By magnetic field When it is generated, , , and The calculation formula is:

[0146] ;

[0147] when By magnetic field and When it is generated, , , and The calculation formula is:

[0148] ;

[0149] in Permeability of free space;

[0150] Step S204, Filtering frequency Included in the vibration fault frequency set The electromagnetic force components, and according to frequency With spatial order r Calculate the composite electromagnetic force :

[0151] ;

[0152] ;

[0153] in and These represent the amplitude and phase of the electromagnetic force, respectively. , , and These are electromagnetic force harmonic components. Spatial order, frequency, amplitude, and phase;

[0154] Step S205: Calculate electromagnetic vibrations under different spatial modes:

[0155] ;

[0156] ;

[0157] ;

[0158] in and They are respectively r The vibration frequency in the first vibration mode is The amplitude and phase of the vibration displacement component. For the motor stator at a frequency of stator of time motor r The vibration transfer function of the first vibration mode. for The phase.

[0159] In this embodiment, the motor vibration modal transfer function can be calculated based on the equivalent cylindrical surface model, and the calculation formula is as follows:

[0160] ;

[0161] ;

[0162] in Let be the vibration mode transfer function under static (i.e., frequency 0) action. and respectively motor stator r The resonant frequency and damping ratio of the first vibration mode. E The elastic modulus of the stator-housing assembly. k Let be the elastic modulus of the motor base. R , R y , h y ,l The values ​​are the outer diameter of the motor, the average radius of the stator yoke, the thickness of the stator yoke, and the axial length.

[0163] In this embodiment, the motor vibration mode transfer function can also be obtained through modal testing.

[0164] Step S206: Calculate the measurement point at the characteristic frequency point. Vibration prediction value:

[0165] The measurement point is at the characteristic frequency point. vibration displacement The calculation formulas are as follows:

[0166] ;

[0167] ;

[0168] in The angle between the measuring point and the axis of the motor's phase A winding is denoted as .

[0169] Step S207: Calculate the vibration caused by the main magnetic flux of the motor as the vibration baseline value. :

[0170] ;

[0171] ;

[0172] ;

[0173] in The amplitude of the main magnetic field. and These represent the electromagnetic force generated by the main magnetic field and the amplitude of the vibration, respectively.

[0174] Step S30: Calculate the predicted waveform coefficient of the squirrel cage fault based on the predicted vibration displacement value and the vibration baseline value; calculate the measured waveform coefficient of the squirrel cage fault based on the fundamental frequency vibration extracted from the actual measured vibration spectrum and the vibration characteristic frequency components extracted from the frequencies included in the vibration fault characteristic frequency set; calculate the fault waveform coefficient based on the offset between the predicted waveform coefficient and the measured waveform coefficient, construct a fault feature vector, compare the fault feature vector with a preset fault area, and determine whether the motor has a fault based on the comparison result.

[0175] In this embodiment, the entity executing step S30 is the rotor squirrel cage fault diagnosis and judgment module.

[0176] Step S30 specifically includes the following steps:

[0177] Step S301, Calculate the predicted waveform coefficients: Read the vibration characteristic frequency set and the vibration prediction value at the corresponding fault frequency point Vibration prediction baseline Calculate the predicted waveform coefficients for squirrel cage faults:

[0178] ;

[0179] ;

[0180] in , and Frequency points , and Vibration prediction value, and These represent the 0th and 1st order prediction waveform coefficients, respectively.

[0181]

[0182]

[0183] in , and Respectively, frequency conversion and k The right-side band frequency and the left-side band frequency of the vibration characteristics, among which... , and Frequency points , and Vibration prediction value, and These represent the 0th and kth order predicted waveform coefficients, respectively.

[0184] Step S302, calculate the measured waveform coefficients: extract the fundamental frequency vibration from the vibration spectrum. and in Extract vibration characteristic frequency components from the included frequencies Calculate the measured waveform coefficients of the squirrel cage fault:

[0185] ;

[0186] ;

[0187] in , and Frequency points , and The measured vibration values, and These represent the measured waveform coefficients of the 0th and 1st order vibrations, respectively.

[0188]

[0189] in , and Frequency points , and The measured vibration values, and These represent the measured waveform coefficients of order 0 and order k, respectively.

[0190] It should be noted that this embodiment uses the predicted value of measured vibration velocity based on the sensing scheme employed on-site. Compared with measured values Or the predicted value of vibration acceleration Compared with measured values The predicted value of vibration displacement in steps S301 and S302 is replaced. Compared with measured values . and The calculation formula is:

[0191] ;

[0192] .

[0193] Step S303, Construct the fault feature vector: Based on the measured waveform coefficients , With predicted waveform coefficients , Calculate the offset between the 0th and 1st order fault waveform coefficients. , And construct fault feature vectors ,Right now:

[0194] ;

[0195] ;

[0196] .

[0197] It should be noted that this embodiment can also calculate based on the offset between the measured waveform coefficients and the predicted waveform coefficients. k =0~5th order fault waveform coefficient ~ And construct fault feature vectors ,Right now:

[0198] .

[0199] As one implementation scheme, this embodiment can further assist in diagnosis by using higher-order sideband harmonic components in the vibration, as described in step S103 regarding the vibration fault characteristic frequency set. Fault characteristic frequency The higher-order sideband harmonic components are included, that is:

[0200] .

[0201] The calculation of waveform coefficients in steps S301 to S303 is extended to sideband harmonics from the 0th to the Kth order, where the... k The calculation formula for the fault waveform coefficient and the complete fault feature vector R The expressions are as follows:

[0202]

[0203]

[0204]

[0205]

[0206]

[0207] in and Frequency points The predicted vibration value and the measured vibration value, for k Predicted waveform coefficients, for k Measured waveform coefficients of order, for k Waveform coefficients of first-order vibration faults, fault feature vectors R for K +1-dimensional real vector space Vectors in the array.

[0208] In step S303, a fault feature vector is constructed. R Weighting coefficients can be introduced at this time:

[0209] ;

[0210] in for k The weighting coefficients corresponding to the fault waveform coefficients.

[0211] Step S304, Motor health status detection: When Located in a safe area If no fault occurs within the specified time, repeat the above fault diagnosis process; when Located in the fault area If the motor malfunctions, an alarm signal will be issued.

[0212] In this embodiment, the safe area Area with rat cage malfunction The shape of the region can be used as a fault feature vector. The shape of a generalized sphere, generalized polyhedron, or other region in the vector space.

[0213] safe zone Area with rat cage malfunction The boundary parameters can be obtained through statistical analysis during the motor commissioning or healthy operation phase, and can be dynamically adjusted according to the calculation method of the characteristic relative offset vector and the changes in motor operating conditions.

[0214] In this embodiment, the faulty area of ​​the mouse cage can be... Further divided into , , These areas are used to distinguish faults of different severity.

[0215] when Located in different fault areas If the value is within a certain range, a motor malfunction is determined, and a corresponding fault signal is issued. In this embodiment, and The ranges are as follows:

[0216] ;

[0217] ;

[0218] ;

[0219] .

[0220] The asynchronous motor rotor squirrel cage fault diagnosis method of this invention, which integrates an electrical mechanism model, achieves the following beneficial effects through an innovative architecture of "data preprocessing + vibration fault feature prediction calculation + calculation and measurement fusion diagnosis" and addresses the shortcomings of existing technologies such as reliance on a single signal, weak anti-interference, and insufficient reliability:

[0221] 1. A deep integration mechanism for electrical quantities and vibration quantities was established. Through the electromagnetic force-vibration response mapping model, the characteristics of electrical faults were correlated and verified with the characteristics of mechanical vibration, thus solving the problem that single signal sources are easily interfered with.

[0222] 2. By calculating the predicted values ​​of vibration fault characteristics, a dynamic benchmark matching the operating conditions is constructed, effectively distinguishing the impact of non-fault factors such as load fluctuations and power grid disturbances from the actual fault.

[0223] 3. By combining the predicted values ​​of vibration fault characteristics based on electrical quantities with the measured values ​​of vibration monitoring, the difference between theory and measurement can be used to effectively eliminate algorithm interference through the difference method.

[0224] 4. Through high-order sideband harmonic expansion and weighting coefficient adjustment, it can be adapted to special working conditions such as variable frequency drive and pole changing operation, and has good technical scalability and multi-working-condition adaptability.

[0225] To achieve the above objectives, this invention also proposes an asynchronous motor rotor squirrel cage fault diagnosis system that integrates an electrical mechanism model, such as... Figure 4 As shown, the system includes a processor 1001, a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002, and an asynchronous motor rotor squirrel-cage fault diagnosis program with an integrated electrical mechanism model stored on the processor. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or stable non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0226] Those skilled in the art will understand that Figure 4 The system structure shown does not constitute a limitation on the system and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0227] like Figure 4 As shown, the memory 1005, which serves as a computer storage medium, may include an operating device, a network communication module, a user interface module, and an asynchronous motor rotor squirrel cage fault diagnosis program that integrates an electrical mechanism model.

[0228] exist Figure 4 In the system shown, the network interface 1004 is mainly used to connect to the network server and communicate with the network server; the user interface 1003 is mainly used to interact with the user terminal and receive user input instructions; and the processor 1001 can be used to call the asynchronous motor rotor squirrel cage fault diagnosis program with integrated electrical mechanism model stored in the memory 1005.

[0229] To achieve the above objectives, the present invention also proposes a computer-readable storage medium storing an asynchronous motor rotor squirrel-cage fault diagnosis program that integrates an electrical mechanism model. When the asynchronous motor rotor squirrel-cage fault diagnosis program that integrates an electrical mechanism model is run by a processor, the steps of the method described above are executed, which will not be repeated here.

[0230] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for diagnosing rotor squirrel cage faults in asynchronous motors that integrates electrical mechanism models, characterized in that, The method includes the following steps: Step S10: Collect the three-phase stator voltage, three-phase stator current and operating speed of the asynchronous motor to be monitored; preprocess the three-phase stator voltage, three-phase stator current and operating speed to establish a vibration fault characteristic frequency set; Step S20: Calculate the vibration fault feature prediction values ​​corresponding to all frequency points in the vibration fault feature frequency set using a preset strategy. The vibration fault feature prediction values ​​include the vibration displacement prediction values ​​of the measuring point at each frequency point in the feature frequency set, and the vibration base value generated by the main magnetic flux of the motor. Step S30: Calculate the predicted waveform coefficient of the squirrel cage fault based on the predicted vibration displacement value and the vibration baseline value; calculate the measured waveform coefficient of the squirrel cage fault based on the fundamental frequency vibration extracted from the actual measured vibration spectrum and the vibration characteristic frequency components extracted from the frequencies included in the vibration fault characteristic frequency set; calculate the fault waveform coefficient based on the offset between the predicted waveform coefficient and the measured waveform coefficient, construct a fault feature vector, compare the fault feature vector with a preset fault area, and determine whether the motor has a fault based on the comparison result. Step S10 includes: Step S101, Electrical quantity reading and processing: During motor operation, the three-phase stator voltages (a, b, c) of the asynchronous motor to be monitored are collected. , , With three-phase stator current , , Extract the mapping of each frequency component in the complex frequency domain. , , and , , It is decomposed into positive and negative zero sequence voltages. , , and positive and negative zero sequence currents , , : ； ； in ,in The imaginary unit, The base of the natural logarithm, subscript , , These represent the motors. Mutually, Mutually, Phase, subscript , , These represent the positive-sequence, negative-sequence, and zero-sequence components of voltage and current, respectively. Step S102, Reading and processing operating parameters: While reading electrical quantities, simultaneously acquire the motor operating speed. And calculate the frequency. and slippage rate s : ； ； ； in For the fundamental frequency, p This represents the number of pole pairs of the motor. Synchronous speed; Step S103: Establish a set of characteristic frequencies for vibration faults. : ； in The characteristic frequency of vibration, f 1 represents the fundamental frequency.

2. The asynchronous motor rotor squirrel cage fault diagnosis method based on the integrated electrical mechanism model according to claim 1, characterized in that, Step S20 includes: Step S201, Calculation of excitation current components: Based on the positive and negative sequence voltage spectra , With the positive and negative sequence spectrum of current , Calculate the excitation electromotive force , With excitation current , : ； ； ； in and These are the stator winding resistance and the rotor winding resistance. and These are the stator winding leakage inductance and the rotor winding leakage inductance, respectively. and The frequencies are respectively f The slip rates of the magnetic field in positive and negative sequences are calculated using the following formulas: ； Step S202, Calculation of magnetic field components: Based on the excitation current components, calculate the number of pole pairs, rotation frequency, amplitude, and phase of each harmonic magnetic field to form a set of magnetic field harmonic components. : ； in Represents the record in the magnetic field set. k One magnetic field harmonic , , and These represent the pole pairs, rotation frequency, amplitude, and phase of the harmonic component, respectively. It is in phase with the current. and The calculation formula is: ； ； ； ； in This represents the number of pole pairs of the motor. =0,1,2,3,…, For magnetizing inductance, and The frequencies are respectively f The positive-sequence excitation current and the negative-sequence excitation current. and Generated by positive sequence current and negative sequence current respectively The amplitude of the secondary magnetic field harmonic components and Generated by positive sequence current and negative sequence current respectively The rotation frequency of the secondary magnetic field harmonic components; and Generated by positive sequence current and negative sequence current respectively Phase of the secondary magnetic field harmonic components for The winding coefficients of the polar magnetic field harmonics, The winding coefficient of the main magnetic field; Step S203: Calculate the spatial order, amplitude, rotational frequency, and phase of the electromagnetic force components within the motor based on the magnetic field, thus forming a set of records for the harmonic components of the magnetic field. F : ； in Represents the record of the electromagnetic force set. n One magnetic field harmonic , , and These are the spatial order, frequency, amplitude, and phase of the harmonic component, respectively. when By magnetic field When it is generated, , , and The calculation formula is: ； when By magnetic field and When it is generated, , , and The calculation formula is: ； in Permeability of free space; Step S204, Filtering frequency Included in the vibration fault frequency set The electromagnetic force components, and according to frequency With spatial order r Calculate the composite electromagnetic force : ； ； in and These represent the amplitude and phase of the electromagnetic force, respectively. , , and These are electromagnetic force harmonic components. Spatial order, frequency, amplitude, and phase; Step S205: Calculate electromagnetic vibrations under different spatial modes: ； ； ； in and They are respectively r The vibration frequency in the first vibration mode is The amplitude and phase of the vibration displacement component. For the motor stator at a frequency of stator of time motor r The vibration transfer function of the first vibration mode. for The phase; Step S206: Calculate the measurement point at the characteristic frequency point. Vibration prediction value: The measurement point is at the characteristic frequency point. vibration displacement The calculation formulas are as follows: ； ； in The angle between the measuring point and the axis of the motor's A-phase winding; Step S207: Calculate the vibration caused by the main magnetic flux of the motor as the vibration baseline value. : ； ； ； in The amplitude of the main magnetic field. and These represent the electromagnetic force generated by the main magnetic field and the amplitude of the vibration, respectively.

3. The asynchronous motor rotor squirrel cage fault diagnosis method based on the integrated electrical mechanism model according to claim 2, characterized in that, Step S30 includes: Step S301, Calculate the predicted waveform coefficients: Read the vibration characteristic frequency set and the vibration prediction value at the corresponding fault frequency point Vibration prediction baseline Calculate the predicted waveform coefficients for squirrel cage faults: ； ； in , and Frequency points , and Vibration prediction value, and These represent the 0th and 1st order predicted waveform coefficients, respectively; Step S302, calculate the measured waveform coefficients: extract the fundamental frequency vibration from the vibration spectrum. and in Extract vibration characteristic frequency components from the included frequencies Calculate the measured waveform coefficients of the squirrel cage fault: ； ； in , and Frequency points , and The measured vibration values, and These represent the measured waveform coefficients of the 0th and 1st order vibrations, respectively. Step S303, Construct the fault feature vector: Based on the measured waveform coefficients , With predicted waveform coefficients , Calculate the offset between the 0th and 1st order fault waveform coefficients. , And construct fault feature vectors ,Right now: ； ； ； Step S304, Motor health status detection: When Located in a safe area If no fault occurs within the specified time, it is determined that no fault has occurred; when Located in the fault area If the motor malfunctions, an alarm signal will be issued.

4. The asynchronous motor rotor squirrel cage fault diagnosis method based on the integrated electrical mechanism model according to claim 2, characterized in that, In step S202, the stator resistance is obtained through motor test parameters or online parameter identification technology. Rotor resistance Iron loss resistance stator leakage Rotor leakage inductance Magnetizing inductor Extreme logarithms p .

5. The asynchronous motor rotor squirrel cage fault diagnosis method based on the integrated electrical mechanism model according to claim 1, characterized in that, In step S10, the motor speed is obtained by a tachometer or by sensorless state identification technology.

6. The asynchronous motor rotor squirrel cage fault diagnosis method based on the integrated electrical mechanism model according to claim 3, characterized in that, Step S301 further includes: Obtain the predicted value of vibration velocity Compared with measured values Or the predicted value of vibration acceleration Compared with measured values ; and The calculation formula is: ； 。 7. The asynchronous motor rotor squirrel cage fault diagnosis method based on the integrated electrical mechanism model according to claim 3, characterized in that, In step S303, a fault feature vector is constructed. R When introducing weighting coefficients: ； in for k The weighting coefficients corresponding to the fault waveform coefficients.

8. A fault diagnosis system for an asynchronous motor rotor squirrel cage integrating an electrical mechanism model, characterized in that, The system includes a memory, a processor, and an asynchronous motor rotor squirrel cage fault diagnosis program with a fused electrical mechanism model stored on the processor. The asynchronous motor rotor squirrel cage fault diagnosis program with the fused electrical mechanism model is executed by the processor to perform the steps of the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an asynchronous motor rotor squirrel cage fault diagnosis program with integrated electrical mechanism model, which, when run by a processor, performs the steps of the method as described in any one of claims 1 to 7.

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