System and method for detecting broken bar faults in squirrel cage induction motors

Abstract

A computer-implemented method for detecting broken bar faults in an induction motor during operation is provided, comprising the steps of injecting a frequency modulated continuous wave (FMCW) voltage signal into a voltage source to power the motor, acquiring, in the time domain, signals of stator currents powering the induction motor via an interface, performing a Fourier transform (FT) on the stator currents and the injected FMCW signal to obtain a spectrum of the stator currents and a spectrum of the injected signal, calculating a cross-correlation between the spectrum of the injected signal and the spectrum of the stator currents, and calculating the cross-correlation function at a frequency f=±2(1-s)f 0 and detecting a broken bar fault in the induction motor if the magnitude of the fault signature is greater than a threshold.

Description

[Technical Field]

[0001] The present invention relates generally to the field of monitoring electric machines, and more particularly to broken bar fault detection in induction motors using (FMCW) signal injection. [Background technology]

[0002] Broken rotor bars (BRBs) are one of the most common faults in squirrel-cage induction motors. While BRB failures generally do not lead to immediate failure of the induction motor, they can have serious secondary effects such as poor starting performance, excessive vibration, and torque fluctuations. In some cases, broken pieces can strike the stator windings at high speed, causing catastrophic failure of the winding insulation. Therefore, it is very important to detect BRB failures and carry out timely repairs or maintenance.

[0003] To detect BRB faults, motor current signature analysis (MCSA) is widely used because it is non-invasive and low-cost. When one or more rotor bars break in a squirrel-cage induction motor, the rotor will induce extra frequency components in the stator current during operation due to the rotor asymmetry caused by the broken bars. Therefore, BRB fault detection can be achieved by detecting the characteristic frequency components.

[0004] However, there are three major problems that hinder detection performance. First, the magnitude of the characteristic frequency is relatively small depending on the total number of rotor bars. Second, the characteristic frequency is very close to the power supply frequency, making it difficult to distinguish the fault signature from the dominant operating frequency components. Third, background noise hinders detection performance. Due to these practical issues, the characteristic frequency may be buried in the side lobes or noise of the power supply frequency components. Therefore, it is difficult to detect broken bar faults.

[0005] Over the past few decades, various MCSA methods have been developed to improve detection performance. For example, researchers have used signal processing techniques such as ESPRIT, MUSIC, and compressed sensing to achieve high-resolution frequency spectra that allow for good separation of characteristic frequency components. However, these methods typically require a high signal-to-noise ratio and may not work well in strong noise conditions. As another example, researchers have used the transient start-up process of a motor to detect broken bar faults. In this case, the rotation speed is much lower, so the characteristic frequency components are well separated from the fundamental frequency. However, this method requires restarting the motor, making it unsuitable for online monitoring. Furthermore, because the characteristic frequency changes during the start-up process, it is difficult to capture the fault characteristic frequency components in a short time.

[0006] Signal injection methods are widely used for fault detection. For example, Jordi Cusido et al. proposed injecting a wideband signal into a motor and measuring the system response. Motor faults can be detected based on changes in the impulse response. However, this signal injection method lacks theoretical support and physical modeling. In practice, it is unclear how to distinguish whether a change is due to a fault or noise.

[0007] Therefore, there is a need for a method and system for detecting broken bar faults in squirrel cage induction motors that provides improved detection performance. Summary of the Invention

[0008] It is an object of some embodiments of the invention to provide a system and method suitable for performing broken bar fault detection in an induction motor based on analysis of the stator currents powering the induction motor under varying speed and load operation, such that broken bar fault detection can be performed continuously concurrently with the operation of the induction motor without the need to restart the induction motor.

[0009] The main differences between our method and other existing methods are as follows: First, our method is an active sensing method that actively monitors the induced stator currents of an injected frequency modulation continuous wave (FMCW) signal, whereas most existing methods are passive methods that do not inject a signal. Second, our injected signal is an FMCW signal, whereas other signal injection methods mainly inject narrow pulses rather than FMCW signals. Third, our method aims to extract fault signatures based on physical analysis of motor dynamics and signal processing techniques, and demonstrates superior performance by increasing the magnitude of the fault signature in very noisy conditions, whereas other detection methods rely on very weak fault signatures or changes in stator currents that may be due to noise or other interference.

[0010] Some embodiments of the present invention provide a method for extracting motor fault signatures by actively injecting an FMCW signal. The FMCW signal has a small amplitude and a frequency band higher than the fundamental frequency. Because the FMCW signal has a small amplitude, it does not interfere with the operation of the motor. Under a broken bar fault condition, the injected signal will induce another FMCW signal with a frequency band lower than that of the injected signal. By using signal processing techniques to analyze the cross-correlation between the injected and induced signals in the frequency domain, the fault signature can be robustly extracted even in noisy conditions.

[0011] Some embodiments of the present invention are based on the recognition that a motor broken bar fault signature in the stator current is difficult to extract because its magnitude is small and close to the operating frequency. The present invention can provide a broken bar fault detection method that improves detection performance and sensitivity by injecting a frequency-modulated continuous wave (FMCW) signal into the stator voltage. Under broken bar fault conditions, this injected signal will induce another FMCW signal in a frequency band that is coherent with the injected signal in the frequency domain but has a lower frequency than that of the injected signal. By analyzing the cross-correlation between the injected signal and the induced signal, the fault signature can be robustly extracted even under strong noise conditions.

[0012] Some embodiments of the computer-implemented method use a dynamic model with a multi-loop equivalent circuit to simulate stator currents, where broken bar faults are modeled by open circuits in the corresponding branches. The injection signal is simulated by adding an extra term to the stator voltage, and the stator current is simulated and monitored using the dynamic model. We then develop signal processing techniques to extract fault signatures by analyzing the stator currents. Simulation results demonstrate that our approach can effectively detect broken bar faults even in noisy conditions.

[0013] Motor current signature analysis (MCSA) can be used to detect broken rotor bar (BRB) faults because it is non-invasive and low cost. When one or more rotor bars break in a squirrel-cage induction motor, the rotor generates a frequency component f in the stator current during operation due to rotor asymmetry. b =(1±2κs)f0, where s is the slip speed, f0 is the power supply frequency, and κ is the harmonic frequency index. Of these extra induced components, the (1-2s)f0 component is the strongest and is typically treated as the characteristic frequency of a BRB fault. Therefore, BRB fault detection is achieved by detecting the characteristic frequency component (1-2s)f0.

[0014] Furthermore, three major issues hinder detection performance. First, the magnitude of the signature frequency is relatively small, depending on the total number of rotor bars. For example, for a squirrel-cage motor with 30 rotor bars, the fault component is typically 30–40 dB lower than the fundamental power frequency component. Second, the signature frequency is very close to the power frequency f0. Under steady-state operating conditions, the slip speed s typically ranges from 0.005 to 0.05 Hz. For a power supply with f0 = 50 Hz, the difference between the signature frequency and the fundamental frequency f0 can be as small as 0.01 Hz (f0 = 0.5 Hz), making it difficult to distinguish the fault signature from the dominant operating frequency component. Third, background noise hinders detection performance. Due to these practical issues, the signature frequency can be buried in the side lobes or noise of the power frequency component. Therefore, it is difficult to detect broken bar faults.

[0015] Some embodiments of the present invention are based on the recognition that under fault conditions, the resulting stator currents powering an induction motor are sparse in the frequency domain because they contain the fundamental frequency of the power source generating the stator currents, its harmonics, and fault frequency components caused by the fault.

[0016] According to an embodiment of the present invention, a broken bar fault detection method is provided that improves detection performance by injecting an FMCW signal into the stator voltage. Based on an equivalent circuit model, when a broken bar fault occurs in a squirrel-cage induction motor, this injected signal will induce another FMCW signal in a certain frequency band. By analyzing the cross-correlation of the signals, the fault signatures can be well separated and effectively extracted even under strong noise conditions. Simulation results demonstrate that our proposed method significantly improves broken bar fault detection performance.

[0017] Additionally, some embodiments of the present invention provide a computer-implemented fault signal measurement method for detecting broken bar faults during operation of an induction motor powered by a stator voltage. In this case, the method uses a processor coupled to a memory storing instructions implementing the method, which instructions, when executed by the processor, perform steps of the method, including injecting an FMCW signal into the operating induction motor via an interface, the FMCW signal being modulated such that the FMCW signal is superimposed on a fundamental frequency of the stator voltage, and further including acquiring, via the interface, a response current signal generated from the induction motor in response to the injected FMCW signal for a frequency sweep period; performing a spectral analysis of the injected FMCW signal and the response current of the induction motor; calculating a cross-correlation between a stator current frequency spectrum of the induction motor and a frequency spectrum of the injected FMCW voltage signal; extracting a fault signature of a broken bar fault from the calculated cross-correlation result; and determining that a broken bar fault has occurred in the induction motor if a magnitude of the fault signature is greater than a threshold.

[0018] Further, according to some embodiments of the present invention, there is provided a fault detection apparatus for detecting broken bar faults during operation of an induction motor powered by a stator voltage, the fault detection apparatus including: an interface connected to a frequency modulated continuous wave (FMCW) generator and configured to cause a controller of the induction motor to add an FMCW signal to the stator voltage via the FMCW generator; a memory configured to store instructions of a computer-implemented fault signal measurement method for detecting broken bar faults during operation of the induction motor powered by a stator voltage; and a processor coupled to the memory, the processor configured to execute the instructions implementing the method, the instructions, when executed by the processor, performing method steps, the method steps including injecting the FMCW signal into the stator voltage of the induction motor via the interface, the FMCW signal being added to the FMCW signal. a W signal is modulated to be superimposed on a fundamental frequency of the stator voltage, and the method steps further include acquiring, during a frequency sweep period, a response current signal generated from the induction motor in response to the injected FMCW signal via the interface connected to a sensor disposed in the induction motor; performing a spectral analysis of the injected FMCW signal and the response current of the induction motor; calculating a cross-correlation between a stator current frequency spectrum of the induction motor and a frequency spectrum of the injected FMCW signal; extracting a fault signature of a broken bar fault from the calculated result of the cross-correlation; and determining that a broken bar fault has occurred in the induction motor if the magnitude of the fault signature is greater than a threshold.

[0019] The presently disclosed embodiments will be further described with reference to the accompanying drawings, in which the drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the presently disclosed embodiments. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a schematic diagram of a system for controlling an induction motor according to an embodiment of the invention. [Figure 2A] FIG. 2 is a diagram showing an equivalent circuit of the stator windings and rotor in a squirrel-cage induction motor in a normal, healthy state according to an embodiment of the invention. [Figure 2B] FIG. 2 is a diagram showing an equivalent circuit of the stator windings and rotor in a squirrel-cage induction motor in a normal, healthy state according to an embodiment of the invention. [Figure 2C] FIG. 10 illustrates an equivalent circuit for a broken bar fault condition, according to an embodiment of the invention. [Figure 3A] 1A-1C show stator current spectra for a single high frequency (HF) signal injection under healthy and fault conditions, respectively, according to an embodiment of the invention. [Figure 3B] 1A-1C show stator current spectra for a single high frequency (HF) signal injection under healthy and fault conditions, respectively, according to an embodiment of the invention. [Figure 4A] 10A-10C are exemplary plots of stator voltage with and without FMCW signal injection powering an induction motor in a zoomed-in view of time and voltage, according to an embodiment of the invention. [Figure 4B] 10A-10C are exemplary plots of stator voltage with and without FMCW signal injection powering an induction motor in a zoomed-in view of time and voltage, according to an embodiment of the invention. [Figure 5A] 10A-10C are exemplary plots of stator current spectra with FMCW injection in healthy and fault conditions, respectively, according to an embodiment of the invention. [Figure 5B] 10A-10C are exemplary plots of stator current spectra with FMCW injection in healthy and fault conditions, respectively, according to an embodiment of the invention. [Figure 5C] 1A-1C are exemplary plots of spectra processed using cross-correlation analysis for fault signature extraction in healthy and faulty conditions, respectively, according to an embodiment of the invention. [Figure 5D] 1A-1C are exemplary plots of spectra processed using cross-correlation analysis for fault signature extraction in healthy and faulty conditions, respectively, according to an embodiment of the invention. [Figure 6] FIG. 1 is a block diagram of a method for detecting faults in an induction motor, according to an embodiment of the present invention. [Figure 7] FIG. 4 is a block diagram of a method for automatically detecting faults in an induction motor according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0021] Description of the embodiment Various embodiments of the present invention will now be described with reference to the drawings. It should be noted that the drawings are not drawn to scale, and that elements of similar structure or function are designated by like reference numerals throughout the drawings. It should also be noted that the drawings are intended merely to facilitate the description of particular embodiments of the present invention. They are not intended to be an exhaustive description of the invention or to limit the scope of the invention. In addition, aspects described in connection with a particular embodiment of the present invention are not necessarily limited to that embodiment, but may also be practiced in other embodiments of the present invention.

[0022] 1 is a schematic diagram of a fault detection system configuration 10 illustrating an exemplary squirrel-cage induction motor according to one embodiment of the invention. The squirrel-cage induction motor 100 includes a squirrel-cage rotor assembly 102, a stator assembly 104, a main shaft 106, and main bearings 108. In this example, the induction motor 100 is a squirrel-cage induction motor. Broken rotor bars in assembly 102 are a common fault in such induction motors.

[0023] Controller 110 is powered by power source 120 and can be used to monitor and control the operation of induction motor 100 in response to various inputs in accordance with embodiments of the present invention. For example, a controller coupled to induction motor 100 can control the speed of the induction motor based on input received from sensors 130 configured to obtain data regarding the operating conditions of induction motor 100. According to certain embodiments, the electrical signal sensors can be current and voltage sensors for obtaining current and voltage data related to induction motor 100. For example, the current sensors sense current data from one or more of the induction motor's phases. More specifically, if the induction motor is a three-phase induction motor, the current and voltage sensors sense current and voltage data from the three phases of the three-phase induction motor. While certain embodiments of the present invention are described with respect to a polyphase induction motor, other embodiments of the present invention are applicable to other polyphase electromechanical machines.

[0024] The FMCW generator 140 generates a small magnitude FMCW voltage signal (approximately 2% of the power supply voltage) that the controller 110 injects into the power supply signal (FIG. 4A) to power the induction motor 100 for diagnosing the induction motor 100.

[0025] Some embodiments of the present invention describe a system for detecting broken bar faults in an electric machine, such as induction motor 100. The system configured for detection includes a fault detection module 200 for detecting the presence of fault conditions in various components, including the rotor bars, within the induction motor assembly. Fault detection module 200 may be referred to as a fault detection device. In one embodiment, fault detection module 200 is implemented as a subsystem of controller 110. In an alternative embodiment, fault detection module 200 is implemented using a separate processor. Fault detection module 200 may be a hardware circuit module operably connected to controller 110. In some implementations, fault detection module 200 and controller 110 may share information. For example, fault detection module 200 may reuse sensor data used by the controller to control the operation of the induction motor.

[0026] The fault detection module 200 further includes a processor 210, a memory 220, and a fault detection program 230. The fault detection program 230 is stored in a storage and uploaded to the memory 220 when instructions of the program 230 are executed by the processor 210. The module 200 further includes an interface 250 configured to acquire signals from the sensors 130. The interface 250 includes the processor 210, the memory 220, the fault detection program 230, a user interface 240, and an A / D (analog-to-digital) converter and an A / D (analog-to-digital) converter for data communication with the sensors 130. The processor 210 may be multiple processors, and the memory 220 may be a memory module including multiple memories. The user interface 240 is configured to connect to a keyboard and a display configured to display normal / fault status information of the induction motor 100 in response to an output of the fault detection module 200.

[0027] The presence of a broken bar in induction motor 100 reduces rotor torque and increases reliance on other rotor bars in induction motor 100 to provide the desired current. This increased reliance on other rotor bars accelerates the rate at which the other rotor bars deteriorate, affecting the overall performance of the induction motor.

[0028] In one embodiment of the present invention, current and voltage sensors detect stator current and stator voltage data, respectively, from stator assembly 104 of induction motor 100. The current and voltage data obtained from the sensors is communicated to a controller and / or fault detection module for further processing and analysis. This analysis includes performing motor current signature analysis (MCSA) to detect faults in induction motor 100 using a cross-correlation-based method. In some embodiments, upon detecting a fault using fault detection module 200, controller 110 receives a fault detection signal via interface 250 of fault detection module 200 and stops operation of the induction motor by sending a signal to controller 110 to interrupt the stator voltage of induction motor 100 for further inspection or repair. In some cases, sensor 130 may include a controller interface (not shown) configured to receive the fault detection signal from interface 250 and send a fault condition signal to controller 110, such that controller 110 interrupts the stator voltage of induction motor 100 to stop operation of induction motor 100. If the sensor 130 does not include a controller interface, the interface 250 may be configured to connect to the controller 110 such that the controller 110 interrupts the stator voltage of the induction motor 100 to stop operation of the induction motor 100 in response to a fault detection signal from the fault detection module 200 via the interface 250.

[0029] The system also includes a memory for storing the signal measurements, the injected signal, and various parameters and coefficients for performing the cross-correlation analysis, and a user interface for indicating a fault when a peak component at a frequency near f=-2(1-s)f0 in the cross-correlation function is detected as a fault signature.

[0030] Broken rotor bars (BRBs) are one of the most common faults in squirrel-cage induction motors. Although BRB failures generally do not lead to immediate failure of the induction motor, they can have serious secondary effects such as poor starting performance, excessive vibration, and torque fluctuations. In some cases, broken pieces can strike the stator windings at high speed, causing catastrophic failure of the winding insulation. Therefore, it is very important to detect BRB failures and perform timely maintenance.

[0031] The motor current signature analysis (MCSA) method is widely used to detect BRB faults because it is non-invasive and low-cost. When one or more rotor bars break in a squirrel-cage induction motor, the rotor generates a frequency component f in the stator current during operation due to rotor asymmetry. b =(1±2κs)f0, where s is the slip speed, f0 is the power supply frequency, and κ is the harmonic frequency index. Of these extra components, the (1-2s)f0 component is the strongest and is typically treated as the characteristic frequency of a BRB fault. Therefore, BRB fault detection using the MCSA method is achieved by detecting the characteristic frequency component (1-2s)f0.

[0032] In practice, there are three major problems with detecting the characteristic frequency component. First, the magnitude of the characteristic frequency is relatively small, depending on the total number of rotor bars. For example, for a squirrel-cage motor with 30 rotor bars, the fault component is typically 30–40 dB below the fundamental power frequency component. The larger the number of rotor bars, the smaller the relative magnitude of the fault component. Second, the characteristic frequency is very close to the power frequency f0. Under steady-state operating conditions, the slip speed range is typically 0.005–0.05 Hz. For a power supply with f0 = 50 Hz, the difference between the characteristic frequency and the fundamental frequency f0 can be as small as 0.01 Hz (f0 = 0.5 Hz), making it difficult to distinguish the fault signature from the dominant operating frequency component. Third, background noise hinders detection performance. Due to these practical issues, the characteristic frequency can be buried in the side lobes or noise of the power frequency component.

[0033] Over the past few decades, researchers have developed various MCSA methods to improve detection performance. For example, studies have used signal processing techniques such as ESPRIT, MUSIC, and compressed sensing to achieve high-resolution frequency spectra that can effectively isolate characteristic frequency components. However, these methods typically require a high signal-to-noise ratio and may not work well in strong noise conditions. As another example, researchers have used the motor start-up process to detect broken bar faults. As the motor speed increases from zero to a constant asynchronous speed, the slip velocity s decreases from 1 to a small number close to 0. In this case, the characteristic frequency components are well separated from the power frequency components in the frequency domain. However, this method requires a motor restart, making it unsuitable for online monitoring. Furthermore, the characteristic frequency changes during the start-up process, making it difficult to capture the fault characteristic frequency components in a short time.

[0034] This disclosure proposes a method for extracting motor fault signatures by actively injecting a frequency-modulated continuous wave (FMCW) signal. This FMCW signal has a small magnitude and a frequency band higher than the fundamental frequency. Because of its small magnitude, this FMCW signal does not interfere with the operation of the motor. Under a broken bar fault condition, this injected signal will induce another FMCW signal with a frequency band lower than that of the injected signal. By using signal processing techniques to analyze the cross-correlation between the induced signal and the injected signal, the fault signature can be robustly extracted even in noisy conditions.

[0035] Signal injection, an active sensing method, is widely used for motor fault detection. For example, a high-frequency (HF) sinusoidal signal can be injected into the stator and the induced harmonics can be measured to detect motor faults. The current spectrum for a healthy motor is shown in Figure 3A, and the current spectrum for a faulty motor is shown in Figure 3B. However, this high-frequency signal injection method proved difficult to detect broken bar faults because the magnitude of the induced signal was too small to detect. In Figure 3B, the magnitude of the fault signature is approximately 70 dB lower than the operating signal, making detection extremely difficult, especially in noisy environments.

[0036] The main differences between our proposed method and other existing high-frequency signal injection methods are as follows: First, our injection signal is an FMCW signal, whereas other signal injection methods mainly inject a single high-frequency signal or a narrow time-domain pulse. Second, our fault signature extraction process is based on a physical model and signal cross-correlation analysis, rather than simply thresholding frequency components. Third, our fault signature frequency is located at frequencies around f = ±2(1-s)f0 of the cross-correlation function, whereas other MSCA-based methods obtain fault signatures at the frequency f = (1-2s)f0. In addition, our method exhibits robust performance in noisy environments.

[0037] To validate our method, we construct a dynamic model of a squirrel-cage induction motor using a multi-loop equivalent circuit to represent the coupling between the stator and rotor. We simulate the stator currents under healthy and fault conditions by modifying the corresponding equivalent circuits. We then develop signal processing techniques to extract the fault signature. Simulation results demonstrate that our method improves the robustness of broken bar fault detection under noisy conditions. Dynamic model of an induction motor

[0038] In a squirrel-cage induction motor, the stator consists of three sinusoidally distributed windings offset by 120°. The rotor consists of longitudinal conductive bars connected at both ends by short-circuited rings, giving it a squirrel-cage shape. When an induction motor operates, the stator windings create a rotating magnetic field through the rotor, inducing currents in the rotor bars and generating tangential forces perpendicular to the rotor, resulting in a torque that rotates the shaft.

[0039] In the remainder of this section, we first develop a dynamic model of the motor under normal healthy conditions and then extend it to fault conditions. For simplicity, we ignore magnetic saturation and assume linear magnetic characteristics. We use bold capital letters for matrices, regular capital letters for constant parameters, and lowercase letters for time-varying parameters.

[0040] Figure 2A shows the equivalent circuit of the stator winding, and Figure 2B shows the equivalent circuit of the rotor in a squirrel-cage induction motor. Assuming there are n rotor bars, the squirrel-cage rotor can be modeled as n+1 independent current loops, of which n are identical circuit loops in the ideal case, each loop consisting of two adjacent rotor bars connected by two end ring sections. The remaining circuit loop is formed by one of the end rings. Therefore, the rotor current distribution can be expressed in terms of (n+1) independent loop currents, i.e., n rotor bar loop currents i j (1≦j≦n) one end ring loop current i eIt can be identified in terms of the sum of the above.

number

[0041] It is important to note that the stator winding resistance in (7) and the stator inductance in (8) are constant under our assumption, but the stator-rotor mutual inductance in (9) varies with the angular position of the rotor, because the mutual inductance is related to the relative position between the stator winding and the rotor bars, which changes during operation.

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[0042]

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[0043] In summary, equations (1) through (19) form a dynamic model of an induction motor with unknown stator and rotor currents. Given the motor parameters, standard methods, such as the fourth-order Runge-Kutta method for solving differential equations, can be used to simulate the stator currents during dynamic operation. Fault condition model

[0044] Some embodiments of a computer-implemented method are based on a dynamic model using a multi-loop equivalent circuit to simulate the stator currents of an induction motor. In this case, a broken bar fault is modeled by an open circuit in the corresponding branch. The injection signal is simulated by adding an extra term to the stator voltage, and the stator currents are simulated and monitored using the dynamic model.

[0045] If one bar fails completely, the associated branch becomes an open circuit. The two associated loops are then replaced by a new loop with twice the number of end ring segments, reducing the total number of circuit loops by one. The equivalent circuit of the rotor under fault conditions is shown in Figure 2C. As a result, the corresponding loops in (11) to (16) must be deleted or reconstructed using equivalent parameters.

[0046] Compared with other models such as the dq model, this equivalent circuit model is easier to understand and can flexibly simulate intermediate fault conditions. Motor parameters

[0047] Under normal healthy conditions, the inductances and resistances of (7), (8), (9), (15), and (16) can be calculated. The details of the parameter calculations are omitted. Instead, our simulations use publicly available parameters. FMCW injection based broken bar fault detection

[0048] In this section, we ignore the detailed hardware implementation and focus on fault signature extraction from a signal processing perspective. FMCW signal

[0049]

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[0050]

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[0051]

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[0052] amplitude U iIt is selected to fold the two sides. On the one hand, the amplitude must be small enough so that the injection signal does not interfere with the motor operation. On the other hand, the signal must be large enough so that the induced signal can be detected.

[0053] Figures 4A and 4B are exemplary plots of the stator voltage u a0 (t) in (20) and the stator voltage u a (t) in (23) when no signal injection is performed to supply power to the induction motor and when FMCW signal injection is performed, respectively, in enlarged views of time and voltage according to some embodiments of the invention. As can be seen from Figure 4B, when the FMCW signal is injected into the power supply voltage, the power supply waveform is slightly distorted. Since U i <<U0, the extremely small distortion caused by the injection signal does not affect the motor operation.

[0054]

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[0055]

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[0056] When f1 = 0, that is, when f min = f max = f2, the injection signal becomes a single-frequency signal having a frequency f2, where f2 > f0. Figures 3A and 3B are exemplary plots of the current spectra when a single-frequency signal injection is performed to supply power to the induction motor and when it is not performed, according to some embodiments of the invention. However, it has been found that with this high-frequency signal injection method, it is difficult to detect broken bar faults because the magnitude of the induced signal is too small to be detected. In Figure 3B, the magnitude of the fault signature is approximately 70 dB lower than that of the operating signal, so detection is very difficult, especially in a noisy environment.

[0057]

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[0058] If the kth harmonic amplitude is much greater than that of the fundamental frequency, the motor will accelerate to a much higher speed. Since the kth harmonic amplitude is not large enough to accelerate the motor speed, the motor will always run at a constant low speed during the starting process.

[0059]

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[0060] When k>>1, the absolute value of the induced frequency is 2(1-s)f0 lower than the injection frequency. Note that this frequency shift 2(1-s)f0 between the injected signal and the induced signal does not depend on the frequency of the injected signal. The magnitude of the induced signal is very small and difficult to detect, but this property can be used to detect faults. When an FMCW voltage signal of a certain band is injected, the frequency band of the induced signal shifts by 2(1-s)f0 to a lower frequency band. Next, the cross-correlation between the injected signal spectrum and the induced signal spectrum is calculated by integrating all frequency components. It is expected that a peak component can be detected at the frequency shift f=-2(1-s)f0.

[0061]

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[0062]

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[0063]

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[0064] Typically, I F,f < i,f and I F,f ​<I0, that is, the magnitude of the induced fault signal is much smaller than the magnitude of the injection signal and the magnitude of the operating signal.

[0065] Figures 5A and 5B are exemplary plots of the stator current spectrum in the healthy and faulty states, respectively, for FMCW injection according to some embodiments of the present invention, using an injection signal frequency band of 650 Hz to 750 Hz, or the 13th to 15th harmonics of k = 13. It is clear that the magnitude of the induced fault signal from 550 Hz to 650 Hz is more than 20 dB lower than the magnitude of the injection signal, and at 50 Hz it is more than 80 dB lower than the magnitude of the operating signal. Since the magnitude of the fault signal is small, detection is difficult, especially in noisy measurements.

[0066]

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[0068] Figure 6 is a block diagram showing the steps of a computer-implemented fault signal measurement method for automatically detecting a broken bar fault during the operation of an induction motor powered by a stator voltage. The computer-implemented fault signal measurement method includes at least the step of injecting an FMCW signal into the stator voltage at 610, the step of receiving a measured value of the stator current at 620, the step of spectral analysis and signal processing at 630, and the step of determining a fault when a fault signature is extracted at 640.

[0069] First, in step 610, an FMCW signal (FIGS. 4A and 4B) is injected into the stator voltage to power a squirrel-cage induction motor. During operation, the stator current is measured as shown in equations (32) and (33). Next, the method analyzes the frequency spectrum of the stator current in step 630, as shown in FIG. 5A for a healthy motor and in FIG. 5B for a faulty motor. The cross-correlation between the stator current spectrum and the injected signal spectrum is calculated according to equation (35), and the results are shown in FIG. 5C for a healthy motor and in FIG. 5D for a faulty motor. The fault signature at frequencies f=±2(1−s)f0 of the cross-correlation function is extracted for further evaluation.

[0070] FIG. 7 is a block diagram illustrating the process steps of a computer-implemented fault signal measurement method (fault detection program 230) for automatically detecting faults in induction motors according to another embodiment of the present invention.

[0071] An FMCW signal is injected into the power supply voltage and the stator current is monitored as shown in equations (32) and (33). In step 730, the frequency spectrum of the stator current (FIG. 5A for a healthy motor and FIG. 5B for a faulty motor) and the frequency spectrum of the injected signal are analyzed. In step 720, the cross-correlation between the injected signal spectrum and the stator current spectrum is calculated, as shown in FIG. 5C for a healthy motor and FIG. 5D for a faulty motor. Next, the method extracts a fault signature at frequencies f=±2(1−s)f0 of the cross-correlation function in step 740. If the magnitude of the fault signature is greater than a threshold in step 710, a broken bar fault is detected in step 770. In other words, the system determines that a broken bar fault has occurred. Otherwise, the system continues to monitor the stator current for analysis in step 750. In some cases, the threshold can be defined around −50 dB compared to the maximum cross-correlation value.

[0072] The above-described embodiments of the present invention can be implemented in any of numerous ways. For example, these embodiments can be implemented using hardware, software, or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether located on one computer or distributed across multiple computers, for example, in a computer cloud. Such multiple processors can be implemented as an integrated circuit, with one or more processors in an integrated circuit component. However, a single processor can be implemented using circuitry in any suitable format.

[0073] Furthermore, it should be understood that the computers may be embodied in any of numerous forms, such as rack-mounted computers, desktop computers, laptop computers, minicomputers, or tablet computers. Such computers may be interconnected by one or more networks of any suitable form, such as a local area network or a wide area network, such as an enterprise network or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol, and may include, for example, wireless networks, wired networks, or fiber optic networks.

[0074] Additionally, embodiments of the present invention may be embodied as a method, an example of which is provided. The order of steps performed as part of this method may be determined in any suitable manner. Thus, embodiments may be configured to perform operations in an order different from that illustrated, including performing some operations simultaneously, even though in the illustrated embodiment they are shown as a sequence of operations.

[0075] While the invention has been described in terms of preferred embodiments, it is to be understood that various other adaptations and modifications can be made within the spirit and scope of the invention. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention.

Claims

1. 1. A computer-implemented fault signal measurement method for detecting broken bar faults during operation of an induction motor powered by a stator voltage, the method using a processor coupled to a memory storing instructions implementing the method, the instructions, when executed by the processor, performing steps of the method, the steps comprising: injecting a frequency modulated continuous wave (FMCW) signal into the induction motor via an interface during operation, the frequency modulated continuous wave (FMCW) signal being modulated such that the frequency modulated continuous wave (FMCW) signal is superimposed on a fundamental frequency of the stator voltage, the method steps further comprising: acquiring, via the interface, a response current signal generated by the induction motor in response to the injected frequency modulated continuous wave (FMCW) signal; performing a spectral analysis of the injected frequency modulated continuous wave (FMCW) signal and the response current of the induction motor; calculating a cross-correlation between a stator current frequency spectrum of the induction motor and a frequency spectrum of the injected frequency modulated continuous wave (FMCW) signal; extracting a characteristic frequency of a broken bar fault occurring in a stator current from the calculated cross-correlation result; determining that a broken bar fault has occurred in the induction motor if the magnitude of the characteristic frequency is greater than a threshold.

2. The method of claim 1 , wherein the frequency modulated continuous wave (FMCW) signal is a modulated frequency continuous sinusoidal signal.

3. The method of claim 1 , wherein the frequency range of the frequency modulated continuous wave (FMCW) signal is configured to be greater than an operating frequency of the induction motor.

4. The method of claim 1 , wherein the induction motor is a squirrel-cage induction motor.

5. The method of claim 1 , further comprising the step of interrupting the supply of the stator voltage if the determining step indicates the occurrence of the broken bar fault.

6. The method of claim 1 , wherein the broken bar fault is modeled by an open circuit corresponding to an equivalent circuit of a rotor in the induction motor.

7. If the equivalent circuit represents n rotor bars, the squirrel-cage rotor can be modelled as n+1 independent current loops, of which n are identical circuit loops in the ideal case, each loop consisting of two adjacent rotor bars connected by two end ring sections, and the remaining circuit loops formed by one of the end rings, so that the rotor current distribution can be modelled in terms of n+1 independent loop currents, i.e., n rotor bar loop currents i ｊ (1≦j≦n) current i of one of the end rings ｅ 7. The method of claim 6, wherein the method is specified in terms of the addition of [Request Item 8] [Number 1] [Request Item 9] [Number 2]

10. 1. A fault detection apparatus for detecting broken bar faults during operation of an induction motor powered by a stator voltage, comprising: an interface connected to a frequency modulated continuous wave (FMCW) generator and configured to cause a controller of the induction motor to add the frequency modulated continuous wave (FMCW) generator to the stator voltage via the FMCW generator; a memory configured to store instructions for a computer-implemented fault signal measurement method for detecting broken bar faults during operation of an induction motor powered by the stator voltage; a processor coupled to the memory, the processor configured to execute the instructions implementing the method, the instructions, when executed by the processor, performing steps of the method, the steps of the method including: injecting a frequency modulated continuous wave (FMCW) signal into the stator voltage of the induction motor via the interface, the frequency modulated continuous wave (FMCW) signal being modulated such that the frequency modulated continuous wave (FMCW) signal is superimposed on a fundamental frequency of the stator voltage, the method steps further comprising: acquiring a response current signal generated by the induction motor in response to the injected frequency modulated continuous wave (FMCW) signal via the interface connected to a sensor disposed within the induction motor; performing a spectral analysis of the injected frequency modulated continuous wave (FMCW) signal and the response current of the induction motor; calculating a cross-correlation between a stator current spectrum of the induction motor and a current spectrum of the injected frequency modulated continuous wave (FMCW) signal; extracting a characteristic frequency of a broken bar fault occurring in a stator current from the calculated cross-correlation result; and determining that a broken bar fault has occurred in the induction motor if the magnitude of the characteristic frequency is greater than a threshold value.

11. The fault detection device of claim 10, wherein the frequency modulated continuous wave (FMCW) signal is a modulated frequency continuous sinusoidal signal.

12. The fault detection device of claim 10 , wherein the frequency range of the frequency modulated continuous wave (FMCW) signal is configured to be greater than an operating frequency of the induction motor.

13. 11. The fault detection device of claim 10, wherein the induction motor is a squirrel-cage induction motor.

14. The fault detection apparatus of claim 10 , further comprising the step of interrupting the supply of the stator voltage if the determining step indicates the occurrence of the broken bar fault.

15. The fault detection apparatus of claim 10 , wherein the broken bar fault is modeled by an open circuit corresponding to an equivalent circuit of a rotor in the induction motor.

16. If the equivalent circuit represents n rotor bars, the squirrel-cage rotor can be modelled as n+1 independent current loops, of which n are identical circuit loops in the ideal case, each loop consisting of two adjacent rotor bars connected by two end ring sections, and the remaining circuit loops formed by one of the end rings, so that the rotor current distribution can be modelled in terms of n+1 independent loop currents, i.e., n rotor bar loop currents i ｊ (1≦j≦n) current i of one of the end rings ｅ 16. The fault detection device of claim 15, wherein the fault detection device is specified in terms of the sum of [Request Item 17] [Number 3] [Request Item 18] [Number 4]

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