This disclosure claims the benefit of UK Patent Application No. 1503930.8, filed on 9 Mar. 2015, which is hereby incorporated herein in its entirety.
This disclosure relates to induction motors, and in particular to methods of fault detection and diagnosis in an induction motor.
Conventional induction motors operate via a multi-phase stator ac winding and a rotor. When in use, an alternating current supplied to the stator winding causes a magnetic field to form. Due to the alternating nature of the current supplied to the stator winding, the magnetic field rotates in time with the oscillations of the current. Relative motion between the magnetic field and the induction motor's rotor induces a current in the rotor, which in turn creates a magnetic field that opposes the change in current through the rotor. The rotor thereby rotates in the same direction as the rotating magnetic field formed by the stator winding. The rotor and the magnetic field rotate at different speeds, and when the ratio between these speeds is high enough a torque is generated which may be used to turn a load.
Induction motors are extensively used in industry to drive a wide variety of machinery, and may also be used to form induction generators, which are useful for applications such as wind turbines. Examples of use of induction motors include use as actuators in aircraft, and use in propulsion motors in marine vessels. Reliable operation of induction motors is therefore vital.
Two of the most common reasons for the occurrence of faults in an induction motor are electrical and thermal stress. If electrical and thermal stress go undetected at an early stage, then they can lead to electrical (stator side) or mechanical (rotor side) failure, which may cause irreversible damage to the induction motor, and may result in increased maintenance costs. It is therefore desirable to avoid such failures, where possible.
Examples of prior systems for fault detection in electric machines are provided by U.S. Pat. No. 8,135,551, U.S. Pat. No. 5,514,978, U.S. Pat. No. 6,640,196 and EP2565658. Such systems generally require bespoke sensing of both three phase current and three phase voltage in order to adequately identify stator winding faults, for example to distinguish such faults from voltage unbalance. However the need for such a bespoke sensing arrangement adds cost and complexity to the machine.
Furthermore certain faults, such as air gap eccentricity faults, require current measurement at a particularly high sampling rate and/or a high resolution harmonic analysis. It has been found to be problematic to identify with certainty fault signatures that may be of particularly small magnitude at higher frequencies.
There has now been devised a method of detecting rotor and/or stator side faults in an induction motor, and a method of detecting and diagnosing rotor and/or stator side faults in an induction motor, which overcome or substantially mitigate the aforementioned and/or other disadvantages associated with the prior art.
According to a first aspect of the present disclosure there is provided a method of detecting rotor and/or stator side faults in an induction motor, the method comprising:
i. acquiring signals indicative of three-phase stator currents of the induction motor;
ii. converting the signals indicative of three-phase stator currents into three-phase current phasors;
iii. combining the three-phase current phasors to obtain a quantity having a DC component and an AC component;
iv. obtaining a frequency spectrum of the AC component; and
v. comparing the obtained frequency spectrum of the AC component to a frequency spectrum of the AC component under healthy operating conditions in order to determine the presence of a rotor and/or stator side fault in the induction motor.
The method of the first aspect of the present disclosure is advantageous as it may allow the detection of rotor and/or stator side faults in an induction motor using only the measured three-phase current. The need for additional sensors, for example voltage sensors, in order to detect rotor and/or stator side faults may be reduced or eliminated, thereby reducing the cost and complexity associated with fault detection in an induction motor.
Furthermore, the disclosure may be beneficial as the combination of the three-phase current phasors may increase the magnitude of the fundamental and harmonic frequencies of the AC component, and hence increase signal to noise ratio (SNR), thereby enabling for more reliable and earlier stage fault detection. In particular, in typical early stage fault detection the magnitudes of any harmonic components are relatively low, which makes it difficult to achieve reliable fault detection based on these harmonic components.
A method according to embodiments of the present disclosure may remove this difficulty by amplifying the magnitude of one or more relevant signal component, for example one or more harmonic component. This may be achieved via the combination of the three-phase current phasors. Such amplification may occur when the magnitude of the current is greater than 1 Amp (1A), which may be particularly suited to industrial electrical machine applications.
The AC component may be substantially zero under healthy operating conditions, and may be substantially non-zero when a fault occurs in the induction motor.
The three-phase current phasors may be combined by calculating the square root of the squared sum of the three-phase current phasors. The combination of the three-phase current phasors in such a manner may be beneficial as it may combine the three-phase current phasors such that the magnitudes of any currents are amplified. This may result in an increased reliability of fault detection. In addition, any offset present in current/voltage sensors will result in appearance of the fundamental frequency component in the AC component. If there is no offset a DC signal can be injected in all/any one of the three phase currents to induce the fundamental frequency component as that of three phase currents in the AC component.
A stator fault may be detected based on spectral features of the obtained frequency spectrum. A stator fault may be detected based on each of a fundamental supply frequency of the AC component or three-phase stator currents and at least one harmonic of the fundamental supply frequency of the AC component or three-phase stator currents. A stator fault may be detected based on a ratio of a magnitude of a fundamental supply frequency of the AC component or three-phase stator currents to a magnitude of a harmonic of the fundamental supply frequency of the AC component or three-phase stator currents. A stator fault may be detected based on a ratio of a magnitude of a harmonic of the fundamental supply frequency of the AC component or three-phase stator currents of the induction motor to a magnitude of a harmonic of the fundamental supply frequency of the AC component or three-phase stator currents of a healthy induction motor. A stator fault may or may not be detected based on a significant and/or sudden increase in said ratio. The harmonic in presently preferred embodiments may be the second harmonic of the fundamental supply frequency of the AC component or three-phase stator currents, or may be any harmonic of the fundamental supply frequency or three-phase stator currents of the AC component, for example up to the tenth harmonic.
A rotor fault may be detected based on spectral features of the obtained frequency spectrum. A rotor fault may be detected based on sideband components of at least one harmonic of a fundamental supply frequency of the AC component present in the obtained frequency spectrum. A rotor fault may be detected based on the presence of sideband components of at least one harmonic of a fundamental supply frequency of the AC component present in the obtained frequency spectrum. A rotor fault may be detected based on the magnitude of sideband components of at least one harmonic of a fundamental supply frequency of the AC component present in the obtained frequency spectrum. In presently preferred embodiments, the sideband components considered may be sidebands of the sixth harmonic of the fundamental supply frequency of the AC component.
The method may comprise determining whether the acquired signals indicative of three-phase stator currents of the induction motor are steady state signals, for example prior to combining the three-phase current phasors. This may be advantageous as it may eliminate and/or mitigate false fault detections based on fluctuations or transients present in the three-phase stator currents.
The signals indicative of three-phase stator currents may be filtered prior to conversion into three-phase current phasors and/or prior to determining whether the signals indicative of three-phase stator currents are steady state signals. The signals indicative of three-phase stator currents may be filtered using a digital low pass filter.
Determination of steady state signals may be based on frequency and magnitude variation analysis.
The frequency spectrum of the AC component may be obtained by a harmonic analysis, such as a Fourier transform/analysis. A discrete Fourier Transform (Fast Fourier transform) or other spectrum analysis techniques may be used.
The frequency spectrum of the AC component considered may be limited by a maximum frequency. The maximum frequency may be a harmonic of the fundamental supply frequency of the AC component. In presently preferred embodiments the frequency spectrum of the AC component may be considered up to the tenth harmonic of the fundamental supply frequency of the AC component.
The signals indicative of three-phase stator currents may be converted into three-phase current phasors by any conventional means, including, for example, using standard phasor transformations.
The method may comprise diagnosing the type of rotor and/or stator side fault.
Diagnosis of the type of stator side fault may be based on the magnitude of a fundamental supply frequency of the induction motor and the magnitude of at least one harmonic of the fundamental supply frequency of the induction motor, for each phase. Diagnosis of the type of stator fault may be based on a ratio of magnitude of a fundamental supply frequency of the induction motor to the square root of the sum of the magnitudes of harmonics of the fundamental supply frequency of the induction motor, for each phase.
Diagnosis of the type of stator fault may be based upon the variance between the ratios of the magnitude of the fundamental supply frequency of the induction motor to the square root of the sum of the magnitudes of harmonics of the fundamental supply frequency of the induction motor for each phase.
A stator fault may be diagnosed as a voltage unbalance fault when the variance of the ratio of the magnitude of the fundamental supply frequency to the square root of the sum of the magnitudes of harmonics of the fundamental supply frequency of the induction motor is less than the threshold value calculated from the healthy dataset of a corresponding working condition of the induction motor.
A stator fault may be diagnosed as a stator winding fault when the variance of the ratio of the magnitude of the fundamental supply frequency to the square root of the sum of the magnitudes of harmonics of the fundamental supply frequency of the induction motor is greater than the threshold value calculated from the healthy dataset of a corresponding working condition of the induction motor. The threshold value is the minimum value of the ratio of the magnitude of the fundamental supply frequency to the square root of the sum of the magnitudes of harmonics of the fundamental supply frequency of the healthy induction motor of each phase
In presently preferred embodiments, the harmonics of the fundamental supply frequency of the induction motor that may be considered are harmonics up to and including the seventh order harmonic.
Diagnosis of the type of rotor fault may be based on sideband components of harmonics of the fundamental supply frequency of the AC component. Diagnosis of the type of rotor fault may be based on the magnitude of sideband components of harmonics of the fundamental supply frequency of the AC component. Diagnosis of the type of rotor fault may be based on the power distribution of sidebands around a selected harmonic.
A rotor fault may be diagnosed as a broken rotor bar fault when the power distribution is below a specified threshold value. For example under rated load condition the power distribution will be less than 1. A rotor fault may be diagnosed as an airgap eccentricity fault when the power distribution is above a specified threshold value. For example under rated load condition the power distribution will be greater than 1.The specified threshold value may vary between induction motors, and may be specified based on the healthy operating conditions of the induction motor.
Any of the preferential features of the first aspect of the present disclosure may equally be applied to any of the further aspects of the present disclosure, wherever practicable.
According to a second aspect of the present disclosure there is provided a method of detecting or diagnosing rotor and/or stator side faults in an induction motor, the method comprising:
i. acquiring signals indicative of three-phase stator currents of the induction motor;
ii. converting the signals indicative of three-phase stator currents into three-phase current phasors;
iii. combining the three-phase current phasors to obtain a quantity having a DC component and an AC component;
iv. obtaining a frequency spectrum of the AC component;
v. converting each of the acquired signals indicative of three-phase stator currents of the induction motor into three-phase stator current frequency spectra; and
vi. comparing the obtained three-phase stator current frequency spectra and/or the frequency spectrum of the AC component to spectra of the induction motor under healthy operating conditions in order to detect or diagnose the type of rotor and/or stator side fault.
The method according to the second aspect of the present disclosure may be advantageous in that it may allow rotor and/or stator side faults to be detected or diagnosed solely through the use of the measured three-phase current. Thus the need for additional sensors, for example voltage sensors, in order to detect and diagnose rotor and/or stator side faults may be eliminated, thereby reducing the cost and complexity associated with fault detection in an induction motor.
Furthermore, the method according to the second aspect of the present disclosure may provide the benefit of allowing for detection of healthy operating conditions at an early stage of the fault detection and diagnosis procedure. Thus healthy operation can be detected at an early stage, eliminating and/or mitigating the need for the entire fault detection and diagnosis procedure to be carried out in order to determine healthy operation. This may reduce the computational power required to determine healthy operation, and may decrease the time interval between successive iterations of the fault detection and diagnosis procedure during healthy operation, thereby resulting in an increased chance of detecting a fault at an early stage.
The signals indicative of three-phase stator currents of the induction motor may be converted into three-phase stator current frequency spectra using a Discrete Fourier Transform.
According to a third aspect of the present disclosure, there is provided a fault detector for an induction motor having one or more sensors thereon capable of acquiring signals indicative of three-phase stator currents of the induction motor, the detector comprising;
i. a receiver for receiving signals indicative of three-phase stator currents of the induction motor;
ii. a signal convertor for converting the signals indicative of three-phase stator currents into three-phase current phasors and combining the three-phase current phasors to obtain a quantity having a DC component and an AC component to obtain a frequency spectrum of the AC component; and
iii. one or more processor arranged to compare the obtained frequency spectrum of the AC component to a frequency spectrum of the AC component under healthy operating conditions in order to determine the presence of a rotor and/or stator side fault in the induction motor.
According to a further aspect of the disclosure, there is provided a data carrier comprising machine readable instructions for the control of one or more processors of an induction motor fault detector to:
i. receive signals indicative of three-phase stator currents of the induction motor;
ii. convert the signals indicative of three-phase stator currents into three-phase current phasors;
iii. combine the three-phase current phasors to obtain a quantity having a DC component and an AC component;
iv. obtain a frequency spectrum of the AC component; and
v. compare the obtained frequency spectrum of the AC component to a frequency spectrum of the AC component under healthy operating conditions in order to determine the presence of a rotor and/or stator side fault in the induction motor.
The type of fault and the severity of the fault can be notified to the personnel. Depending on the severity of the fault the maintenance can be scheduled to avoid the catastrophic failure.
Preferred embodiments of the present disclosure will now be described in detail, by way of illustration only, with reference to the accompanying figures, of which:
A method of detecting rotor and/or stator faults in an induction motor according to a first aspect of the present disclosure is described with reference to
The method makes use of the Instantaneous Space Phase Square (|ISP|2) of the stator current. The |ISP|2 of stator current is calculated by converting the stator phase currents into phasors and then obtaining the square root for the squared sum of the three phase current phasors. The procedure for obtaining the |ISP|2 is given below, and utilises the stator phase currents P1, P2, and P3, for phases a, b, and c, respectively.
P
1
=I
asin(ωt−φ1)*(cos(0)+jsin(0)) (1)
P
2
=I
bsin(ωt−φ2)*(cos(2π/3)+jsin(2π/3)) (2)
P
3
=I
csin(ωt−φ3)*(cos(−2π/3)+jsin(−2π/3)) (3)
|ISP|2=(√(real(P1+P2+P3)2+img(P1+P2P3)2))2 (4)
The |ISP|2 has both DC and AC components, which are given by the following equations respectively:
DC=½[Ia2+Ib2Ic2]+½[IaIbcos(φ2+φ1)+IaIccos(φ3−φ1)+IbIccos(φ2−φ3) (5)
AC=−½[Ia2cos(2ωt−2φ1)+Ib2cos(2ωt−2φ2)+Ic2cos(2ωt−2φ3)]−½[IaIbcos(2ωt−(φ1−φ1))+IaIccos(2φt−(φ1+φ3))+IbIccos(2ωt−(φ2+φ3))] (6)
Under healthy operation conditions, each of the three phase currents are equal in magnitude, with phase shifts of 0°, −120°, and −240°, for phases a, b, and c, respectively. Thus under healthy operation conditions, Ia=Ib=Ic=I, and φ1=0°, φ232 −120°, and φ3=−240°. The DC and AC components of the |ISP|2 under healthy operation conditions are thus given by:
DC=1.5*I (7)
AC=0 (8)
In general, even under healthy operation conditions of the motor, there will be a small (0.1 to 1% of fundamental component of maximum phase current) AC component because of small unbalances in the stator current due to inherent asymmetries introduced during machine manufacturing. However the magnitude of this AC component will be much less than any AC component introduced due to faults or supply voltage unbalance in the machine.
When there is a stator fault condition, or there is an unbalanced power supply, the magnitude of the AC component of the |ISP|2, and its harmonic components, will vary significantly (greater than 1% of fundamental component of maximum phase current) from the zero value of healthy operating conditions. This variation is dependent both on the type of the fault, and its severity, and thus allows a fault to be both detected and diagnosed.
Asymmetry in the three phase stator current magnitude/phase angle increases the magnitude of the spectral component of the |ISP|2 AC component at twice the fundamental supply frequency of the |ISP|2 AC component. Hence the ratio of fs to 2fs is used to detect stator anomalies, where fs is the fundamental supply frequency of the AC component. The magnitude of the 2fs component in the AC component of the |ISP|2 of stator current occurs primarily because of unequal current magnitudes between the phases, which reduce the ratio under stator side fault compared to that under healthy operating conditions. In order to detect stator faults, the threshold value for the ratio of fs to 2fs is set based on the ratio under healthy operation. The threshold value will be usually 1 for a healthy induction machine. If a stator fault is detected, then a fault indicator is set.
If there are any rotor side anomalies present in the motor, then the ratio of fs to 2fs is largely unchanged from the ratio under healthy operating conditions. Thus, in order to detect rotor side anomalies, frequency sidebands around the 6fs AC component of the |ISP|2 are analysed, and compared to a threshold value that is set based on healthy operating conditions of the motor. The threshold value is typically 10% of the magnitude of the 6fs AC component of the |ISP|2. If there are sidebands with a magnitude that is greater than the threshold value, then a rotor side fault is present, and a fault indicator is set.
The method of the first aspect of the present disclosure initially involves the step of acquiring values 10 for the three phase current signal, and the speed of the motor, using a data acquisition system. The maximum frequency for fault signature detection and analysis is set as 10 times the fundamental frequency of stator current signal. Hence, the current signals are sampled at a frequency of 10 times the fundamental frequency of the phase current. Increasing the sampling frequency will increase the computational time. At specified periods of time, typically n seconds (ranges from 1 s to few seconds) the data is acquired, and then stored in a buffer to convert the samples into a vector to obtain the frequency spectrum using a Discrete Fourier Transform (DFT) The value of n depends on the required frequency resolution and the value should be greater than 5 for fault signature detection at low loads. From the acquired data, any one of the phase currents is selected, and is smoothed using a digital low pass filter 12. The smoothed current is then used to detect transients in the machine by analysing the variation in the frequency and magnitude, so as to confirm that the acquired signals are a steady state signal 14. If any transient is detected, then the fault detection and diagnosis process is bypassed and the data is acquired once again. If there is no transient, then the signal enters the first stage of fault detection and diagnosis (FDD).
The first stage of FDD involves the computation of the |ISP|2 value 16, as previously discussed. The slip of the induction motor is also calculated using conventional methods. The frequency spectrum of the AC component of the |ISP|2 is obtained, and the ratio of fs to 2fs is calculated 18. This allows any stator side faults to be detected and diagnosed by comparison with the ratio under healthy operating conditions 20. If no stator side faults are present, then the harmonic sidebands around the 6fs component of the AC component of the |ISP|2 spectrum are analysed 22, and compared to threshold values based on healthy operating conditions of the induction motor. If a rotor side fault is found, then a fault indicator is set 24.
If there are no stator or rotor anomalies found, then the machine is diagnosed as healthy, and the process is repeated using newly acquired data.
Examples of frequency spectra of the AC component of the |ISP|2 are shown in
A method of detecting and diagnosing rotor and/or stator faults in an induction motor according to a second aspect of the present disclosure is described with reference to
In brief, the method according to the second aspect of the present disclosure utilises the method of the first aspect of the present disclosure to detect whether a stator or rotor side fault is present in the induction motor, and then utilises various techniques to diagnose the type of fault that is present. These techniques are discussed in more detail below.
To diagnose the type of stator side fault that is present, the Inverse Total Harmonic Distortion (ITHD) is calculated, as will now be discussed.
Firstly, the three phase currents of the motor are measured using current sensors and are then converted using a Discrete Fourier Transform to determine the relevant frequencies. A threshold based peak detection algorithm is used to detect any harmonic peaks which are greater than 1% of the magnitude of the fundamental frequency. The threshold 1% is heuristic, the percentage value can be fixed and it can be varied until harmonics are detected (typically it should be greater than 0.5%). The detected harmonic magnitudes are used to calculate the ITHD value for all three phases using the equation below;
where If1 is the magnitude of the fundamental frequency, and Ifn is the magnitude of the nth harmonic.
In order to diagnose the type of stator side fault present, the ITHD values are computed for each phase, namely phases a, b, and c, and the variance of the ITHD values of the phases are calculated. In presently preferred embodiments, the ITHD values are computed by considering harmonic components up to the seventh order. Where the variance is less than 150% of the minimum ITHD value of all phases of a healthy induction motor, the fault is characterised as a voltage unbalance fault. Furthermore, if the variance is greater than 150% of the minimum ITHD value of all phases of a healthy induction motor, the fault is characterised as a stator winding fault. This occurs as a stator winding fault in one phase causes the stator current of that phase to decease, whilst causing the stator currents of the remaining two phases to increase. Furthermore, it is observed that the variance of the ITHD values increases with the severity of the fault for stator winding faults.
Hence the type of stator side fault present in an induction motor can be characterised.
Examples of the ITHD values of an induction motor operating under healthy and faulty conditions are shown in Table 1 below.
Examples of the Discrete Fourier Transforms of the three-phase currents for an induction motor operating at a slip of 0.035, having a stator winding fault and a voltage unbalance fault are shown in
To diagnose the type of rotor side fault that is present, the New Characteristic Equation (NCE) is used, in combination with a comparison between the power distributions of harmonics. The NCE is disclosed in pending UK patent application, GB1404226.1 which is hereby incorporated by reference.
In particular, the NCE is given by:
f
brb×((1−s)fh±ksfs) (10),
where k=1, 2, 3 . . . is the nth order harmonic, fs is the fundamental frequency, and fh=fs, 2fs, 3fs, etc.
In order to utilise the values obtained by the NCE, the frequency spectrum is mapped to the |ISP|2 spectrum using the following FFT:
The power distributions of the sidebands around a selected harmonic are given by:
Where the power distribution is low (<1), the fault is a broken rotor bar fault, and the power distribution is characterized by sidebands of significant magnitudes, as well as having more than two sidebands. Eccentricity faults are characterised by higher power distribution (>1) as the sidebands magnitude are low (less than 20% of the harmonic magnitude), and lower numbers of sidebands, than those present when the fault is a broken rotor bar fault.
Where neither a broken rotor bar fault, nor an air gap eccentricity fault, are diagnosed, conventional bearing fault frequency equations are utilised to diagnose a bearing fault. The conventional bearing fault frequency equations are shown below:
f
bf
=|f
0
±mf
v| (12)
where fbf is the bearing failure frequency, f0 is the supply frequency, m is a positive integer, and fv is one of the characteristic vibration frequencies due to bearing fault which are given by
f
race=(n/2)fr(1+(bd/pd)cosβ) (13)
f
ball=(bd/(2pd))fr(1−((bd/pd)cosβ)2) (14)
where, n is the number of bearing balls, the broken rotor bar frequency fr is the mechanical rotor speed in Hz, bd is the ball diameter, pd is the bearing pitch diameter, and β is the contact angle of the balls on the races. The data obtained from these equations is compared to data of the machine operating under healthy conditions, in order to diagnose a bearing fault.
The method of the second aspect of the present disclosure utilises the method of the first aspect of the present disclosure to detect if a stator or rotor side fault is present.
Where a stator side fault has been detected, ITHD values are computed for the three phase current, and the variance of the ITHD values for the three phase current are measured 26, as discussed previously. The stator side fault is either characterised as a stator winding fault, or a supply voltage unbalance fault 28, and is notified to a user.
Where a rotor side fault has been detected, harmonic components having a value greater than a threshold value, typically 1% of the magnitude of the fundamental, are identified using a peak detection algorithm 30. The NCE is used to obtain sideband components around selected harmonics, and then a DFT is used to map the frequencies to the |ISP|2 spectrum 32. Relevant sideband components are considered, and the power distribution is calculated 34 and compared to that of the induction motor under healthy operating conditions 36.
Where the power distribution is low, the fault is a broken rotor bar fault, and the power distribution is characterized by sidebands of significant magnitudes, as well as having more than two sidebands in total. Eccentricity faults are characterised by sidebands of an even lower magnitude, and lower numbers of sidebands, than those present when the fault is a broken rotor bar fault. Where either a broken rotor bar fault, or an air gap eccentricity fault, is diagnosed, a user is notified 38.
Where neither a broken rotor bar fault, nor an air gap eccentricity fault, is diagnosed, conventional methods are used to diagnose a bearing fault 40, as previously discussed.
Thus the method according to the second aspect of the present disclosure allows for the detection and diagnosis of both stator side faults and rotor side faults in an induction motor.
Alternative methods of detecting and diagnosing rotor and/or stator faults in an induction motor are shown in flow diagram form in
The method of
The method of
Number | Date | Country | Kind |
---|---|---|---|
1503930.8 | Mar 2015 | GB | national |