BROKEN BAR DETECTION IN INDUCTION MOTORS USING ...

BROKEN BAR DETECTION IN INDUCTION MOTORS USING CURRENT AND FLUX SPECTRAL ANALYSIS J. Siau, A. Graff, W. Soong and N. Ertugrul University of Adelaide Abstract Spectrum analysis of the stator current is commonly used for detecting broken rotor bar faults in induction motors. In this paper we examine the practicality of estimating the number of broken bars using stator current and axial leakage flux measurements. The work is based on experimental current and flux measurements taken from induction motors as the number of broken rotor bars are progressively increased. The test results are compared with theoretical predictions and empirical results from other researchers. The effect of partial load operation is also examined. 1

leakage flux measurements. It is proposed to estimate the fault severity using thresholds.

INTRODUCTION

1.1 On-Line Condition Monitoring Induction motors are widely used in industry. They are generally reliable but like all electric motors require maintenance. Condition monitoring provides a means for assessing the health of an induction motor. This can be used to minimize the likelihood of unexpected failures and also to reduce maintenance costs. On-line condition monitoring allows the detection of faults in induction motors while they are operating. Faults in induction motors affect the symmetry of the machine and hence produce characteristic fault frequencies in a variety of sensor signals. On-line condition monitoring involves : taking measurements of signals from sensors; processing these signals to extract features which are sensitive to the presence of faults; and then examining the features to determine whether a fault exists. Fig. 1 shows a block diagram summarising the common motor faults, sensor types, signal processing techniques and fault detection algorithms which have been applied to on-line condition monitoring [1]. laboratory testing with known faults and previous field experience

Motor Faults • bearings • stator winding • rotor bar • eccentricity

Sensor Signals • vibration • current • magnetic flux • voltage

Signal Processing • RMS • Fourier transform • time-frequency • wavelet • higher order stats • Park’s vector • negative seq.

Expert Knowledge

Fault Detection • model-based • trending • threshold • multi-dimension • neural networks • fuzzy logic • expert systems

Fig. 1. Block diagram summarising on-line condition monitoring techniques [1]. The underlined areas are examined in this paper. This paper examines the detection of broken bar faults using frequency analysis of the current and axial

1.2 Significance of Broken Bar Faults Broken rotor bars constitute about 5-10% of induction motor faults [2]. They have received considerable attention in the literature [1-9], perhaps due to their well-defined associated fault frequency components. The rotor of an induction machine has bars which run axially through the machine and are joined to the endrings at both ends. Over time, these bars can crack and break due to thermal or mechanical cycling, or manufacturing defects. A study based on finite-element analysis [3] has shown that broken rotor bars reduce the motor output torque capability, increase losses and increase vibration of the machine. The study also showed that a broken bar causes the currents in neighbouring bars to increase significantly. This can cause these bars to overheat and break, resulting in the fault increasing in severity with time. It is possible with severe rotor faults that parts of the rotor bars or end-rings can break off and damage the stator windings, resulting in catastrophic failure. 1.3 Detecting Broken Bar Faults Broken rotor bars cause an asymmetry in the rotor of the machine. It is known from generalised rotating field theory that an electric or magnetic asymmetry in the rotor of induction machines cause a frequency component in the stator current at (1-2s)f for a supply frequency f and a motor slip s [4]. This (1-2s)f current component causes a torque component with a frequency of 2sf, which generally produces a speed fluctuation at the same frequency. The 2sf rotor speed fluctuation interacts with the supply current to produce another frequency component at (1+2s)f. This (1+2s)f component can also be produced by magnetic saturation in the machine.

Current Spectrum (dB)

0 -20 -40

1-2s (left)

1+2s (right)

healthy

-60

Amplitude of Sidebands (dB)

Generally, it is assumed that these (1±2s)f broken bar current sidebands are only caused by rotor asymmetry and the subsequent speed variation [4]. Note that due to intrinsic manufacturing variations even "healthy" machines have some degree of asymmetry. Fig. 2 shows a measured current spectrum for a motor with one broken bar. The spectrum has been normalised to the fundamental 50Hz component and the two broken bar sidebands are clearly visible. The sideband levels before the fault was introduced are also indicated, showing the change produced by the fault.

-10 Pred 1 Pred 3 Pred 2

-20 -30 -40 -50 -60 0

2

3

Prediction 2 Benbouzid [6] describes an alternative relationship :

healthy

-100

48

50 52 Frequency (Hz)

5

Fig. 3. Theoretical predictions of the variation in the broken bar sidebands with increasing numbers of broken bars for a 4 pole motor with 32 rotor bars.

sin α I BB = 2 p(2π − α ) I

46

4

Number of Broken Rotor Bars

-80

-120 44

1

54

(2)

where p is the number of pole pairs and α is given 2πpn by α = . N

56

Fig. 2. Measured current spectrum around the 50Hz peak for a motor with one broken rotor bar showing the (1±2s)f sidebands. The (1±2s)f sidebands also appear in other sensor signals such as the axial leakage flux of the machine. The use of the axial leakage flux has received much less attention than stator current but has shown some promising results [5].

Benbouzid's prediction has a similar trend to Bellini's prediction except it about 5-8 dB lower. Prediction 3 Thomson [7] proposes the relationship :

I BB n = I 2 N − np

(3)

In this paper we are seeking to better understand whether a quantitative estimate of the number of broken bars can be made from stator current and axial leakage flux measurements.

Thomson's prediction has a very similar shape to Benbouzid's prediction with only a small deviation at higher numbers of broken bars. Due to this similarity only Predictions 1 and 2 will be used in the remainder of this paper.

2

2.2 Case Studies

BROKEN BAR THEORY

2.1 Theoretical Predictions Researchers have made predictions for the variation in the broken bar sideband amplitudes in the current spectrum with the number of broken bars. These sidebands are usually normalised against the amplitude of the fundamental peak and expressed in dB. Prediction 1 Bellini et al. [4] proposed that the relationship given in Eqn. 1 (see Fig. 3) holds for the breakage of n contiguous rotor bars.

I BB n = I N

(1)

where IBB is the amplitude of the (1-2s)f broken bar component, I is the amplitude of the fundamental supply current and N is the total number of rotor bars.

Apart from these three predictions, there are other magnitude threshold values deduced from various case studies, which can be used to distinguish a healthy motor from those with possible broken rotor bar faults. Case Study 1 Benbouzid [6] stated that a (1-2s)f current component with a magnitude of -50dB or less typically indicates a healthy rotor. Case Study 2 A case study with a 3.6MW induction machine showed that broken bar sidebands of less than -45dB correspond to a healthy motor [8]. 54-45 Rule A 54-45 Rule has been proposed which defines the magnitude of the sidebands to be less than -54dB for a healthy machine, and greater than -45dB for a motor

with broken rotor bars [9]. Sideband amplitudes between -45dB and -54dB are considered marginal.

DC machine. The DC machine was loaded using a variable resistance bank.

2.3 Partial Broken Bars The three predictions given above rely on changes in the current distribution in the rotor due to the bar breakages. However, for a partial rotor bar breakage, the change in the bar resistance and hence the change in the rotor current distribution may be small. For instance, consider a bar with a narrow crack which locally reduces the bar cross-sectional area to half. The resistance of this cracked zone will increase the total bar resistance by an amount proportional to the crack length and inversely proportional to the remaining conducting area. Although the area is halved, the length of the crack is short compared with the bar length and so the overall resistance change is small. Based on this, it may be difficult to detect partial broken bar faults. 3 EXPERIMENTAL ARRANGMENT This section describes the experimental arrangement used to collect the current and flux measurements on healthy and faulty machines.

Fig. 4. Photograph of experimental test arrangement showing data-acquisition system (left), dynamometer (under bench) and load bank (right). Auto Transformer

Separately Excited DC Machine Test Motor

I

3-phase 415 V 50 Hz

V

Flux Coil Current Probe

DAQ System

Gear Box

I V

Shaft Variable Resistor Bank

Fig. 5. Diagram of experimental test arrangement.

3.1 Test Motors

3.3 Sensor Measurement and Frequency Analysis

Three identical commercial 2.2kW, 50Hz, 4 pole induction motors were used in the testing. These were chosen to be the largest motors which could be conveniently tested using the available dynamometer equipment. The motors were star-connected and rated at 415V, 4.8A. They used a standard cast aluminium rotor and had 32 rotor slots.

Measurements of the input stator current were taken between the auto-transformer and the test motor using a clip-on Hall-effect current probe. The machine axial leakage flux was measured with a circular search coil of comparable diameter to the motor. This search coil was mounted concentrically with the shaft on the rear of the motor.

The locations of the rotor bars were determined from the tops of the rotor slot openings. Faults were introduced by breaking the bars using a fine milling cutter to cut slots in the end-rings to different depths to either partially or fully break the bars. The slots in the end-rings were made immediately adjacent to the lamination stack and were wide enough to ensure that the sides of the rotor bar were completely removed.

As the frequencies of interest were around the 50Hz fundamental signal, a sampling rate of 400Hz was chosen. This gives a Nyquist frequency of 200Hz.

The rated speed of the machine was 1435rpm (a slip of 4.3%), resulting in expected broken bar frequencies at full-load of 45.67Hz for the (1-2s)f and 54.33Hz for the (1+2s)f component.

Three types of filter were considered : Bessel, Butterworth and Chebyshev. Based on an examination of their frequency response characteristics, an 8th order Butterworth filter was chosen [10]. Due to the high order of this filter, the component tolerances can affect the frequency response. A Monte Carlo sensitivity analysis was performed, and based on this, 1% tolerance capacitors and resistors were chosen.

3.2 Dynamometer Arrangement The experimental work for this project was conducted using the test rig shown in the photograph in Fig. 4 and the block diagram in Fig. 5. The motor under test was connected to the three-phase, 415V, 50Hz AC supply mains through an auto-transformer. The test motor was mechanically coupled via a belt drive to a

Due to this relatively low sampling rate, an antialiasing low-pass filter was required in order to prevent aliasing of the higher frequency signal components. A cut-off frequency of about 100Hz was chosen for the low-pass filter.

The total sampling time T determines the resulting frequency resolution ∆f = 1/T. A value of 100s was chosen to give a frequency resolution of 0.01Hz. This resulted in a total record length of 40,000 points. This

The slip frequency sf signal in the flux spectrum (see Sect. 5.2) was used to accurately determine the actual slip and hence determine the locations of the (1±2s)f sidebands. 3.4 Motor Testing Three motors were used during the testing. Broken bar tests were performed at full and half-load on Motors 1 and 3. Motor 1 was tested with 0, 0.5, 1, 2, 3, 3.5 and 4 broken bars. The results from this showed a significant change in the sidebands around 1 broken bar. Hence Motor 3 was tested with 0, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5 and 4 broken bars. Motor 2 was only tested in the healthy condition. 4

CURRENT SPECTRUM TESTING

4.1 Healthy Motor Measurements (Current) Current spectrum measurements of the (1-2s)f sidebands were taken on the motors in the healthy state. When Motor 1 was tested at full-load it was found that there was a 3dB variation in the (1-2s)f sidebands between its three phase currents. This variation increased to 6dB at half-load. To avoid this variation, it is necessary to take measurements from a consistent motor phase for comparisons or else to take an average of all three phases. Based on experiments conducted on the same phase of Motors 1, 2 and 3, the (1-2s)f sideband magnitude was found to vary from –62dB to –54dB at full-load. This was probably due to manufacturing asymmetries. 4.2 Current Spectrum Broken Bar Results Fig. 6 shows the measured broken bar sidebands for Motors 1 and 3 at full-load versus the number of broken bars. The general trends for integral numbers of broken rotor bars observed from the (1±2s)f measurements are consistent with Predictions 1 and 2. However, for a given number of broken bars, both predictions tended to over-estimate the (1-2s)f sideband amplitudes with offsets of up to 10 dB for Prediction 1 and up to 5 dB for Prediction 2. Prediction 2 thus provides the best severity estimation for predicting integral bar breakages for these motors. The (1+2s)f sidebands

-20

Amplitude of Sidebands (dB)

A 12 bit, simultaneous sampling, data-acquisition system was used to record the current and flux signals. The signals were passed through a Hanning window before being analysed using the standard Fourier transform to obtain the frequency spectra. Fig. 2 shows an example of a typical result.

follow the same trends as the (1-2s)f sidebands but are about 5dB lower on average. Pred 1

Pred 2

C1&3: 1-2s C1&3: 1+2s

-30 -40

"C"=current "1&3"=Motors 1 and 3

-50 -60 -70 0

1

2

3

4

5

Number of Broken Rotor Bars

Fig. 6. Current spectrum, comparison of measured full-load sidebands for Motors 1 and 3. The curves for Predictions 1 and 2 are also shown. Fig. 6 also shows that starting from a healthy motor, the broken bar sidebands do not increase significantly until one bar is fully broken. This could be due to the effect discussed in Sect. 2.3. Thus it is difficult to use the current spectrum to detect faults of consisting of less than one broken bar. The sharp change at one broken bar also suggests that a healthy/faulty threshold of –45dB for the (1-2s)f sideband could be applied to these motors. It is interesting to observe that partial bar breakages beyond one broken bar (e.g. going from 2 to 2.5 broken bars) usually causes the sideband magnitudes to decrease. This does not match the monotonic trend expected from Predictions 1 and 2. This effect makes it more difficult to estimate the exact number of broken bars. Fig. 7 compares half-load versus full-load operation of Motor 3. Reducing the load causes both sideband amplitudes to fall. The (1-2s)f sideband shows a fall of 6dB on average but up to 10dB maximum. However, the (1+2s)f sideband shows much less change, with less than 1dB drop on average. This may make the (1+2s)f sideband more useful for fault detection under partial load operation. Amplitude of Sidebands (dB)

is probably greater than what would normally be required in practice.

-20 Pred 1

Pred 2

C3: 1-2s: full

-30

C3: 1+2s: full C3: 1+2s: half C3: 1-2s: half

-40

-50

-60 0

1

2

3

4

5

Number of Broken Rotor Bars

Fig. 7. Current spectrum, comparison of measured full and half-load sidebands for Motor 3.

The (1±2s)f sidebands are also present in the flux spectrum and this section investigates their use for broken bar detection. 5.1 Healthy Motor Measurements (Flux) The positioning of the flux coil when conducting tests was found to affect the flux spectra. Moving the flux coil by about 10cm away from the motor in an axial direction caused the amplitude of the (1±2s)f flux sidebands to increase by about 5dB relative to the 50Hz flux component. Placing the flux coil at an angle relative to the motor caused these sidebands to decrease relative to the 50Hz component. Therefore, a fixed position for the flux coil was chosen to ensure consistency of results. Measurements taken on the three motors under fullload conditions showed a maximum variation of less than 4dB for the (1±2s)f components. However under half-load conditions this variation increased up to 10dB. Thus, full-load conditions are preferred over half load for minimal variation. From the full-load test results of Motors 1 and 3, it was found that the (1-2s)f component varied between – 35dB and –37dB and the (1+2s)f component varied between –40dB and –50dB. 5.2 Flux Spectrum Slip Measurement The graphs in Fig. 8 show the use of the flux spectrum to accurately measure the motor slip using the slip frequency peak sf. Note that the slip is roughly proportional to load. The slip frequency peak is clearly evident as the largest low frequency peak. This technique appears to work well even for light loading conditions. However, at very low loads, the slip peak can merge with the DC component, requiring a higher sampling resolution to distinguish it. This is achievable with a longer sampling period. 20% Load -20 -40 -60 -80

-100 -120 0

sf

-20 -40 -60 -80

-100 1

2 3 Frequency (Hz)

4

5

-120

0

1

2 3 Frequency (Hz)

4

5

Fig. 8. Slip frequency detection in flux spectrum at two loading conditions. 5.3 Flux Spectrum Broken Bar Results Fig. 9 shows the flux spectrum results at full-load for Motors 1 and 3. The predictions strictly only apply to the current spectrum however they have also been shown for reference.

-10

F1&3: 1-2s

-20

F1&3: 1+2s Pred 1 Pred 2

-30 -40 -50 -60 0

1

2

3

4

5

Number of Broken Rotor Bars

Fig. 9. Flux spectrum, comparison of measured fullload sidebands for Motors 1 and 3. The flux spectrum shows many similar characteristics to the current spectrum : the sidebands shows a monotonic increasing trend for integral numbers of broken rotor bars; the (1+2s)f component follows the same trend as the (1-2s)f component and is about 5 to 10dB lower; partial rotor bar breakages can result in the sideband amplitudes reducing; and a significant increase is only observed when the first rotor bar is completely broken. The main differences are that the sideband amplitudes in the flux spectrum are higher overall, and there is a smaller change going from the healthy state to one broken bar (about 10dB versus about 20dB for the current spectrum). This makes it more difficult to set a threshold between faulty and healthy motors. Fig. 10 shows that under half-load operation, the rotor bar flux sidebands reduce substantially and show dramatic variations with partial rotor bar breakages, for instance at 2.5 broken bars. It is thus difficult to use the flux signal under partial load operation to determine fault severity without further investigation. The use of other frequency components in the flux spectrum for rotor fault detection was examined. The most useful signal found was the magnitude of the slip frequency component sf which shows similar trends to the (1±2s)f signals (see Fig. 11).

80% Load 0

sf Magnitude (dB)

Magnitude (dB)

0

Amplitude of Sidebands (dB)

FLUX SPECTRUM TESTING

Amplitude of Sidebands (dB)

5

-10

F3: 1-2s: full

-20

F3: 1+2s: full Pred 1 Pred 2

-30 -40 -50

F3: 1+2s: half

F3: 1-2s: half

-60 0

1

2

3

4

5

Number of Broken Rotor Bars

Fig. 10. Flux spectrum, comparison of measured full and half load sidebands for Motor 3.

7 ACKNOWLEDGMENT This work was supported by a 2003 University of Adelaide Small Research Grant.

Normalised Amplitude (dB)

0 -10

F1: sf F3: sf

-20

Pred 1 Pred 2

-30 -40 -50 -60 0

1

2

3

4

5

Number of Broken Rotor Bars

Fig. 11. Flux spectrum, measured full-load slip frequency component (sf) for Motors 1 and 3. 6 CONCLUSIONS The aim of this paper was to examine means for estimating the number of broken rotor bars in an induction motor using current and axial leakage flux measurements. The following are the key results : •

At full-load, for integral numbers of broken rotor bars, the (1-2s)f current component follows the monotonically increasing prediction given by Benbouzid. The (1+2s)f component showed a similar trend but was about 5dB lower.

•

A significant change in sideband amplitudes was only found when at least one complete bar breakage occurs. It is thus difficult to detect broken bar faults consisting only of a partially broken bar. It was also found that partial broken bars beyond one broken bar (e.g. going from 2 to 2.5 broken bars) can cause the sidebands to decrease.

•

At full-load, the (1±2s)f flux sidebands showed similar trends to the current. Under these conditions, the flux signal may provide a useful aid to the current signal in estimating fault severity.

•

It was found that the current sidebands, (particularly the (1+2s)f sideband) were much less sensitive than the flux sidebands to partial load operation.

In conclusion, the current spectrum offers a more accurate means than the flux spectrum for detecting broken rotor bar faults. However, it is still difficult to detect partial rotor bar breakages or make accurate estimates of the number of broken rotor bars using the current spectrum alone. Further work is planned to combine the information from the current and flux signals to try to improve the accuracy of rotor fault detection and severity estimation.

Technical support from the School of Electrical and Electronic Engineering's mechanical workshop in the construction of the dynamometer equipment and during the experimental testing is gratefully acknowledged. 8 REFERENCES [1] M.L. Sin, W.L. Soong and N. Ertugrul, “Induction Machine On-Line Condition Monitoring and Fault Diagnosis – A Survey,” Australasian Universities Power Engineering Conference, Christchurch, 2003. [2] S. Nandi and H.A. Toliyat, “Condition Monitoring and Fault Diagnosis of Electrical Machines – A Review,” IEEE Ind. Appl. Society Annual Meeting, 1999. [3] R. Fiser and S. Ferkolj, “Application of Finite Element Method to Predict Damaged Induction Motor Performance,” IEEE Trans. on Magnetics, vol. 37, no. 5, Sep. 2001, pp. 3635-3639. [4] A. Bellini, F. Filippetti, G. Franceschini, C. Tassoni and G.B. Kliman, “Quantitative Evaluation of Induction Motor Broken Bars by Means of Electrical Signature Analysis,” IEEE Trans. on Ind. Appl., vol. 37, no. 5, Sep./Oct. 2001, pp. 1248-1255. [5] M.F. Cabanas, M.G. Melero, G.A. Orcajo, F. Rodriguez Faya and J. Solares Sariego, “Experimental Application of Axial Leakage Flux to the Detection of Rotor Asymmetries, Mechanical Anomalies and Interturn ShortCircuits in Working Induction Motors,” Int. Conf. on Elect. Machines, 1998, pp. 420-425. [6] M.E.H. Benbouzid, “A Review of Induction Motors Signature Analysis as a Medium for Faults Detection,” IEEE Trans. on Ind. Electronics, vol. 47, no. 5, Oct. 2000, pp. 984-993. [7] W.T. Thomson, “On-Line Fault Diagnosis in Induction Motor Drives via MCSA,” EM Diagnostics Ltd., Scotland, 2001. [8] W.T. Thomson and M. Fenger, “Current Signature Analysis to Detect Induction Motor Faults,” IEEE Ind. Appl. Magazine, vol. 7, no. 4, Jul. 2001, pp. 26-34. [9] Vibration Institute – Discussion Zone, 1999. URL: http://www.vibinst.org/_vibezone/000001e7.htm. [10] P. Horowitz and W. Hill, “The Art of Electronics,” Cambridge University Press, 1989.