Detect Induction Motors Fauls

7/27/2019 Detect Induction Motors Fauls

1/21


2/21

3. Rotor related: 10%.4. Others: 12%.

There have been some integrated motor protection systems for monitoring electrical fault in induction motorby analysing the motor current [2,3]. On the other hand, bearing faults are quite difcult to spot from the

motor current. Stack [4] showed from experimental results that faults in bearings produce unpredictable andbroadband changes in the motor current. The authors arrived to the conclusion that for this failure the motorcurrent is a very poor indicator. However, bearing failures because their precise nature, have a clear route tospot through vibration monitoring [5,6].

Schoen [2] developed an unsupervised, online system for induction motor fault detection based on articialneural network. Firstly, the system utilised a selective lter in order to reduce the amount of harmonics to amanageable number. After sufcient training period, the neural network is able to sign a potential failurecondition. While this technique has demonstrated success in identifying an incipient failure, a prerequisite forits operation is a priori fault data, which is clearly no available. This impedes the practical operation of suchmethods. FEM (nite element method) simulations can remove this requirement by predicting the machinebehaviour under various operational conditions. FEM is used in our investigation to foresee the changes of motor performance due to the changes in the internal parameters when the motor is working under faultconditions. Numerical simulations generate useful data, which are used to test the diagnostic techniques.

Acosta [3] presented an online monitoring system that uses the combination of motor current signatureanalysis (MCSA) and Parks vector approach. The authors based on experimental observation and onknowledge of the machine constructed a knowledge-based system. The motor condition identication taskrequires the interpretation of data and makes a decision from this data. From the point of view that seesinduction motor condition as a fuzzy concept, there has been some fuzzy logic approaches for diagnosis. Thelack of proper processing of fuzzy input data and the construction of the membership functions are presentedas the major difculties [7]. This problem is tackled in this work by using FEM in order to generate reliablevirtual data, which allows the construction of the membership functions in all fault and load conditions. Fuzzylogic approach is used to make decisions about the motor condition. Fuzzy logic can describe thecharacteristics of an industrial process with linguistic terms. Fuzzy logic was chosen because the motor

condition constitutes a fuzzy set. In practice, the users are concerned about condition of the motor in terms of a linguistic variable that can be expressed as good, damaged or seriously damaged.

The task of the diagnostic system presented in this work is to detect an upcoming machine fault as early aspossible, in order to save expensive manufacturing processes or to replace faulty parts. The proposedmonitoring system can monitor eccentricity, rotor and stator related faults by analysing the motor current.

This work focuses on the application MCSA to diagnose faults in three phase induction motor drives,establishing a general scheme that permits to spot failures in variable frequency. A traditional MCSA utilisesresults of spectral analysis of the supply current of an induction motor to detect an existing or incipient failureof the motor in the drive system. The spectral analysis is rather complicated and knowledge of the slip of themotor as well as motor data are needed [6,8,9] . The scheme developed in this work suggests new ideas withrespect to the traditional scheme given by Nandi and Toliyat [9]. The proposed scheme permits theidentication of faults in variable frequency and avoids the detailed analysis of the current spectrum, thusreducing the computation task. Further, in many applications it is desirable to detect the presence of the faultwith minimal computation and cost. The motor current amplitudes are also used in order to spot failures inthe stator winding. Motor current amplitudes contain potential fault information and constitute the mostsuitable indicator for diagnosing stator winding fault, in term of easy accessibility, reliability and sensitivity.

2. Methods

2.1. Motor current signature analysis

A traditional MCSA is a non-invasive, on line monitoring technique to diagnose problems in inductionmotor. A large amount of research has been directed toward using the stator current spectrum to sense motorfaults. The monitored spectral components can result from a number of sources, including those related to

ARTICLE IN PRESS

P.V.J. Rodr guez et al. / Mechanical Systems and Signal Processing 22 (2008) 12161236 1217


3/21


4/21

phase winding has less impedance, less turns and therefore less magnetomotive force. This gives a possibility of diagnosing stator short circuit by monitoring only the amplitude of the phase currents [14].

2.2. Finite element method

This software package was designed for the transient magnetic eld analysis of electrical machines coupledwith the circuit equations of the machine windings. This method allows the simulation of an electrical machinefed from measured voltages used in experiments. The simulation of the induction machine is based on thetime stepping, nite element analysis. The magnetic eld in the cross-section area of the test machine iscomputed by using an in-house 2-D FE program. The software uses the time-stepping method, which takesinto account the motion of the rotor and the induced voltage due to this motion. The motion of the rotor isachieved by changing the mesh in the airgap and the time dependence is modelled by the CrankNicholsonmethod.

Some of the 3-D effects like ux fringing and end windings are modelled with analytical and electric circuitapproaches. The magnetic eld, the current and the potential differences of the winding are obtained in thesolution of the coupled eld and circuit equations. A full description of the software and its accuracy is given

in [15]. A detailed description about fault implementation can be found in [16]. A diagram of the FEMmodelling is given in Fig. 1 . The FEM program permits the generation of data with the motor working indifferent condition of load as well as changing the severity of the fault.

The simulations are carried out with a xed time step of 25 ms and a total of 40 000 steps, assuming aconstant speed in the steady state. As examples, Fig. 2 shows the generated data for a healthy motor at fullload (3% slip) and fed at 100 Hz. Fig. 3 shows the generated data for a motor working with 33% of dynamiceccentricity at the same load condition supply with a sinusoidal source at 100 Hz. From this last gure canclearly be seen the harmonic components due to eccentricity given by formula (2) and the main component at100 Hz. Our motor has two parallel paths in the stator winding. The analysed current corresponds to one of these branch currents.

The aim of FEM here is to foresee the changes of motor performance due to the changes in the internalparameters when the motor is working under fault conditions. FEM permits the evaluation of the inuence of different motor faults in an inexpensive and accurate manner.

2.2.1. Eccentricity harmonics with loading and eccentricity degreeFig. 4 shows the generated data for a motor working with a mixed eccentricity of 37% dynamic and 10%

static at half load condition (1.9% slip). From Fig. 4 , can be clearly seen the harmonic components due toeccentricity at f s7 f r and the main component at 100 Hz. The amplitude of the sideband currents increasesproportionally to the level of dynamic eccentricity. Fig. 5 shows the variation of the sideband currents with thedegree of dynamic eccentricity when the motor is working at full load (3% slip).

ARTICLE IN PRESS

Supply voltage

Shaft torque

Dimensions

Material Data

Winding Data

FEM model

The electromagnetic fieldcouples the inputs and outputs

1 A t

u Al

+ =

Inputs Outputs

Currents

Rotational speed

Fluxes in coils

Forces

Losses

Other..

Fig. 1. Electrical machine modelling by FEM.



5/21

It is important for condition monitoring strategies to make clear the dependence between the amplitude of the sideband currents and the machine loading. The load dependence is studied for the case of a pure dynamiceccentricity, see Fig. 6 . A non-linear relationship between harmonic amplitudes and loading is obtained. The

ARTICLE IN PRESS

Fig. 2. Motor current (a) and spectrum (b). Data generated by FEM. Healthy motor. Rated load (3 % slip).

Fig. 3. Motor current and spectrum. Data generated by FEM. Dynamic eccentricity.



6/21

lowest values for the sideband current is obtained at full load, while the highest is obtained at no-loadcondition. During the loading, the currents, which create the MMF increase theirs amplitudes, however, theamplitude of the induced current harmonics do not change linearly with loading as can be seen in Fig. 6 . Thisis explained by the fact that when the machine is slightly loaded, the generated asymmetric ux due to theeccentricity can easily ow through the airgap producing electromotive force. When the load increases, the

ARTICLE IN PRESS

Fig. 4. Motor current (a) and spectrum (b). Data generated by FEM. Mixed eccentricity, 10% static and 37% dynamic. Half load (1.9%slip).

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.1

C u r r e n

t [ A ]

Upper Harmonic (fs+fr) Lower Harmonic (fs-fr)

0.2 0.3

Eccentricity degree

0.4 0.5 0.6 0.7

Fig. 5. Sideband currents as a function of the eccentricity, full load (3% slip).



7/21

asymmetric ux induces current in the motor cage, which opposes the asymmetric ux, damping themagnitude of the electromotive forces.

Furthermore, the asymmetric ux induces circulating currents in the parallel branches (the motor studiedhas two parallel branches) of the stator winding. These currents tend to equalise the ux distribution reducingthe radial ux. From this analysis is deduced that a no-load test is the most informative for the identicationof dynamic eccentricity. In the no-load condition, there are no rotor currents to damp the asymmetric ux,thus the induced harmonics in the motor current have the highest amplitudes, see Fig. 6 , it shows that theworst case scenario to be detected is at full load when the harmonics have the lowest amplitude. The non-

linear relationship implies that eccentricity detection is more reliable at no-load condition.

2.2.2. Behaviour of the broken bars harmonics with loading and number of broken barsFig. 7 shows the current spectrum for a motor working at full load with three broken rotor bars and fed

from a sinusoidal supply of 100 Hz. The main harmonics (from 1 to 6) given by formula (4) can be clearly seen.The sidebands around the fundamental (100 Hz) correspond to the third and fourth harmonics.

The amplitudes of the harmonics due to broken bars are proportional to the number of broken bars as it isshown in Fig. 8 . This gure shows the cases of healthy, one broken bar, three broken bars and ve broken barsat full load condition (3% slip).

The amplitude of harmonics due to broken bars is proportional to loading as can be seen in Fig. 9 . This isthe case of simulated data for a motor working with three broken bars. The fourth harmonic hardly varies

with the loading; this harmonic is called the upper sideband and is due to consequent speed oscillations [6]during broken bars events.

2.3. The proposed motor protection system

The proposed detection system in this work is a consequence of the analytical results. The scheme of thesystem can be seen in Fig. 10 . The system consists of two main blocks. The left-hand block to spotelectromechanical faults from the rotor (broken rotor bars and eccentricity) is based on monitoring thecontent of the spectrum of the current. The right-hand block to spot faults from the stator is based onmonitoring the amplitudes of the motor currents.

The left-hand block has a new idea with respect to the traditional scheme given by Nandi and Toliyat [9], inorder to make the system able to work in variable frequency and avoid the detailed spectral analysis carry out

ARTICLE IN PRESS

0

1

2

3

4

0

C u r r e n

t [ A ]

Upper harmonic (fs+fr) Lower harmonic (fs-fr)

4.5

3.5

2.5

1.5

0.5

0.005 0.01 0.015

Slip [s]

0.02 0.025 0.03 0.035

Fig. 6. Load dependence of the sidebands currents. Motor working with 33% pure dynamic eccentricity.



8/21

by a traditional MCSA. After the A/D block, a predictive lter is used to remove the fundamental component.This lter is able to work in variable frequency keeping the ltering properties [17].

The role of this lter is very important. This lter does not produce delay between the incoming signal andthe ltered signal [17]. This quality is useful for detecting harmonic components by subtracting the input signal

ARTICLE IN PRESS

Fig. 7. Spectrum of the motor current for a motor with three broken rotor bars at full load (3% slip).

Fig. 8. Current spectrum in different rotor conditions. Motor working at full load (3% slip).



9/21

ARTICLE IN PRESS

A m p e r e s

[ A ]

First harmonic

Second harmonic

Third harmonic

Fourth harmonic

Fifth harmonic

Sixth harmonic

2.00

1.80

1.60

1.00

1.20

1.40

0.80

0.60

0.40

0.20

0.000.00 0.01 0.02

Slip [s]

0.03 0.04

Fig. 9. Harmonics during broken bars with loading, three broken bars.

M

Three-phasesource

A/D

Predictivefilter

Subtraction

PreprocessorFFT and

averaging

Calculaterms of current

Fuzzyinferencesystem

Rotor &Stator

STATORFAILURE

BROKEN ROTOR BARSAND ECCENTRICITYFAILURES

A/DLow PassFilter

S/rms

I b

I c

I a

Fig. 10. Block diagram of the proposed integrated motor protection system.



10/21

from the output of the ltered signal without delay, yielding a measurement of the harmonic componentsrather than the fundamental sinusoidal. The multistage lter is shown in Fig. 11 . The rst block is a threepoint median lter, which removes the disturbing impulses.

The median lter is a non-linear lter that operates by sorting the samples inside the moving lter windowby magnitude, choosing the middle value, and removing completely isolated impulses. The median lter causes

one sample delay and does not restore a sinusoidal signal after removing the impulses. The adaptive predictorcompensates these drawbacks. The predictor predicts two steps ahead; one to compensate the delay in medianlter and the other to allow interpolation in the last stage. This lter behaves as a band pass lter with anarrow pass-band centred at the main frequency. Since adaptive predictor is used, the prediction step remainsaccurate, even if the line frequency changes. Thus, the lter is able to work in variable frequency, only bychanging some control parameters correspondingly [17]. The frequency range is limited only by the Nyquistfrequency of the discrete time system. Fig. 12 shows an example of the lter performance in steady state whenthe motor is working with 20% dynamic eccentricity, half load (1.9% slip) and fed at 100 Hz.

The lter structure is shown in Fig. 13 , there are two nite impulse response (FIR) lters. The FIR inside theloop produces the update coefcients for the FIR that produces the ltered output. The coefcients of theadaptive lter are updated using the least mean square (LMS) algorithm, which is chosen due to its robustnessand computational simplicity [17]. The coefcient update formula is

H n 1 H n 2 u en X n 2, (6)

where

en xn H T n X n 2 is the error ;

H n h0; . . . ; hn 1 T is the coefficient vector ;

X n 2 xn 2; . . . ; xn N 1 T is the data vector :

The constant u is xed small enough to guarantee the stability of the LMS algorithm. The adaptive lterconguration was implemented in MATLAB/SIMULINK, with 22 coefcients. The lter shows good

convergence properties and a signicant reduction of the distortion of the primary sinusoidal component.Another advantage from the use of the predictive lter is that digital processing offer higher accuracy thananalog processing. The lter was tested at 50, 60 and 100 Hz with current data from FEM motor simulationprogram.

When the motor is working under fault conditions relevant changes occur in the motor current, e.g. inFig. 14 can be seen the harmonics components due to broken rotor bars. After the subtraction cancels themain component, just the harmonics components can be seen (see Figs. 15 and 16 ). The subtraction result of the ltered signal from the input signal constitutes the inputs for the FFT and pre-processor block. Byremoving the main component, is obtained in the output the harmonic components, which constitute theuseful information for identifying the motor condition. Once we do not have the main component, the sum of the harmonics with higher amplitude than a pre-set level is calculated. Several references indicate [79] thedrawback of the noisy environment and also suggest the idea of reducing the fundamental component in orderto improve the results in the fault identication system. But by removing the main component we obtain in theoutput the harmonic components, which constitute the useful information for detecting the motor condition.Once we do not have the main component we just have to calculate the sum of the harmonics with higheramplitude than a preset level with a simple algorithm for detecting the faulty condition, as it is illustrated inFig. 15 (healthy case) and Fig. 16 (faulty case). The preset level is determined by the harmonic contents duringhealthy situation. Consequently, avoiding the detailed spectral analysis of the line current for detecting the

ARTICLE IN PRESS

MedianFilter

AdaptivePredictor

Enhancen andInterpolation

Fig. 11. Block diagram of the adaptive multistage lter.



11/21

presence of the fault. This means, minimal amount of data memory requirements and minimal computationand cost. Further, very precise information it is not necessary neither about the motor slip nor about motordata, which determine the exact position of the spectral components, related to a motor fault. Further, themultistage adaptive lter is a truly adaptive algorithm. Thus, the lter automatically follows any variation inthe supply frequency, while maintaining their ltering property [17]. The system is also suitable to work invariable frequency, which means that the same lter can work at 50, 60 Hz or any frequency, only changingsome control parameters, correspondingly. It was tested at 50, 60 and 100 Hz with motor data from FEMmotor simulation program and real measurements.

The faulty condition is determined by the total sum of harmonics with amplitude higher than the thresholdlevel, see Figs. 15 and 16 . A block diagram of the pre-processor and the averaging block is shown in Fig. 17 . Inthis ow chart, integer N is the number of harmonic components determined by the wide of the usefulspectrum after the low pass ltering. The chosen value in this work was 500, which means from 1 to 500 Hz,with a frequency resolution of 1 Hz. I integer controls the harmonic order. The sampling frequency used was40 kHz and the length of data set 40 000 samples for both the simulated and measured data.

ARTICLE IN PRESS

Fig. 12. Input current when the motor is working with 20% dynamic eccentricity and ltered current.

x (n )

z -2 LMSFIR

FIR

e (n )

Y (n)

Fig. 13. Filter block diagram.



12/21

ARTICLE IN PRESS

Fig. 14. Stator current and its spectrum. Motor working with two broken bars at half load.

Fig. 15. Current after subtracting the fundamental component and its harmonic contents, healthy condition.



13/21

The value of S is load dependent. Then, it is divided by the rms values of the total current. Later on, this

value from the division that we call R is one of the inputs for the fuzzy logic engine. If the value of R islarge means that the variable rotor condition RC is damage. If the value of R is normal, we have ahealthy rotor. Fig. 18 shows the membership function for the variable R. In a membership function, the x-axis indicates the normalised value of the universe of discourse of the linguistic variable (in this case R),while the y-axis indicates the probability of belonging to one of the classes (in this case large or normal).

The fuzzy system allows the transformation of heuristics and linguistics terms into numerical values viafuzzy rules and membership functions and it is able to approximate the complex relationship related with theidentication task. A no-load test must decide if the harmonics are due to broken bar or eccentricity eventsbecause the harmonics due to broken bars are approximately zero at no load (see Fig. 9 ), while by thecontrary, the harmonics due to eccentricity have maximum amplitude at no load (see Fig. 6 ).

For the detection of stator winding faults (following the right-hand block in Fig. 10 ), the amplitudes of thecurrents (rms) are categorised using four linguistics values. Fig. 19 shows the membership functions for thesecategories. These categories are Very Small (VS), Small(S), Medium (M) and Large (L). Themembership functions for the input and output variables are constructed by the analysis of data generated byFEM.

Thus, the FEM program is run from no-load to full-load in the healthy situation and the rms values of thephase currents are calculated, obtaining values from 25 to 65 A. At every operational point, the loading isconstant. These values are normalised between [0,1], dening the membership function which correspondswith the linguistic term M, for a healthy motor. Thus, the load information is explicitly in the membershipfunctions. A similar process is repeated for the faulty conditions, dening the VS, S, and L. The VS conditionis obtained when the motor has a fully open phase, L corresponds to a short-circuit in one phase and Scorresponds to a unloaded motor. All these situations are simulated in FEM, allowing the denition of thetrapezoidal membership functions. The system was tested with triangular, trapezoidal and Gaussianmembership functions. It was found that the combination of triangular and trapezoidal membership function

ARTICLE IN PRESS

Fig. 16. Current after subtracting the fundamental component and its harmonic contents, faulty condition.



14/21

ARTICLE IN PRESS

Normal Large1

Universe of discourse 1

Fig. 18. Membership function of the variable R.

FFT of differencecurrent

Set Threshold

Save the value of S

If Harmonic ( I )>T

(T ), S =0, I =0

S =S + Amplitude ( I )

I < N

I = I + 1

Fig. 17. Block diagram of pre-processor and averaging block.

VS S M L1


Fig. 19. Fuzzy membership functions for current the normalised current I a .



15/21

is the most appropriated for fault diagnosis of induction motors. The trapezoidal function has well denedcorners as the motor has well dened rated parameters.

The linguistic variable Stator winding condition (SC), interpreting the stator condition, may have valuesGood (G), Damaged (D) or Seriously damaged (SD). G refers to a stator with no faults. D mightbe a stator with voltage unbalance, and SD a stator with an open phase or coil short circuit. Fig. 20 shows

the membership functions for these categories, which are also dened by the analysis of data generated byFEM. The design of rules is based on the expert understanding. The simplied number of rules between thethree-rms inputs and the classication of every current is 14. It is also considered a rule that takes into accountthe rms variance of the phase currents. The last is done to improve the system sensitivity and reliability. Themembership function for the amplitude variance of current is shown in Fig. 21 . The variance is calculated asfollow:

v jI a I j2 j I b I j2 j I c I j2

3, (7)

where I a ,I b,I c are the rms of the input currents I is the mean value of I a ,I b,I c.There are two membership output functions, one to evaluate the stator condition and other to evaluate the

rotor condition. There are totally 17 rules in the inference engine, 15 rules for the stator condition and tworules for the rotor condition. The set of rules is as follows:

Rule 1: If I a is VS then SC is SD.Rule 2: If I b is VS then SC is SD.Rule 3: If I c is VS then SC is SD.Rule 4: If I a is L then SC is SD.Rule 5: If I b is L then SC is SD.

ARTICLE IN PRESS

SC

SeriouslyDamaged

Good Damaged

1


Fig. 20. Fuzzy membership function for the stator condition.

Normal Large1


Fig. 21. Fuzzy membership functions of the variable variance.



16/21

Rule 6: If I c is L then SC is SD.Rule 7: If I a is S and I b is S and I c is M then SC is D.Rule 8: If I a is S and I b is M and I c is M then SC is D.Rule 9: If I a is M and I b is S and I c is M then SC is D.Rule 10: If I a is M and I b is M and I c is M then SC is G.

Rule 11: If I a is S and I b is S and I c is S then SC is G.Rule 12: If I a is S and I b is M and I c is S then SC is D.Rule 13: If I a is M and I b is S and I c is S then SC is D.Rule 14: If I a is M and I b is M and I c is S then SC is D.Rule 15: If v is L then SC is D.Rule 16: If R is large then RC is D.Rule 17: If R is normal then RC is G.

2.4. Simulation results

A SIMULINK/MATLAB model was implemented to test the proposed system. The model was tested with

data from FEM motor simulation program. The rated parameters of the motors are given in Appendix A. Thesampling frequency in the FEM simulation program was 40 kHz and number of samples was 40 000. The datawas introduced from MATLAB workspace, in such a way that the model is working online with the data. TheFEM program was run under different stator winding conditions, healthy, open phase, coil short-circuited andinter-turn short circuit. The data were collected under three load conditions, no load, half load and full load.The simulation model was able to identify the fault with excellent accuracy.

During every data set, the fuzzy lter executes 25 validations of the stator condition. In order to prove theperformance of the SIMULINK model under noise condition, a source of noise to each phase was added.Table 1 shows the percentage of correct detection of stator condition under noise.

The FEM program was also run under different rotor conditions: one, two, three and ve brokenrotor bars, 10%, 22%, 33%, 37% and 50% dynamic eccentricity and a mixed eccentricity of 37% dyna-mic and 10% static. This was done at different load conditions. The model was able to identify the rotorcondition in all the data sets corresponding with broken bars, dynamic and mixed eccentricity with thesimulated data.

2.5. Measurement results

A measuring setup was arranged to get data from a working motor. The motor used in the measurementswas the same as used in the FEM motor simulation program. The data was recorded through a transientrecorder. The sampling frequency used was 40 kHz. The tests were carried out with the motor in healthycondition and with a real inter-turn short circuit, mixed eccentricity and broken rotor bars. The real shortcircuit was done between two adjacent turns. The insulation of winding wires were scratched and two wireswere soldered to them. These were long enough to be closed from outside the motor through a switch. Theshort circuit was made active during a short time, just enough to take the 1 s of data. Different rotor faultswere prepared, from one to ve broken rotor bars and mixed eccentricity. The mixed eccentricity was obtained

ARTICLE IN PRESS

Table 1Percentage of correct detection of stator condition under noise

Motor condition Data sets Accuracy (%)

Healthy motor 3 96Open phase 9 100Inter-turn short 3 92Coil short circuited 3 100



17/21

by tting non-concentric support parts between the shaft and bearing, see Fig. 22 . It was achieved (measured)37% dynamic eccentricity and about 10% static eccentricity.

Data were collected at 50, 60 and 100 Hz and in three load conditions for the cases of broken rotor bars andmixed eccentricity. The model was fed with this data in such a way is working online. The model was able toidentify two, three and four broken rotor bars with total accuracy. Fig. 23 shows the motor current when the

motor was working with three broken bars, full load, inverter supply at 100 Hz measured data. Fig. 24 showsthe measured data when the motor was working with the mixed eccentricity. Fig. 25 shows the three phase

ARTICLE IN PRESS

Fig. 22. Elements of the articially created dynamic eccentricity.

Fig. 23. Measured current and its spectrum from the motor working with three broken bars. Inverter supply at 100 Hz.



18/21

currents in a healthy machine. Fig. 26 shows the three phase currents during the real inter-turn short circuit,from this gure can be seen the current unbalance due to the short circuit.

The SIMULINK model was fed with motor data in healthy condition and with a real inter-turn shortcircuit. Data were collected in three load conditions, no load, half load, and full load, at different frequencies.Table 2 shows the detection rate results for the case of inverter supply. The model was also tested with motor

data, when the motor was fed with sinusoidal supply. The results were also with high accuracy.

ARTICLE IN PRESS

Fig. 24. Motor current when the motor was working with a mixed eccentricity. Measured data.

Fig. 25. Terminal phase currents in a healthy motor. Inverter fed at 100 Hz.



19/21

3. Discussions

The main objective of this work was to establish a layout capable of detecting the motor condition bymonitoring the motor currents. The data analysis from a FEM motor simulation program showed the samefeature in the motor current as it was predicted in the literature. Thus, new harmonics in the current spectrumappear when there are broken rotor bars and eccentricity faults. On the other hand, as was expected from theanalytical results, no new prominent current harmonics are generated when the motor is working under statorwinding fault.

From the analytical and simulation results, a novel fuzzy logic and predictive lter layout was designed. Inthis layout, a predictive lter to cancel the main harmonic component was introduced. The lter was tested atdifferent frequencies with simulation and measured data. It showed good performance and it was able to workwith both sinusoidal and inverter supplies. The lter was able to enhance the spectrum and improve the

ARTICLE IN PRESS

Fig. 26. Terminal phase currents in a faulty motor during an inter-turn short circuit. Inverter fed at 100 Hz. Measured data.

Table 2Percentage of correct detection. Measured data with Inverter supply

Frequency (Hz) Load Detection accuracy (%)

Healthy Faulty

25 No-load 94.7 100Half load 97.2 94.4

Full load 96.3

50 No-load 94.7 100Half load 96 100Full load 97.3

75 No-load 100 100Half load 100 100Full load 100

100 No-load 100 100Half load 100 100Full load 100



20/21

identication system. As a comparison, a classical MCSA requires a frequency resolution better than 0.1 Hzand in practice is unable to distinguish the side bands for so 1.5 [9]. Our proposed system with a frequencyresolution of 1 Hz obtained a good accuracy detection rate. This means minimal amount of data memoryrequirements and minimal computation and cost. Further, the very precise information about the motor slip isnot needed. The detection rate was accurate for the cases of broken rotor bars and dynamic eccentricity with

both, simulated and measured data.In this layout, fuzzy logic was used to analyse the data and make decisions after the cancellation of the maincomponent. It was able to detect the motor condition with high accuracy. However, it is also possible to useother soft-computing or traditional techniques to carry out the identication task. The rotor condition can beidentied even from simple rms values of the harmonic components, once, that has been cancelled the maincomponent.

The SIMULINK model was able to identify the stator winding condition with simulation and real data. Itwas also tested with noisy simulation data. The model was able to identify the stator condition with goodaccuracy even under noisy condition. The model was fed with measured data. It was tested at differentfrequencies with both inverter supply and sinusoidal supply. In both cases, the SIMULINK model was able toidentify the stator condition. Two conditions were tested with measured data: healthy condition and inter-turnshort circuit. The model showed that comparing the rms current of the phases reveals changes in the internalelectrical balance of the machine. It was sensitive enough to reveal one shorted turn in the stator winding,where there were 11 turns per coil.

It is important to sign that in the case of stator winding faults in induction machines the most importantfault to be detected is a primary inter-turn short circuit (the fault in an early stage). Because by detecting it atthe early stage would avoid the total destruction of the stator winding. Our proposed system showed goodperformance in detecting such a condition.

4. Conclusions

This work showed the feasibility of spotting stator winding faults and broken rotor bars by monitoring themotor current with appropriate use of the existing techniques for signal processing and soft computing. Theproposed system is able to detect different induction motor faults with high accuracy and it is remarkable thatthe detailed spectral analysis is avoided. This work is an example of fusion between the soft-computingtechnique (fuzzy logic) and hard-computing technique (FEM) in order to make a reliable identication system.A strait forward application of the system is in variable speed drives.

Appendix A. Motor rated parameters

Parameter

Rated power 35 kW

Rated frequency 100 HzRated voltage 400 VRated current 64 AConnection StarNumber of pole pairs 2Number of stator slots 48Number of rotor bars 40

References

[1] Motor Reliability Working Group, Report of large motor reliability survey of industrial commercial installations, part I, IEEETransactions on Industry Applications IA-1 (1985) 853872.

ARTICLE IN PRESS



21/21

[2] R. Schoen, B. Lin, T.G. Habetler, J. Schalag, S. Farag, An un-supervised online system for induction motor fault detection usingstator current monitoring, IEEE Transactions on Industry Applications 31 (6) (1995) 12801286.

[3] G.G. Acosta, C.J. Veruchy, E.R. Celso, A current diagnosis system for diagnosing electrical failures in Induction Motors, MechanicalSystems and Signal Processing 20 (2006) 953965.

[4] J.R. Stack, T.G. Hableter, R.G. Harley, Fault classication and fault signature production for rolling element bearings in electricalmachines, IEEE Transactions on Industry Applications 40 (3) (2004).

[5] P. Tavner, J. Penman, Condition Monitoring of Electrical Machines, Research Studies Press, Letchworth, Herefordshire, England,1987, 302pp.[6] S. Nandi, H. Toliyat, Condition monitoring and fault diagnosis of electrical motorsa review, IEEE Transactions on Energy

Conversion 20 (4) (2005).[7] F. Fillipetti, C. Tassoni, P. Vas, Recent developments of induction motor drives fault diagnosis using AI techniques, IEEE

Transactions on Industrial Electronics 47 (5) (2000) 9941000.[8] W.T. Thomson, M. Fenger, Current signature analysis to detect induction motor faults, IEEE Transactions on Industry Applications

July/August (2001) 2634.[9] M.E.H. Benbouzid, M. Viera, C. Theys, Induction motors fault detection and localization using stator current advanced signal

processing techniques, IEEE Transactions on Power Electronics 14 (1) (1999) 1422.[10] J.R. Cameron, W.T. Thomson, C. Eng, A.B. Dow, Vibration and current monitoring for detecting airgap eccentricity in large

induction motors, IEE Proceedings 133 (3) (1986).[11] D.G. Dorrell, W.T. Thomson, S. Roach, Analysis of airgap ux, current, and vibration signals as s function of a combination of static

and dynamic eccentricity in 3-phase induction motors, IEEE Transactions on Industry Applications 33 (1997) 2434.

[12] G.B. Kliman, R.A. Koelg, J. Stein, R.D. Endicott, M.W. Madden, Noninvasive detection of broken bars in operating inductionmotors, IEEE Transactions on Energy Conversion 3 (4) (1988) 873879.

[13] G.M. Joksimovic, J. Penman, The detection of inter-turn short circuits in the stator winding of operating motors, IEEE Transactionson Industry Applications 47 (5) (2000) 10781084.

[14] M.E.H. Benbouzid, A Simple Fuzzy Logic Approach for Induction Motor Stator Condition Monitoring, IEMDC 2001, IEEEInternational, January 2001, pp. 634639.

[15] A. Arkkio, Analysis of induction motor based on numerical solution of the magnetic eld and circuits equations, Acta PolytechnicaScandinavicaElectrical Engineering Series 59 (1987) 97 Available at / http://lib.hut./Diss/list.html#1980 S .

[16] S. Po yho nen, M. Negrea, P. Jover, A. Arkkio, H. Hyo tyniemi, Numerical magnetic eld analysis and signal processing for faultdiagnostic of electrical machines. COMPEL, The International Journal for Computation and Mathematics in Electrical Engineering22 (4) (2003) 969981.

[17] O. Vainio, S. Ovaska, Multistage adaptive lters for in-phase processing of line frequency signal, IEEE Transactions on IndustrialElectronics 44 (2) (1997) 258264.

ARTICLE IN PRESS

http://lib.hut.fi/Diss/list.html#1980http://lib.hut.fi/Diss/list.html#1980

Documents

Detect Induction Motors Fauls