Intelligent System Design for Stator Windings Faults Diagnosis: Suitable for Maintenance Work

doi:10.4236/jsea.2013.610063

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Journal of Software Engineering and Applications, 2013, 6, 526-532

http://dx.doi.org/10.4236/jsea.2013.610063 Published Online October 2013 (http://www.scirp.org/journal/jsea)

Intelligent System Design for Stator Windings Faults

Diagnosis: Suitable for Maintenance Work

Lane M. Rabelo Baccarini, Vinícius S. Avelar, Valceres Vieira R. E. Silva, Gleison F. V. Amaral

Department of Electrical Engineering, Federal University of São João del Rei, Praça Frei Orlando, Brasil.

Email: [email protected]

Received June 3rd, 2013; revised July 4th, 2013; accepted July 12th, 2013

License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ABSTRACT

The short circuit is a severe fault that occurs in the stator windings. Therefore, it is very important to diagnose this type

of failure in its beginning before it causes unscheduled stop and the machine loss. In this context, the Support Vector

Machine (SVM) is a tool of considerable importance for standard classification. From some training data, it can diag-

nose whether or not there is a short circuit beginning, and which is important for predictive maintenance. This work

proposes a technique for early detection of a short circuit between the turns aiming at its implementation in a real plant.

The paper shows simulation and experimental results, and validates the proposed technique.

Keywords: Fault Diagnosis; Support Vector Machines; Maintenance Work; Software Tool; Winding Short-Circuit

1. Introduction

The induction machine is used in a wide variety of ap-

plications to convert electrical energy into mechanical

energy. There is no doubt that it is the most widely used

machine in industry due to its robustness and low cost.

However, the induction motors are very easy to be dam-

aged during their operations. In some industrial processes,

the induction motors are often installed in the hostile

environment that may easily lead to the deterioration [1].

The production technique evolution grows up as fast

as the productive capacity of industrial plants supported

by the equipment improvement. The equipment’s life

cycle requires high investments. Thus, the maintenance

and operation schedule need to be safe, efficient and en-

sure larger indexes of availability and security.

Recently, there are a lot of methods used to detect sta-

tor inter-turn short circuit in predictive maintenance.

Nevertheless, many of them are expensive, ineffective or

difficult to be implemented in real process. Another point

is the fact that many processes are in continuous activi-

ties or in aggressive environments; the motor requires

monitoring since it drives a non-stop machine. It depends

on a non-invasive way to detect faults, and no human is

exposed directly to the machine in working condition [2,

3].

The induction machine stator windings are exposed to

stress by different causes: thermal effects; mechanical

vibrations; repetitive voltage pulses stresses under PWM

inverter drives. Research shows that stator faults are re-

sponsible for 36% of induction machine faults [4-6]. De-

terioration of the induction machine three-phase insula-

tion usually begins with short-circuit involving little

turns of one phase. Fault current is approximately twice

as high as the blocked rotor current. It causes local heat-

ing and quickly spreads to other winding sections [7-10].

A short circuit is recognized as one of the most diffi-

cult failures to be detected. Inter-turn short circuit faults

that occur on the stator, start with a few inter-turns until

reaching a more severe failure, such as phase-to-phase

faults and phase-to-ground faults. It is known that the

speed of the faults’ spread is fast, thereby justifying con-

tinuous motor monitoring to detect the short circuit [11].

Thomson and Fenger (2001) analyzed short circuit

between inter-turns of an induction machine in low volt-

age working conditions [12]. According to the authors,

even with a significant percentage of short-circuit (20%

of turns in short circuit), the engine worked for 20 min-

utes before complete breakdown. But researchers report

that the impact of failure on the characteristics of the

engine is small, which makes their detection difficult

[13].

A variety of sensors could be used to collect meas-

urement from the machine for the purpose of failure

monitoring. These sensors measure stator voltages and

currents, air-gap and external magnetic flux densities,

Intelligent System Design for Stator Windings Faults Diagnosis: Suitable for Maintenance Work 527

rotor position and speed, output torque, internal and ex-

ternal temperature, vibrations [14]. Methods that use

voltage and current measurements offer several advan-

tages over test procedures that require machine to be

taken off line or techniques that require special sensors

coupled to the motor.

There are two main issues associated with the detec-

tion of induction machine faults. The first issue is the

induction motor modeling, due to the lack of comprehen-

sive field fault data-bases. The second issue is to develop

a completely faulty model in order to avoid the occur-

rence of false positives while diagnosing faults. Special

attention must be given to the net asymmetries and load

condition presence [13].

Some researchers have investigated the potentiality of

reading vibration signals to diagnose electrical faults

[15-17]. However, the techniques are invasive since they

depend on the signals measured and acquired by accel-

erometers, which placed at the equipment structures.

Moreover, the vibration analysis requires a specialist to

analyze the collected signals and provide the correct sys-

tem diagnosis and the techniques, which are ineffective

in diagnosing electrical faults.

The magnetic flux spectrum technique is also an inva-

sive method, once it studies the signals from sensors that

need to be installed inside the motor, thus increasing the

initial cost of the machine and making the system less ro-

bust [18]. The methods based on electrical current analy-

sis are not invasive and they do not require system inter-

ruption [2,3,7,19]. Measurement is made by common

sensors (Current Transformers) and often, they are al-

ready present in the application being monitored.

The fault causes impedance imbalance among the

three motor phases, resulting in the appearance of current

and impedance negative sequence components. However,

the unbalances of voltages supply are inherent sources

that have the same impact, i.e., it causes the appearance

of current and impedance negative sequence components,

which make it difficult to diagnose the failure.

In this context, the Support Vector Machine (SVMs)

has received great attention in the last years [2,20]. The

SVMs highlights are in its ability to generalize, and al-

low its use in diverse areas of knowledge. The technique

has not yet been well explored in the area of predictive

maintenance, thus, the detection and faults diagnosis in

induction machines still need more encouragement and

attention due to the lack of existing papers. The efforts to

find a new novel idea must be encouraged to give more

contributions to robust machine condition monitoring

and diagnosis [21].

This work aims to develop a diagnostic technique for

initial short-circuit between a few turns of the stator us-

ing SVMs features. The method requires only measure-

ment sensors normally presented in the application. First

we will present the simulations results for the technique

application and then it will be validated on a test bed.

The use of computational intelligence techniques re-

quires a set of significant data over faulty conditions for

system design, which precluded their use in a real plant.

It is therefore necessary to find alternatives to practical

systems implementation that requires real data especially

in the predictive maintenance area. The proposed tech-

nique has achieved this goal by making their practical

implementation possible. The following sections describe

the adopted procedures and their practical validation.

2. The SVM Design Using Computing

Simulation Data

This work first step was the design of support vector

machines using only the simulation data. The symmetric

and asymmetric machine model has been implemented

and current and voltage data were obtained from differ-

ent operating conditions. To make the model as close as

possible to the real plant the following conditions were

inserted: under or overload percentage, inherent unbal-

ance supply voltage, small percentage of short circuit

turns and noise in sensor’s measure.

2.1. Asymmetrical Induction Machine Model

The symmetrical machine model is well known in the

literature. The asymmetrical model that allows to analyze

the stator and power supply asymmetries was presented

in [5,13]. The shorted turn leakage inductance is as-

sumed to be ls



where



denotes the shorted turns

fraction. The voltage and flux linkage



equations

of the stator and rotor windings are transformed to dq

axes, rotating at an arbitrary speed ddt





. The s

and r subscripts indicate the stator and rotor variables

respectively. The machine stator equations can be ex-

pressed in complex two-phase dq variables as follows:

d2cos

qss qsdssf

vri ri







 



(1)

d2sin

dss dsqssf

vri ri











  (2)

The parameters ,

and r



represent the fault re-

sistance, are the short circuit current and mechanical

speed. The equations for the rotor circuits are equal to

those corresponding to a healthy motor (Equations (3)

and (4)).



rqrr dr







  (3)



rdrr qr







  (4)

For the shorted turns (as2), the flux linkage equation is

Intelligent System Design for Stator Windings Faults Diagnosis: Suitable for Maintenance Work

528

given by (5).



dcos sin



fs dsqsf

rir iii





 (5)

The differential equations derived above can be solved

by the fourth-order Runge-Kutta method. The stator and

rotor currents can be obtained from the following equa-

tions:



23 56

cos

qs qsqrrmf

iaaLaLai







(6)



23 56

sin

ds dsdrrmf

iaaLaLai







(7)



43 65

cos

qrqr qss mf

iaaLaLai







(8)



43 65

sin

drdr dss mf

iaaLaLai







(9)







27 87 8

cos sin

fasqsqr dsdr

iaiaiaiaia







6







(10)

where:

1234

56 78

1; ;;

;;;

rsr

LLa

aaaa

LL aLLaL

aa aLa



 

 

The parameters

and are the stator and rotor

self inductances, ls and lr are the stator and rotor

leakage and m is the mutual inductance. The electro-

magnetic torque can be expressed in dq variables as:



3sin cos

22 2

mqsdrdsqrmf qrdr

TLiiiiLii i







 



(11)

The first member of this equation is equal to those

corresponding to a healthy motor and the second member

is the additional component introduced by the fault. The

Equations (1) to (11) computer implementations give the

fluxes, the speed and current of the motor in short-circuit

conditions. The components in dq axes (direct and quad-

rature) could be transformed to the abc phase axes. Noise

can be inserted in the current and voltage signals. The

current and voltage signals can be analyzed in time or

frequency domain using Fast Fourier Transform tool.

2.2. Induction Machine Model Simulations

Simulations of different squirrel cage induction machine

were carried out. The motor used here have the following

nominal parameters: 3 CV, 220 V, 60 Hz, 1710 rpm, 4

poles. It is the motor of the test bed. The equivalent cir-

cuit parameters were determined through a no-load test

and a blocked rotor test results. The machine equivalent

circuit parameters are: rs =1.81 Ω, rr= 0.36 Ω, Lls= 2.52

mH, Llr = 4 mH and Lm = 32.7 mH.

The machine symmetrical and the asymmetrical mod-

els were used to generate a database to train and validate

the SVM. For each condition, it was varied the load per-

centage, imbalance level inherent of the power condi-

tions and the percentage of the turns short-circuited. Ta-

ble 1 shows the simulations parameters variations, where

Tn and Vn are torque and voltage nominal values respec-

tively.

2.3. Simulations Results

The data were classified into two conditions:

 Condition 1: Symmetrical operation, i.e., no short

circuit occurrence.

 Condition 2: Inter-turn fault.

For the training process, the kernel parameter, the

Linear Function and the Radial Bases Function (RBF),

with variance within 0.001 to 1 were tested. The SVM

regularization parameters spanned from 1 to 100,000.

Possible combinations of these parameters were made

and the best results taken. The current and voltage fun-

damental and the third harmonic components were used.

The Support Vector Machines were trained in order to

get no failure condition to avoid the engine stop due to

false alarms.

Figure 1 shows the used methodology outline. The

decision making is responsible for diagnosing whether

the engine can continue to operate or if it is necessary to

interrupt service.

Figure 2 presents the current fundamental component

values in the R3 space. It can be noticed that the sampled

data are grouped and overlaid and thus, can not be line-

arly split.

Table 2 contains the number of simulated tests used

for network training and validation and Table 3 rein-

forces the SVM good performance for pattern classifica-

tion. The parameters used for SVM training and valida-

tion were the current fundamental and third harmonic

components.

Table 1. Simulated system parameters variation database.

Parameter MinimumValue MaximumValue

Load 50% of Tn 100% of Tn

Source amplitude 95% of Vn 105% of Vn

Shorted turns percentageμ = 0 μ = 0.03

Figure 1. Decision making structure.

Intelligent System Design for Stator Windings Faults Diagnosis: Suitable for Maintenance Work 529

Figure 2. Pattern distribution in R3 for the stator current

fundamental component—from Simulation.

Table 2. Number of simulations.

Training Validation

Condition 1 100 250

Condition 2 100 250

Table 3. Simulations results.

Training Validation

Condition 1 100 % 100 %

Condition 2 100 % 99.6 %

3. Implementation Using Experimental Data

The test rig, Figure 3, in the Electrical Engineering De-

partment Experimental Research Laboratory, was used to

validate the technique. The experimental systems is set

with: special induction machine to simulate the failure, a

DC machine, a measuring system, encoder, computer

with LabView software installed, a three phase varivolt,

resistances and the National Instruments board acquisi-

tion.

The mechanical load was provided by a separate DC

generator which feeds the variable resistor. In order to

allow tests to be performed at different load levels, the

DC excitation current and load resistor were both con-

trolled. The mechanical structure where the motors are

settled offers the possibility to move the two machines,

in a way the system can be either aligned or different

degrees of misalignment can be tested.

The motor was rewound to allow short circuit simula-

tion between different numbers of coil turns. The con-

figuration allows short-circuits between the minimum of

three turns to thirty three turns maximum. For data ac-

quisition, three to six turns were short circuited. To limit

the short circuit current, a resistance was inserted in se-

ries with the turns.

Shaft alignment in the setup was guaranteed by using a

laser alignment tool from the Ultraspec 8000 equipment.

The baseline measurements for this experiment were

recorded for the test case of minimum vibration magni-

tude. Table 4 shows the parameters deviation from the

engine nominal values.

Three types of mechanical faults: radial and angular

shaft misalignment, mechanical looseness and rotor im-

balances were performed. Each of these types was in-

duced mechanically to different degrees of intensity (or

fault level) and for different load conditions. This allows

analyze whether the mechanical failure misled the short

circuit diagnosis.

Radial misalignments were created by installing addi-

tional shims of specific thickness under the motor’s base

to lift it up slightly from the coupled load shaft. Angular

misalignments were created by rotating the machine at

specific angles away from the original position of the

coupled load shaft.

Mechanical imbalance was created by adding a steel

bolt and 21 grams nut of mass placed at different radial

distances from the rotor shaft on a balanced metal disk.

Mechanical looseness was caused by a structural loose-

ness at the induction motor base.

After each short circuit test, a symmetrical operation

test was performed. This procedure allowed the checking

if the short circuit tests had damaged the induction ma-

chine insulation system.

Figure 4 shows the current fundamental component

normalized in R3 space. The same way, for the model

simulation, Figure 2, we can see from this figure the two

classes splitting difficulty, i.e., no fault condition or short

circuit condition between turns.

Figure 3. The test bed for the exper i mental data acquisition.

Table 4. Real system’s measured parameters variation da-

tabases.

Parameter Minimum Value Maximum Value

Load 20% of Tn 110% of Tn

Source amplitude 85% of Vn 105% of Vn

Shorted turns percentagenone 6 turns

Intelligent System Design for Stator Windings Faults Diagnosis: Suitable for Maintenance Work

530

Figure 5 presents the 180 Hz stator current component

(3th harmonic) distributed in R3 space. This figure shows

that the short-circuit impact is not easily represented by

the current third harmonic component.

Table 5 presents the number of real tests used for the

network training and validation, and Table 6 shows the

classification results. For this condition the current and

voltage fundamental components were used since they

gave the best result. The current third harmonic compo-

nents were not used. It is important to notice that there

was no serious misleading to a short circuit diagnose in a

symmetrical operation causing unnecessary system stop-

ping.

Figure 4. Pattern distribution in R3 for the stator current

fundamental component: Experimental Re sults.

Figure 5. Pattern distribution in R3 for the stator current

third harmonic component: Experimental Result.

Table 5. Number of experimental tests.

Training Validation

Condition 1 45 55

Condition 2 25 55

Table 6. Each test condition hits.

Training Validation

Condition 1 100% 100%

Condition 2 100% 85%

Overall hit 100% 92%

4. Proposed Diagnostic Approach

We can concluded that the proposed technique can be

used to detect initial short-circuit fault. Notice that the

method uses only the plant actual sensor signals. The

proposed method disadvantage is the need of real faulty

condition data for the SVM design, what turns difficult

their use in a real plant. Thus, this work main motivation

is to answer the following question:

Do SVMs designed through simulation data be used

with real data? In different words, does the model pa-

rameters trained systems represent well the actual plant?

Dynamic symmetrical and asymmetrical model data to

train the SVM were obtained. After the network design

using simulation data we used real experimental data to

analyze the network performance. Table 7 gives the per-

formed tests number. For training we used simulated data,

and for validation real data. Tab l e 8 shows the individual

and overall hits using the normalized current and voltage

fundamental components real data.

The total hits rate for the SVM trained by simulated

data was 75%. The system performed significantly well,

since experiments in a short-circuit condition was con-

trolled by a resistance that limited the current dropping it

to less than the real faulty current. This was necessary

to prevent the machine to collapse due to the number of

tests performed. In practice, the faulty current is ap-

proximately 14 (fourteen) times the nominal motor cur-

rent. Thus, the hit rates could be even greater than that

found.

5. Conclusions

Short-circuit is a severe fault and should be diagnosed in

an early stage to avoid major losses, such as unscheduled

production shutdown or even irreversible engine loss.

This study showed the SVM effectiveness for fault diag-

nosis in induction machines and furthermore, made it

possible to be implemented in real plant.

The method requires only sensors usually presented in

Table 7. Number of final tests.

Training Validation

Condition 1 100 100

Condition 2 100 80

Table 8. Experimental result hits.

Training Validation

Condition 1 100% 79%

Condition 2 100% 70%

Overall hit 100% 75%

Intelligent System Design for Stator Windings Faults Diagnosis: Suitable for Maintenance Work 531

the application. The work proposed a non invasive fault

diagnosis method, and no human is exposed directly to

the machine in working condition.

It also showed that mechanical failures and unbalance

voltage supply do not compromise the system shortcir-

cuit fault diagnosis.

The proposed approach came up with very good re-

sults. Finally, from this work we also realized that the

current third harmonic component does not supply the

SVM with useful information for fault diagnosis purpose,

and reduce the cost of data processing.

6. Acknowledgements

The authors thank Fapemig (APQ-00589-11) for the sup-

port given to this work.

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