Winding fault diagnosis of a 3-phase induction motor ...

Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014

Eds. Leo J De Vin and Jorge Solis

Winding fault diagnosis of a 3-phase induction motor powered by frequency-inverter drive using the current and voltage signals Agusmian P. Ompusunggu∗ , Zongchang Liu† , Hossein D. Ardakani† , Chao Jin† , Frederik Petr´e∗ and, Jay Lee† ∗ Flanders’

Mechatronics Technology Centre (FMTC) Heverlee, 3001, Belgium Email: [email protected]

† NSF

Abstract— Three-phase induction motors are critical devices in many engineering areas including high-speed train, aerospace, electric vehicles, industrial robots, machine tools, etc. Nowadays, there is an increasing need of a condition monitoring and prognostic system for induction motors to maintain the availability of systems equipped with induction motors. This paper focuses on the development of a winding fault diagnosis method using 3-phase current and voltage signals. To conduct this study, an induction motor test-bed was developed and constructed. One of the most difficult insulation faults to detect, namely interturn fault, was induced in the motor under three different severity levels. A number of experiments were carried out under different operating regimes, comprising both healthy and faulty states. Two on-line winding fault detection techniques including (i) negative impedance and (ii) voltage mismatch detector have been implemented to develop an early fault detection scheme for stator winding insulation of an induction motor powered by a frequency-inverter drive (i.e. controller). In order to remove the effect of pulse-width modulation (PWM) caused by such a drive mainly on the voltage signals, an integration based signal processing method is proposed to demodulate 3-phase output voltage signals measured on the drive. While, a simple lowpass filtering is applied to the measured 3-phase output current signals. The integration of the proposed signal processing method along with the on-line monitoring techniques provided a robust approach capable of detecting motor’s winding insulation (interturn) faults in early stages.

I. I NTRODUCTION Three-phase induction motors have applications in many engineering areas including high speed trains, aerospace, electric vehicles, robotics, machine tools, etc. Despite reliable and matured devices, failures owing to the thermal, electrical and mechanical stresses are inevitable. As induction motors play a vital function in such applications, an unexpected failure occurring in these devices can thus lead to an unscheduled total breakdown. This undesirable situation can: (i) put human safety at risk and (ii) possibly cause long-term downtimes, that eventually result in high maintenance costs and lost production (i.e. loss of financial income). Condition Based Maintenance (CBM) strategy, which is also known as Predictive Maintenance (PdM), has been proven ∗ Corresponding author

I/UCRC Center for Intelligent Maintenance Systems (IMS) University of Cincinnati, Cincinnati, OH 45221, USA Email: [email protected]

in modern industries as a maintenance strategy that can reduce unscheduled breakdown of machines/systems due to unexpected failures. To realize this strategy in practice, three key technologies are therefore required, namely (a) condition monitoring (CM), (b) diagnosis and (c) prognosis. Nowadays, there is an increasing need for these CBM technologies due to increasing range of induction motor applications and a constant awareness of the high impact of their failure. Fig. 1 shows statistical distribution of common failure modes typically observed in induction motors. As shown in the figure, rolling-element bearing and winding failures due to insulation degradation are the primary causes of unexpected breakdown in induction motors. Because of wide applications of rolling-element bearings in almost all of rotating machinery, many CM, diagnosis and prognosis technologies for such bearings have been developed since the last four decades and widely published in literature. However, the amount of research in CM, diagnosis and prognosis of the winding faults remains limited. 12%

10%

40%

38% Bearing faults Stator winding faults

Rotor faults Other faults

Fig. 1.

Statistics of failure modes in induction motors, adapted from [1]

Winding faults due to insulation degradation can be classified into four types [2], namely (i) inter-turn short of same phase, (ii) short between coils of same phase, (iii) short between two phases and (iv) short between phase to earth. Among these fault modes, inter-turn fault has been considered as the most challenging winding fault to detect in induction

Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014

motors. Several off-line techniques with invasive and nondestructive testing approaches, such as (1) resistance test, (2) DC High-Potential (Hi-Pot) test and (3) surge test, have been developed and widely used in industry for monitoring and detecting winding faults in induction motors [3]. To perform such non-destructive testings, a dedicated equipment is needed. Unfortunately, such an equipment commercially available on the market is quite expensive (approximately ¤30,000 per unit) that can hamper realization of CBM strategy on induction motors. Another drawback of these off-line techniques is that the actual winding condition of a motor cannot be assessed while the motor is in service. On-line techniques based on low-cost hardwares and lesscomplex signal processing for realizing CBM strategy on induction motors have long been desired by industry. Two promising on-line techniques using 3-phase current and voltage signals have been proposed since the last decade ([4], [5]). These techniques assume that an induction motor is directly connected to a 3-phase electric line, implying that the measured 3-phase voltage and current signals are sinusoidal time waveforms. However, an induction motor in many applications is equipped with a frequency-inverter drive (i.e. variable frequency drive) as the controller, which typically uses pulse-width modulation (PWM) methods to control the 3-phase voltages in order to maintain the output current and power within the boundary of reference. Consequently, these two existing on-line monitoring techniques are not readily applicable to such applications. To remedy this gap, this paper proposes an improvement on the monitoring techniques, where the 3-phase PWM voltage signals are preprocessed prior to applying the techniques. The remainder of this paper is organized as follows. Section II discusses the theoretical background of the two on-line techniques and the signal processing applied to the 3-phase current and voltage signals. Section III briefly discusses the experimental setup and the test procedure for data generation. Section IV demonstrates the effectiveness of the improved online monitoring techniques through the experimental data analyses. Section V summarizes some important findings obtained in this study. II. T HEORETICAL BACKGROUND A. Winding Faults Characteristics and Negative-Sequence Impedance Detector The concept of symmetrical components proposed by Fortescue in 1918 [6] is a mathematical representation to describe unbalanced power systems. It suggests that any set of unbalanced voltages or currents can be transformed into three sets of symmetrical balanced phases, namely zero-, positive- and negative-sequence components. Let Vu , Vv and Vw be 3-phase voltages. Their symmetrical components can be calculated as: ⎡ ⎤ ⎤⎡ ⎤ ⎡ V0 Vu 1 1 1 1 ⎣ Vp ⎦ = ⎣ 1 α α 2 ⎦ ⎣ Vv ⎦ , (1) 3 1 α2 α Vw Vn

Eds. Leo J De Vin and Jorge Solis

with V0 , Vp and Vn being the zero-, positive- and negative√ sequence components respectively, α = ej2π/3 and j = −1 being the imaginary unit. The symmetrical components of 3-phase currents, namely I0 , Ip and In , can also be calculated in the same mathematical approach. Under a healthy state, the positive- and negativesequence current should be balanced while the zero-sequence current should be barely observable. When turn-to-earth short circuit exists, the zero-sequence current will not be zero anymore [7]. The imbalance of positive- and negative-sequence currents however, may not be due to actual motor faults. As explained in ([8], [9]), the imbalance of 3-phase voltage supply can also cause imbalance currents in a healthy motor. Hence, when using sequence current as an indicator for motor winding fault, it is necessary to distinguish the effect of imbalanced voltage supply and injected current (fault current) [10]. The sequence impedance can be simply calculated as the ratio between the sequence voltage and the corresponding current. In ([10], [11]), some approaches were proposed to separate the negative-sequence current caused by supply voltage imbalance and the current arising from the motor stator winding fault based on the calculation of sequence impedance under healthy condition. However, another difficulty is that the sequence impedance is not always constant. Factors such as speed, load, and temperature can cause great considerable variations on sequence impedance [12]. The magnitude of negative-sequence impedance itself can be seen as an indicator of stator winding fault. Under a healthy state, the negative-sequence impedance Zhn is given by: Zhn =

Vn Vnr + jVni = = Rhn + jXhn . In Inr + jIni

(2)

If a short circuit exists in stator windings (i.e. faulty state), the negative-sequence current rise (hereafter called the fault current) Im is present and the equivalent negative-sequence impedance Zf n is given by Vn Vnr + jVni = Rf n +jXf n . = In + I m (Inr + jIni ) + (Imr + jImi ) (3) For quantifying the severity level of winding faults, the ratio between the negative-sequence impedance of a healthy and faulty state, being denoted as k, can be considered as the severity factor

Zf n =

k=

Zhn Imr + jImi =1+ . Zf n Inr + jIni

(4)

When the severity level of winding fault increases, the fault current Im will increase, thus making Zf n smaller as revealed in Eq. (3) and k larger as shown in Eq. (4). Experimental observations in [5] also suggest similar conclusions. B. Voltage Mismatch Detector In order to predict the incipient winding faults in both balanced and unbalanced systems, a method called voltage mismatch approach was proposed by Sottile et al ([13], [4]). This method defines certain impedance parameters and

Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014

performs a training stage to define a baseline for them. At each speed regime, a baseline is defined and the behavior of the motor would then be compared to the baseline. Since the baseline represent the healthy condition of a motor with any possible asymmetry in its structure, the deteriorations in the winding insulation of the motor will not be covered by the asymmetries in the structure of the motor. Suppose that V0 , Vp and Vn are respectively the zero-, positive- and negative-sequence components of the motor 3phase voltages, while I0 , Ip and In are respectively the zero, positive- and negative-sequence components of the motor 3-phase currents. The following set of equations can be constructed according to [4]: ⎡ ⎤ ⎡ ⎤⎡ ⎤ V0 z00 z01 z02 I0 ⎣ Vp ⎦ = ⎣z10 z11 z12 ⎦ ⎣ Ip ⎦ , (5) Vn z20 z21 z22 In where zxy are the parameters representing the motor’s design and construction. If no turn-to-earth fault is present, the summation of the 3phase currents will be zero according to Kirchhoff’s law. Consequently, the zero-sequence current I0 and the corresponding voltage V0 will be zero. Under this assumption, Eq. (5) can be simplified as follows [14]: Vp = z11 Ip + z12 In , Vn = z21 Ip + z22 In .

(6)

In [13], it was revealed that the load does not have much influence on winding impedance. For different speeds, current and voltage in 3-phases are used to calculate the z-coefficients. So a library of z-coefficients will be made from measurements in different speed regimes. If the condition of the motor changes, the stored z-coefficients will no longer be correct for these equations. In such a case, the calculated Vp and Vn will be different from their measured value. The difference between the calculated and measured positive- and negativesequence voltages can thus be seen as an indicator of winding faults in the motor. This indicator can be further quantified as the Square Prediction Error (SP E) defined in the following equation: N 1  2 SP E = [Vi − Vl ] , (7) N i=1 where Vi denotes either the positive- or negative-sequence voltage at an arbitrary state and Vl denotes the corresponding voltage at a reference state. The flowchart shown in Fig. 2 schematically summarizes how the voltage mismatch detector works ([13], [14], [4]).

Yes

0

I0

Ground Fault Exists

Baseline Data from Different Speeds

I0

Calculate

No

Vp

z 11 I p  z 12 I n

Vn

z 21 I p  z 22 I n

Electric Motor

Test Data

Obtain Library of zxy for Different Speeds Calculate Vp and Vn Using zxy Library

Turn to Turn or Inter-turn Faults Exist

Yes

Voltage Mismatch Detect?

Yes

I0 0

Directly Calculate Vp and Vn

No

No Winding Insulation Fault

Fig. 2.

Flowchart of the voltage mismatch detector

signals. In order to retrieve the original waveforms, the 3phase voltage signals need to be demodulated, while a lowpass filtering can be simply applied to the 3-phase current signals. The demodulation of such PWM signals is commonly done in three stages: Firstly convert the PWM signal to pulse amplitude modulation (PAM) signal with an integrator, secondly apply a band-pass filter to the PAM signal and thirdly adjust the amplitude of the resulting signal by multiplication with the shaft rotational speed. Note that the frequency band of the band-pass filter is made adaptive to the shaft rotational speed as the center of the frequency band, with the bandwidth of 20 Hz. For low-pass filtering on the 3-phase current signals, the cut-off frequency is also adjusted with respect to the shaft rotational speed. In this study, the low-pass cut-off frequency was set to 10 Hz above the shaft rotational speed. The flowchart for signal processing is shown in Fig. 3. The comparison of 3-phase voltage and current signals before and after signal-processing are shown in Fig. 4. PWM Voltage Signal

Demodulation Integrator

C. Signal Processing As the PWM technique used in a variable frequency drive (VFD) affects the 3-phase output current and voltage signals, these signals therefore need to be preprocessed prior to applying the two aforementioned techniques. In practice, PWM time-waveforms are predominantly pronounced in the 3-phase voltage signals, but less pronounced in the 3-phase current

Eds. Leo J De Vin and Jorge Solis

Rotational speed

Current Signal

Band-pass Filter

Low-pass Filter

Multiplier

Filtered current signal

Demodulated voltage signal

Fig. 3.

Flowchart of signal processing

Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014

(a)

0

−10 0.2

(b) U−Phase V−Phase W−phase

0.25 (c)

0.3

H(x) =

0.25

0.3

0.25 (d)

0.3

Time (s)

0.25

0.3

Time (s)

Fig. 4. Signals comparison before and after processing: (a) Original current signal, (b) Original PWM voltage signal, (c) Low-pass filtered current signal, (d) Demodulated voltage signal.

D. Statistical Pattern Recognition for Health Assessment Statistical pattern recognition belongs to one of the many approaches for machine health assessment. The objective of applying pattern recognition is to determine the health status by measuring the similarity between the current (arbitrary) state features and the healthy state features. As the name suggests, the similarity metric is quantified by calculating the overlap between statistical distributions of features from both healthy and faulty states. When feature values are normally distributed, this overlap can be simply represented by the L2 distance. Yet when the feature distributions are non-normal, which is usually the case encountered in practice, a Gaussian Mixture Model (GMM) would be appropriate [15]. The GMM approach assumes that the non-normal feature data are generated from several different hidden sources, which can be described by Gaussian probability density functions with certain weights. Attributing to the virtue of normal distribution, the weighted component Gaussian functions having different means and variances are additive, and the resulting model is referred to as a ”mixture model”. Hence the feature values extracted under healthy states will be utilized to build a GMM model as a reference. If there are no faulty condition data available, another GMM model will be obtained from unsupervised learning on new feature values, and the distance between the current state and the healthy state can thus be measured using the L2 distance. The confidence value (CV ), which represents the normalized overlap between the two distributions, can be calculated according to Eq. (8) based on the L2 distance: CV =

pi h(x, θi ),

(9)

where h(x, θ) is called ”mixture”, namely the component probability density function (pdf) with parameter θi for signal x, and pi is the ”mixture weight”, namely the probability that observation comes from that component. G(x) can also be calculated by following the same fashion. After a threshold is set up based on expert knowledge and experience, the health state can thus be determined. If the faulty condition data are available, supervised learning can be performed, and the current machine health will be determined by choosing the ”closest” feature space [16].

0

−100 0.2

N  i=1

100

0

−10 0.2

arbitrary state. H(x) can be calculated based on Eq. (9): U−Phase V−Phase W−phase

0

−500 0.2

Voltage (V)

Current (A)

10

500

Voltage (V)

Current (A)

10

Eds. Leo J De Vin and Jorge Solis

H(x) ∗ G(x)L2 , H(x))L2 ∗ G(x))L2

(8)

where H(x) is the density estimation for a mixture distribution of the data collected under healthy state, and G(x) is that under

III. E XPERIMENTAL M ETHODOLOGY For experimental validation purposes, a dedicated induction motor setup has been developed and constructed. With this test setup, one is able to simulate the motor either in a healthy or faulty state, where two different types of winding fault namely (i) inter-turn and (ii) turn-to-earth faults can be induced. Note that turn-to-earth fault has not been considered in this study since it is much easier to detect. Besides, one can also easily control the rotational speed and the load applied to the motor such that experiments at different operating conditions can be realized. In the following sections, the test setup, the procedures of inducing winding faults and the experiments are described. A. Test setup Fig. 6 shows the photograph and the schematic view of the test setup. The setup consists of a 11kW-19.7A-400V-3-phase induction motor driven by a variable frequency drive (VFD). The shaft rotational speed of the motor can be varied from 0 to 3000 rpm with either a stationary mode or a transient mode (run-up/run-down). The motor shaft is connected to the shaft of a magnetic brake through a timing-belt and pulley mechanism, where the transmission ratio of 2 was chosen such that the rotational speed of the brake shaft is two times lower than that of the motor shaft. An external load (i.e. torque) to be applied to the motor can be varied from 0 to 50 Nm by controlling the input current to the brake. The control signals to the VFD and to the brake controller are sent out by a PC using dedicated Labview programs. A variable resistor with the resistance range of 0 - 580 Ω was used for inter-turn fault simulations as will be discussed in the following section. The 3-phase voltages and currents generated by the VFD are measured respectively by high frequency band-width 3phase current and voltage probes. The signals captured by the probes are conditioned by the corresponding signal conditioning module. A tachometer based on a proximity probe is used to measure the shaft rotational speed of the motor where the probe head is directed to a 4-tooth flywheel attached on the motor shaft, thus generating 4 pulse signal per revolution. To measure the actual torque applied to the motor, a torque sensor is mounted on the brake shaft. All the signals are

Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014

Eds. Leo J De Vin and Jorge Solis

Torque sensor

Timing belt

Variable frequency drive

Motor under test NI Compact DAQ

Test Motor 3-phase current 3-phase voltage probes probes Signal conditioning

Signal conditioning

Tachometer

Triaxial accelerometer

NI Compact DAQ

Magnetic brake

Variable resistor

Magnetic Brake

Geared pulleys

Variable frequency drive

(a)

Signal conditioning

Controller

PC

(b) Fig. 5.

Experimental setup: (a) photograph and (b) schematic view

synchronously acquired by a National Instruments (NI) data acquisition system and then stored to the PC with a Labview program. B. Fault Simulation and Test Procedure The stator winding of the motor used in this study is random wound, which means that there are no conductive bars with exact defined location within stator slots, as shown in Fig. 6(a). For this study, the test motor has been modified as follows. Three shielded wires (1, 2 and 3) are connected to the coil of the phase w stator winding at different locations and the other ends of the wires are brought outside as schematically illustrated in Fig. 6(b). This way, different scenarios of interturn fault can be simulated by means of connecting two of the shielded wires with a resistor. 1) Healthy State Simulation: To simulate a healthy state, the floating ends of the three shielded wires shown in Fig. 6(b) are kept unconnected. 2) Faulty State Simulation: In this study, fault simulations were carried out under two different scenarios, referred to as inter-turn fault I and II, as follows. To simulate inter-turn fault I, wire 1 (in orange) was shorted to wire 2 (in green) through a variable resistor as illustrated in Fig. 6(b). In similar way, wire 1 was shorted to wire 3 (in black) to simulate inter-turn fault II. Three different severity levels have been considered in this study for each scenario. These levels were adjusted by changing the resistance value of the variable resistor, namely 580, 300 and 50 Ω, as summarized in Table I.

3) Test Procedure: The motor was operated at a constant speed of 3000 rpm and a constant brake torque of 12 Nm for each condition. At an imposed degradation level (F1, F2, or F3), the current il flowing through the variable resistor was also measured and the corresponding dissipated power Pd was calculated as listed in Table II. TABLE II C URRENTS AND DISSIPATED POWER THROUGH THE VARIABLE RESISTOR AT DIFFERENT STATES .

State F1 F2 F3

Inter-turn I il [mA] Pd [W] 265 297 1126

40.7 26.5 63.4

Inter-turn II il [mA] Pd [W] 86 155 990

4.3 7.2 49.0

Prior to signal digitizing, each measured signal was lowpass filtered with an anti-aliasing filter embedded in each channel of the used NI data acquisition system. This way, potential aliasing problems resulting from high frequency noise can be avoided. With this data acquisition system, the cut-off frequency of the anti-aliasing filter is automatically selected depending on the used sampling frequency. Later on, the filtered signals were sampled at 102.4 kHz with a duration of 4 seconds. Finally, the digital data were stored in the PC and then processed off-line with dedicated Matlab programs as will be discussed in the next section. IV. R ESULTS AND D ISCUSSION

TABLE I D IFFERENT DEGRADATION LEVELS INDUCED IN THE MOTOR .

State

Resistance [Ω]

Comment

F1 F2 F3

580 300 50

Lowest level Moderate level Most severe level

A. Negative-Sequence Impedance Detection The magnitudes of the negative-sequence impedance have been calculated based on the method discussed in Section II for each inter-turn fault scenario in both healthy and faulty states under a constant operating speed of 3000 rpm and constant brake torque of 12 Nm. Fig. 7 shows the distributions of the negative-sequence impedance for all conditions. The

Count

Count

Count

Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014

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x 10

Eds. Leo J De Vin and Jorge Solis

(i)

4

2 0 5.6 4 5.7 x 10 4

5.8

5.9

5.8

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(ii)

6

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(iii)

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(a)

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B. Voltage Mismatch Detection A linear regression model was built for each speed regime using data collected from the healthy state. The regression coefficients were stored for each speed regime to build the library of z-coefficients. Then, those coefficients were used to calculate the positive- and negative-sequence voltages by Eq. (6) using data with the three conditions: healthy, interturn fault I and inter-turn fault II. Meanwhile, the voltage symmetrical components are calculated from the demodulated phase voltages according to Eq. (1).

5.8

5.9

(ii)

0 5.6 4 5.7 x 10 2

(iii)

1 0 5.6

5.7

5.8

5.9

(b)

Count Count

confidence value representing the health condition of the motor for different states is listed in Table III. It can be observed from the figure above that the magnitude of the negative-sequence impedance for healthy condition is larger than that for inter-turn fault I and inter-turn fault II. It is also obvious that the highest severity level F3 is more easily detectable than the other two fault levels. This observation is also reflected by the calculated confidence value (CV ) which is much smaller than that of the other levels (F1 and F2). As the fault severity level increases, the distribution is more shifted to the left indicating that the calculated negativesequence impedance decreases.

5.9

Impedance (Ohm)

Count

Fig. 6. (a) Photograph of the disassembled motor exposing random wound stator winding, and (b) the schematic winding diagram with three taps on the phase w winding for different inter-turn fault scenarios.

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(c) Fig. 7. Negative-sequence impedance distributions for (a) lowest severity level F1, (b) moderate severity level F2 and (c) highest severity level F3. Note that (i) represents healthy state, (ii) represents inter-turn fault I, (iii) represents inter-turn fault II.

Fig. 8 shows the histogram of the residuals of the negativesequence voltage calculated from the data collected in different states (including healthy, F1, F2 and F3). As shown in the figure, there is a clear difference between the distributions of healthy and the faulty states. The residual voltage of the healthy state exhibits a zero-mean Gaussian distribution, which is due to the uncertainties of the modeling process. However, the residual of the faulty states are more likely a combination of two distributions with larger variances, which is expected due to the change of the z-coefficients. As the fault severity level increases, the variances of the two (joined) distributions

Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014

Eds. Leo J De Vin and Jorge Solis TABLE III

become larger.

C ONFIDENCE VALUE (CV)

FOR IMPEDANCE DISTRIBUTION AND

P REDICTION E RROR (SPE) (i)

Count

Count

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4

x 10

Indicator

2 0 2

−20 4 −15 x 10

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−5

0 (iii)

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−15

(i)

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Residual Voltage (V)

(b) (i)

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Count

CV SP E

≈1 2.55

Inter-turn I F2 F3

0.85 3.23

0.72 3.55

0.06 6.12

F1

Inter-turn II F2 F3

0.16 4.32

0.15 5.06

4e-7 11.6

1

(a)

Count

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Residual Voltage (V)

Count

S QUARE

FOR VOLTAGE MISMATCH DISTRIBUTION .

4

x 10

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0 (iii)

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1 0 2

ACKNOWLEDGMENT

1 0

−20

−15

−10

−5

0

This paper presents a framework for early fault detection and diagnosis of stator winding fault (i.e. inter-turn fault) for 3-phase induction motors using two established on-line methods, i.e. negative-sequence impedance and voltage mismatch, which are all based on the symmetrical components. The two methods have been modified in this study by introducing an additional signal processing step for removing the effects of pulse-width modulation (PWM) on 3-phase voltage and current signals. This step is necessary for the 3-phase current and voltage signals collected from induction motors equipped with variable-frequency drive (VFD), prior to calculating the symmetrical components. The experimental results show obvious distinctions between healthy and faulty states using both modified negative-sequence impedance and voltage mismatch approaches. The L2 distance between the distributions of the negative-sequence impedance is proposed as an indicator that can easily capture the mean shift and variance change, while Square Prediction Error (SPE) is proposed as an indicator to quantify the voltage mismatch. Both the modified negative-sequence impedance and voltage mismatch detectors show encouraging results for winding fault detection even for incipient faults. It should be noted that both modified detection methods need a library of references under different environmental and working conditions, since the experimental results suggest that the modeling of motor windings do vary in different conditions.

5

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Residual Voltage (V)

(c) Fig. 8. Residual voltage distributions for (a) lowest severity level F1, (b) moderate severity level F2 and (c) highest severity level F3. Note that (i) represents healthy state, (ii) represents inter-turn fault I, (iii) represents interturn fault II.

V. C ONCLUSION Stator winding faults damage the symmetry of the winding resistance and hence cause unbalanced sequence currents. As a result, the equivalent sequence impedance will drop if short circuits exist (i.e. winding faults) in any phase of the windings. Based on this reasoning, the symmetrical components of 3phase currents and voltages can be used as effective tools for winding fault detection in early stages.

The authors would like to thank Aaron L´eon Hern´andez for setting up and performing the experiments during his internship at Flanders’ Mechatronics Technology Centre (FMTC), Belgium. R EFERENCES [1] P. V. J. Rodrguez and A. Arkkio, “Detection of stator winding fault in induction motor using fuzzy logic ,” Applied Soft Computing, vol. 8, no. 2, pp. 1112 – 1120, 2008. [2] A. Ukil, S. Chen, and A. Andenna, “Detection of stator short circuit faults in three-phase induction motors using motor current zero crossing instants,” Electric Power Systems Research, vol. 81, no. 4, pp. 1036 – 1044, 2011. [3] M. Hanif, “Principles and Applications of Insulation Testing with DC,” IEP-SAC Journal, pp. 57–63, 2005. [4] J. Sottile, F. C. Trutt, and J. L. Kohler, “Condition monitoring of stator windings in induction motors. II. Experimental investigation of voltage mismatch detectors,” IEEE Transactions on Industry Applications, vol. 38, no. 5, pp. 1454–1459, Sep 2002. [5] J. Kohler, J. Sottile, and F. Trutt, “Condition monitoring of stator windings in induction motors. I. Experimental investigation of the effective negative-sequence impedance detector,” IEEE Transactions on Industry Applications, vol. 38, no. 5, pp. 1447–1453, Sep 2002.

Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014

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