2014 IEEE International Conference on Industrial Technology (ICIT), Feb. 26 - Mar. 1, 2014, Busan, Korea
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Fault Diagnostics of Induction Motors Based on Internal Flux Measurement Khalid Saad and Galina Mirzaeva School of Electrical Engineering and Computer Science University of Newcastle, Callaghan, NSW 2308, Australia
[email protected],
[email protected]
Abstract—A new fault diagnosis scheme based on the monitoring of main air gap flux of squirrel cage induction motors is proposed. Most of the existing flux monitoring techniques are based on the leakage or stray flux measurement outside of the motor. A few methods, however, use the main air gap flux as the fault signature, where search coils are used to monitor the derivative of the flux, which eventually introduces noise in the signal. Moreover, the diagnosis methods are mainly based on detecting a fault, whereas very little initiative has been taken to locate a fault precisely. To address these problems, a sophisticated yet robust condition monitoring and fault diagnosis method is needed. To this aim, we propose to monitor the main air gap flux using Hall Effect Flux Sensors (HEFS) at all the stator slots of an induction motor, which can be used to address the stator and rotor slot effects not only through frequency analysis of the magnetic flux, but also by magnitude and phase shift comparison of sensors located at different geometric positions around the stator. We have successfully detected the stator turn-to-turn fault at a very incipient stage and detected the location of the fault precisely. Promising results have been obtained through simulation in the case of broken rotor bar faults as well. Keywords—Induction motor; Squirrel cage; Condition monitoring; Fault diagnostics; Internal flux measurement; Stator turn-toturn fault; Broken rotor bar fault
I.
I NTRODUCTION
Ever since the induction motor was invented in the late 19th century, it has been used extensively in different fields of industry. In developed countries, around 60% of the total industrial electrical energy consumption accounts for the energy consumed by induction motors [1]. Nevertheless, the use of induction machines are growing rapidly, eliminating the last remaining usages of their direct current counterparts, for several advantages e.g. robustness, being inexpensive than the other types of machines, easy maintenance, ranging from small (a few watts) to large (up to 10,000 hp) ratings etc. However, like all the other machines, induction machines are also subject to various electrical and mechanical faults. A fault on a component may be defined as the condition of reduced capability related to specified minimal requirements, and may be the result of normal wear and tear, poor specification or design, poor mounting (including poor alignment), wrong use, or a combination of these. If a fault is not detected, or if it is allowed to develop further, it may lead to a failure [2], in which case the faulty part of the motor is to be replaced or repaired before usage. Condition monitoring of induction
motors may be defined as a technique or process of monitoring the operating characteristics of a machine so that changes and trends of the monitored signal can be used to predict the need for maintenance before a breakdown or serious deterioration occurs, or to estimate the current condition of the machine [3]. Electromagnetic flux monitoring is one of the most effective electrical techniques for the purpose of condition monitoring and fault diagnosis of an induction motor. However, most of the existing techniques are based on the leakage or stray flux measurement outside of the motor. Although this method has been proven useful so far, there are some certain drawbacks of these methods such as external environmental dependency. Nevertheless, the research works for condition monitoring of induction motors using electromagnetic flux sensor as a signature is being carried out for quite some time. One of the first of such works was presented in [4] and [5], where a comparative analysis of flux, current and vibration monitoring based condition monitoring schemes are provided. A theoretical analysis of simplified axial flux spectrum is presented for both no load and rated load conditions in [6], followed by experiments using search coils to detect 1–6% the stator inter-turn short circuits of a 11 KW 3 phase 4 pole induction motor. The output of such a search coil is the Electromotive Force (EMF), which is induced and thus proportional to the derivative of axial leakage flux. It is to be noted that the derivative usually introduces additional noises, which can make the fault detection difficult. The advantages of stray flux monitoring using external flux sensors over the traditional current monitoring schemes are discussed in [7], where tests were done at the motor standstill, no load and rated load and comparison between current analysis and flux analysis have been presented. 6 turns were shorted for experiment and the low frequency spectrum comparison between current analysis and stray flux analysis shows that the fault signatures are more prominent in the flux analysis. The advantage of such a scheme is that it is indeed a low cost and noninvasive technique, as the sensor is needed to be placed outside the motor. Analysis of the ability of electromagnetic flux based condition monitoring to enhance the fault detection and localization accuracy in a 35 KW cage induction motor is presented in [8]. Although turn-to-turn faults can be detected at an incipient stage, the experimented broken rotor bar fault was introduced with 3 broken bars and a broken end ring and the degree of eccentricity was 40%, both of which are quite developed from the early stage of the corresponding faults.
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An arguable method for rotor vibration monitoring by using the air gap flux as a fault signature, which is measured by search coils, is presented in [9]. The search coils are said to be placed at maximum air gap and minimum air gap in case of static eccentricity, which is not very realistic due to measurement issues in reality. Moreover, the minimum and maximum air gaps rotate with the rotor in case of dynamic eccentricity. Another method based on the air gap flux is also presented in [10], where the positions of the flux sensors are not clearly mentioned. A time domain analysis to detect air gap eccentricity and broken rotor bar condition using radial leakage flux sensors is presented in [11], where it is claimed that there is no eccentricity in normal condition and hence the magnetic flux density remains constant. But in reality, there is always some static eccentricity present [12], which is due to slight misalignment of the rotor with respect to stator, which needs to be taken into account. Moreover, only the time domain analysis may be ambiguous in different circumstances and hence might cause false alarm. A comparison to detect the broken rotor bar fault detection techniques based on the stator current monitoring, axial leakage flux monitoring and instantaneous power monitoring is presented in [13]. A method based on the monitoring of main air gap flux to detect the stator turn shorting fault, broken rotor bar fault and bearing outer race fault is presented in [14], where the test were carried out only when the motor is at 100% of rated load. An eccentricity fault detection scheme based on monitoring the (fs ± fr ) harmonics of external magnetic flux spectrum is presented in [15]. The same authors extended their works to detect the bearing faults through similar procedure [16]. Broken rotor bar fault detection based on the analysis of external radial and axial leakage fluxes, concentrating on the lower harmonics zone of the magnetic flux frequency spectrum, in particular the sfs and 3sfs components is presented in [17]. Magnetic flux density analysis of a wound rotor motor is presented in [18]. Frosini et al. argues that Motor Current Signature Analyais (MCSA) can be an aid to monitor the condition of AC motors, but it may not be sufficient to detect all kinds of faults, e.g. stator winding fault [19]. A leakage flux monitoring based condition monitoring technique using external flux sensors is outlined, which can be used for all sizes of induction machines. The motor steady state condition is considered with a future view of considering the start and stop transient conditions. A method to differentiate between the rotor asymmetry condition and load-torque variation and thus suitable for the motor fed by drives based on the air gap magnetic flux measurement is presented in [20]. One search coil around one of the stator teeth is used to measure the air gap flux. The presented method is based on the displacement of the motor poles introduced by a broken rotor bar fault and analysing the zero crossing sequences and the time interval between successive zero crossings in the magnetic flux density waveform. It is stated that if the zero crossing intervals are constant, the motor has said to be at good rotor condition and if a periodic oscillation is found around the mean value of ½fs , the motor is said to have a rotor with a broken bar. Since the design of the stator winding of the low voltage
motors is ‘random wound’, where each turn could be placed randomly against any other turn in the coil regardless of the voltage level of the turn, the voltage across the turn insulation can range up to the rated phase-to-phase voltage of the stator. Such a turn insulation fault in a low voltage motor of 1.5 KW power rating is analysed in [21], where the effectiveness of the flux monitoring was compared to the conventional MCSA based methods. Low pass filter was used in the analysis to attenuate all the frequencies above the maximum considered harmonic, thereby reducing the additional noise. The test was carried out for both no load and rated load and the flux analysis is proved to give better results than MCSA at no load condition. The experimented shorted turn was 5% of the whole turn. However, the motor case was thicker than the standard, for it to be explosion proof, which made axial flux very little in amount and thus it was needed to be amplified, which also amplified the noise. A combination of external axial and radial magnetic field has been analysed focusing on the lower frequencies of the magnetic flux density spectrum to detect the broken rotor bar fault and eccentricity fault in [22]. A theoretical analysis of the faults based on permeance circuit model was provided and verified by experimental results. The analysis is based on the slip, for which the determination of rotor speed is necessary. Thorough research works on induction motor fault diagnosis by electromagnetic flux monitoring can be found in [12] and [23]. The rest of the paper is organized as follows: our proposed HEFS based main air gap flux monitoring scheme is described in section II, the experimental setup is briefed in section III, section IV shows and analyses the results of the stator turnto-turn fault and broken rotor bar fault and finally the paper concludes in section V with future work directives. II. P ROPOSED C ONDITION M ONITORING T ECHNIQUE BASED ON THE M AIN A IR G AP F LUX M EASUREMENT The main air gap Magnetomotive Force (MMF) hence the magnetic flux is one of the most important characteristics of the induction motor. It is the electromagnetic bridge between the stator and the rotor and does the actual energy conversion from electrical energy to mechanical energy. The stator current frequency spectrum is often mentioned as the electrocardiogram of an induction machine [24]. However, the stator current harmonics, those are mainly used in the electrical fault analysis techniques such as MCSA, in fact depend on the magnetic condition of the main air gap. The stator and rotor slot effects are first created in the main air gap, which eventually creates important harmonics in the main air gap itself as well as in the stator current. That is why, although the stator current is a similar signature of the main air gap flux, measuring the main air gap flux is a more effective idea as a condition monitoring approach than measuring the stator current. Another advantage of analysing the main air gap flux is the fact that the changes in the signature at different slot positions can be directly monitored, which is not possible in the case of MCSA. This advantage will be further detailed in the next section, where the experimental setup will be described. The stator turn-to-turn fault can be detected and
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located successfully using this technique, which is already proven through simulation and experimental results. In the existing literature on condition monitoring of induction motor, emphasis was given only in the time and/or frequency analysis. To the best of the authors’ knowledge, the fault diagnostics that uses measurements around the entire air gap circumference has not been done yet. The main air gap flux can be measured by using search coils or Hall Effect Flux Sensors (HEFS). Due to some certain advantages of the HEFS over search coils described as follows, we propose to use HEFS sensors to measure the main air gap flux. One possible disadvantage of the main air gap flux measurement might be fitting sensors in a small air gap, reliable attachment of sensors, routing of the sensor signals out, etc. These difficulties have been addressed in recent works by G. Mirzaeva et al. [25], where the main air gap flux measurement was successfully used for diagnostics of large industrial DC motors. It is to be noted that large industrial machines typically have main air gap size from 2 to several mm. The flux density inside main gap is usually around 1T. Consequently, the size of a flux sensor should be small enough to fit in the air gap with some clearance. On the other hand, the sensor should be able to provide accurate measurement within ±1T range. Cabanas et al. use search coils to measure the main air gap flux [20], where voltage e induced in a search coil is given by Faraday’s Law: dϕ dB = NA (1) dt dt Where, N is the coil turns number, A is area enclosed by the coil, ϕ is the air gap flux and B is the air gap flux density. According to (1), voltage measured by a search coil is proportional to the derivative of the quantity of interest (flux density), which may potentially cause some noise problems. The more prospective sensor type is a miniature HEFS, which directly measures the magnetic flux density B. The latter would require an accurate calibration before the use. In a more recent work [26], G. Mirzaeva et al. proposed a method to calibrate a flux sensor with high accuracy. With the proposed calibration system, the air gap flux density of 100mT to over 1T can be measured with accuracy ±3%, potentially up to ±1%. Therefore, practical difficulties associated with main air gap flux measurement by using HEFS sensors have been presently successfully addressed and, to a large degree, overcome.
Fig. 1: Complete experimental setup
e=N
III.
E XPERIMENTAL S ETUP
To run the motor at different load-torque levels, it is coupled with a DC motor on the same test bed (Fig. 1). The induction test motor can be connected to a clean supply from the mains or controlled by an AC drive based on an IGBT inverter. The nameplate data of the test motor are presented in Table 1. In the experiments, MLX90251 HEFS sensors by Melexis [27] were used to measure the main air gap flux. The positions of these sensors in the stator are shown in Fig. 2. The test motor the main air gap of only 0.38 mm. To fit 1 mm high HEFS with capacitors and wires, two grooves of 1.5 mm depth were machined in the stator. This slightly altered the
Fig. 2: Positions of the Hall Effect Flux Sensors magnetic flux being measured. However, the small scale motor instrumentation was only needed as a proof of concept. With large industrial motors, for which the condition monitoring tool is being developed, the air gap length is sufficient to fit the sensors. Two sensors are machined in every slot of the stator, one at the front and the other at the rear, yielding a total of 72 HEFS sensors. A MATLAB simulation model of the induction motor focusing on the main air gap flux was developed and the broken rotor bar fault was simulated with it. IV. R ESULTS AND A NALYSIS A. Experimental results of the stator turn-to-turn fault Stator winding fault is one of the most severe and fast acting faults, that can happen to an induction motor. Our
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Fig. 3: Introducing turn-to-turn faults by external switches tests for winding insulation faults included turn-to-turn and coil-to-coil short circuits within the same phase. To preserve integrity of the stator winding, three extra turns were laid on top of the existing winding of phase A as shown in Fig. 3. These extra turns were then sequentially shorted introducing 1, 2 and 3 turn(s) short circuit within the same phase. These artificial faults correspond to actual stator winding faults, which typically start with one turn-to-turn fault and then propagate to include more turns. Short circuits between phases and phase-to-ground are picked by protection devices and are not included in this study. It is to be noted that 1 shorted turn corresponds to 1/100 of a phase coil, and each phase includes 3 such coils, therefore, losing one turn will have almost no effect on the total air gap MMF. However, the short circuit current circulating in the shorted turn(s) will produce disturbance to the air gap MMF located near slots corresponding to the affected turn(s). Since we are using HEFS sensors at different slot positions, the small disturbance in the flux density can be located by comparing the flux density values of all the sensors with a faulty case and the good stator case. The short circuit test was experimented for both the clean supply and inverter fed case. The stator fault related harmonics are usually load dependent, which is given by [28], n fst = fs (1 − s) ± k (2) Pp Where, fst are the fault signature frequencies, fs is the supply frequency, n = 1, 2, 3, ..., k = 1, 3, 5, ..., Pp is the pole pair number and s is the slip of the rotor. Some of the fault signature harmonics given by (2) may coincide with harmonics due to unbalanced supply or rotor eccentricity. Identification of such harmonics is typically difficult and is further complicated by an inverter supply. Using the HEFS measurement method changes this situation dramatically. Fig. 4 shows the magnitude changes of the measured flux density, around the entire circumference of the motor air gap using HEFS sensors, as compared to no-
Fig. 4: HEFS measurement changes to detect and locate stator faults; clean supply (top), inverter fed (bottom) fault conditions. Colors of the waveforms are 1 turn – blue, 2 turns – green, 3 turns – red. It is clearly seen that immediately next to the fault the flux density significantly deviates from its no-fault magnitude and the deviation is proportional to number of turns, i.e. fault severity. The two HEFS adjacent to the fault on both sides show deviations of an opposite sign, due to an attempted compensation of the disturbance associated with the fault. It is well known that fault diagnostics of inverter-fed AC motors can be difficult as compared to AC motors fed from direct supply. This is because of high level of noise injected by the inverters. With the proposed method of stator fault detection, the inverter supply proves to have little no effect on the sensitivity to faults. This is readily observed from Fig. 4 where the flux density difference for clean supply (Fig. 4a) and for inverter supply (Fig. 4b) are almost identical. It is also clear that the proposed method allows not only to identify a fault but also to accurately localize it within the stator winding and to quantify it (i.e. 1, 2 or 3 turn(s) fault). It is to be noted that 1 turn is only 0.17% of a phase winding, which can be successfully detected and located with the help of our proposed method.
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Fig. 6: The flux difference between good motor and motor with a broken rotor bar at different times
Fig. 5: Comparison of the main air gap MMFs in case of good rotor (top) and broken rotor bar (bottom) B. Simulation results of the broken rotor bar fault Detecting the broken rotor bar is one of the most crucial and ambiguous problems among the induction motor faults due to its direct dependency on the slip of the rotor. The classical MCSA method usually relies on monitoring the ‘twice-slip’ frequencies in the current spectrum to detect the presence of the broken bar fault, given by [29], fbrb = (1 ± 2ks)fs
that of a motor with a broken rotor bar (Fig. 5, lower plot) can be used as a fault signature, not only to detect the fault but also to locate the broken bar. This ‘anomaly’ caused by the broken rotor bar travels with the rotor. Since we are using sensors in all the stator slots, we believe that this anomaly can be tracked if the sensors are sophisticated and well calibrated. The difference between the good rotor flux and a broken bar rotor flux at different time instants (assuming that the rotor is rotating counter clockwise) are shown in Fig. 6. The rotor bar flux is not measured in isolation but as a part of the main air gap flux. However, the traveling pulse associated with the broken rotor bar has a magnitude detectable by the HEFS sensors. As follows from Fig.6, the peak magnitude of such a pulse can reach 10% of the peak air gap flux magnitude. This simulation result shows that the broken rotor bar can not only be detected, but also located using the HEFS sensors by our proposed method.
(3)
where k = 1, 2, 3, ... However, for low values of slip, identifying the fault frequencies may be difficult and requires rigorous signal processing. Our proposed method is completely different from the existing schemes. In a squirrel cage induction motor, the stator MMF induces current in the rotor bars at a lagging power factor and this current produces the rotor MMF. In other words, the rotor ‘sees’ a sampled version of the stator MMF wave, which is lagged from the stator MMF by the torque angle. In the case of a broken rotor bar, there will be apparently no change in the stator MMF, whereas the rotor MMF will be changed. The broken rotor bar acts as an open circuit, so no current is induced in the broken bar and hence, the sampling of the stator flux to the rotor bars will be disturbed. The broken rotor bar does not contribute in the rotor MMF. The instantaneous MMF waves for a good rotor bar and a broken rotor bar is presented in Fig. 5. It is to be noted that the flux density values shown in the figures are arbitrary. The ‘anomaly’ created by the broken rotor bar is evident from Fig. 5. As the main air gap flux is being measured in our proposed method, the magnitude of the air gap flux at different stator slot helps to identify the rotor fault. The difference between the flux of a good motor (Fig. 5, upper plot) and
V.
C ONCLUSION AND F UTURE W ORK
In this paper, we proposed a new technique for condition monitoring of induction motors by monitoring the main air gap flux with the help of well-calibrated Hall Effect Flux Sensors. We argued that the HEFS sensors are better to measure the main air gap flux than the search coils, because search coils measure the change in the air gap flux where as HEFS measures the flux directly, thereby giving better signal-to-noise ratio. We presented the results of stator turn-toturn fault detection experiment and broken rotor bar detection simulation. In both cases, the proposed method proves its effectiveness. In the case of the stator fault, the method is able to detect as incipient as 0.17% short of the total winding, to locate the position of the fault precisely and to detect the severity of the fault. In the case of broken rotor bar fault, simulation results show prospective result of detecting the fault and locating the faulty bar as well. In future, experiments on broken rotor bar detection will be carried out. Other faults such as bearing fault and eccentricity will also be simulated and experimented with the proposed method.
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6 Parameter Manufacturer Rated power Rated voltage Rated current
Table I.
Value Monarch (TECO) 11kW 380 - 415V 21.2 - 20 A
Parameter Rated speed Number of poles Connection Cos ϕ
Value 1465 RPM 4 Delta 0.85
[15]
[16]
NAMEPLATE DATA OF THE TEST MOTOR [17]
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