New Fault Detection Techniques For Induction Motors

Transcription

1 Electrical Power Quality and Utilisation, Magazine Vol. II, No. 1, 2006 New Fault Detection Techniques For Induction Motors Jordi CUSIDO 1 ; Javier ROSERO 2, Emiliano ALDABAS 2, Juan Antonio ORTEGA 2, Luis ROMERAL 2 1) Asea Brown Boveri, Barcelona, Spain 2) Universitat Politècnica de Catalunya, Spain Summary: Double frequency s are used for evaluating stator windings analyzing the temperature. Likewise, signal injection on induction machines is a well-known technique on sensorless motor control fields to find out the rotor s position. Motor Current Signature Analysis (MCSA) is the most widely used method for identifying faults in induction motors. MCSA focuses its efforts on the spectral analysis of stator current. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics respectively. In both cases, the problem of frequency accuracy is very tricky since the sideband harmonic is close to the fundamental harmonic. This paper proposes injecting an additional voltage into the machine under at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies. Key words: induction motor, fault detection, motor current signature analysis 1. INTRODUCTION Induction motor drives are the most widely used electrical drive systems, and typically consume more than 50% of an industrialized nation s total generating capability. Among the different techniques for fault detection in induction machines, MCSA is one of the most widely used. MCSA focuses its efforts on the spectral analysis of the stator current and has been successfully used in the detection of broken bars. Typically the procedure consists in evaluating the relative amplitude of the current harmonic f 1 s brb = f1 m ± s p 2 (1) where m = 1,2,3, is the harmonic order. If the amplitude of these harmonics with regard to the amplitude of the main harmonic at f 1 is lower than a limit value then the machine is considered healthy, otherwise, a fault condition can be assumed. The classical method of analysis with the MCSA uses the first harmonic, finding the fault near f 1 (1±2s). Other studies propose evaluating the side of the fifth harmonic, finding the fault near f 1 (5 4s) and f 1 (5 6s). Other specific faults can be found by using the same method, such as the bearing damage [1] and the dynamic eccentricity [2], caused by a variable air gap due to a bend shaft or thermal bow. Another usual fault is the static eccentricity, which is due to a stationary minimum air gap caused either by stator core deformation or oval shape, or by incorrect positioning of the rotor or the stator. The frequencies related to different faults in the induction machine, such as air gap eccentricity (Fig. 1), and the effect of bearing damage, are expressed by equations (2) and (3) respectively, being f i the rotational speed frequency of the rotor. The equation (3), however, requires the rotor slots distribution and, hence, a good knowledge of the construction and bearing parameters of the machine. Finally other studies group all the motor faults [3] [4] together, typifying the different harmonic effects depending on the fault. 1 s = ± fecc f 1 1 m p = ± (2) fairgap f1 mfi n fi,o fr 1 bd = cosβ 2 ± pd (3) The method described works well under constant load torque and with high power motors, but some difficulties appear when this method is applied to medium and high power engines which are working at low speed or low load torque. J. Cusido et al.: New Fault Detection Techniques For Induction Motors 39

2 can be insufficient to give a good sideband harmonics detection. The consequence of the former conside-rations is the following: if the reference magnitude of the sideband harmonics cannot be accurately measured in a healthy induction machine, it is not possible to detect the failures of the machine using a standard MCSA and a simple stator current sensor. In these cases, it is necessary to improve the technique by artificially exciting new harmonics without torque quality deterioration, and evaluating their magnitude and location for proper motor fault detection. Fig. 1. Stator current spectrum of induction motor with rotor eccentricity. Base frequency of 50 Hz Fig. 2. Stator current spectrum of induction motor with broken bars. Base frequency of 50 Hz In case of low power machines, the amplitude of the fault harmonic is too small in the incipient faults and the relationship signal-noise is very low, and thus the failure becomes difficult to identify. On the other hand, the signal processing needed by a method, such as the FFT (Fast Fourier Transform), is difficult to apply even in high power induction machines. In this case, the slip is very low, the sideband harmonics are close to the fundamental harmonic and the frequency resolution must be high. Since the accuracy is inversely proportional to the data acquisition time, problems with the meaning of the spectrum occur. The problem of frequency accuracy is very tricky since the sideband harmonics are close to the fundamental harmonic in both cases, with low torque operation of the power induction machines because the slip is very small, and at low speed of the machine because the fault frequency separation from the fundamental harmonic depends directly on the speed. In the case of electrical drives, the problem is much more complicated since the fundamental frequency is not fixed, and it can in fact reach very low values. Then, the frequency resolution 2. PRINCIPLE OF OPERATION OF MCSA Motor Current Signature Analysis (MCSA) is a condition monitoring technique that is now going to be widely used to diagnose problems such as broken rotor bars, abnormal levels of airgap eccentricity, short circuits in stator windings, and other mechanical problems. The idea is that stator current contains components directly related to rotating flux, caused by faults by broken rotor bars, eccentricity, etc. Despite the fact that motor failure can be detected by mechanical vibration spectrum analysis, the vibration is a second order effect compared to current components, and in many cases the severity of the fault [i.e., the number of the broken rotor bars] must be high so as to be detected by vibration analysis. Instead, MCSA has been ed in many industrial cases with good results since the late 80s. The rotating magnetic field induces voltages and currents in the rotor at slip frequency, which produce an effective three-phase magnetic field rotating at slip frequency with regard to the rotating rotor. Symmetrical cage winding Þ only forward rotating field Asymmetric rotor Þ there will be a resultant backward rotating field at slip frequency with regard to the rotor. This backward rotating field induces a voltage and current in the stator winding at fsb = f1(1± 2 s) Hz (4) It means that twice slip frequency sideband is due to broken rotor bars. A torque pulsation appears at twice slip frequency 2sf 1, and a corresponding speed oscillation, function of the drive inertia. The 40 Electric Power Quality and Utilization, Magazine Vol. II, No 1, 2006

3 speed oscillations may reduce the magnitude of f 1 (1 2s) sideband amplitude, but an upper sideband of f 1 (1+2s) is then induced in the stator winding. These are the classical twice slip frequency sidebands due to broken rotor bars (Fig. 2), although other frequencies appear around the upper harmonic, especially in the fifth (Fig. 3), as the equation (1) shows. However, the sidebands can vary due to: Mechanical load that affects the slip frequency Supply frequency, which is different from the inverter. Then, the MCSA application must take these variations into account. The effects caused by a damaged motor with both rotor eccentricity and broken rotor bars on the sideband frequencies around the fifth harmonic are shown in Fig. 3. The motor is running at a slip frequency of 5% and at full load. As it is shown, the effect of rotor eccentricity (frequency inside green oval) is greater than the effects of broken bars (frequencies inside red ovals). All the previous figures have been obtained with the motors directly supplied by the power grid. In the case of electrical drives supplied by power inverters, the problem becomes more difficult, since the fundamental frequency is not fixed, and can reach very small values. Moreover, the switching frequency of the converter introduces additional harmonics that make the localization of the faulty frequencies on the current spectrum more difficult. These effects can be clearly demonstrated by the experiments obtained from the motor supplied by a standard inverter. Figure 4 shows the current spectrum of a faulty motor running at full load and slip frequency of 10%. Red and green ovals indicate faulty frequencies due to broken bars and rotor eccentricity respectively. On the other hand, it must also be pointed out that any information about the motor state can be extracted from the side band frequencies around the fifth harmonic. Despite the problems previously described, in the case of small induction motors it is easy to detect not only the magnitude of the side band harmonics, but also the faulty frequency with an accuracy as low as 0.2 Hz, since the slip is relatively large (5% to 10%) and the motor is usually working near the nominal load. However, in the case of large power induc-tion motors the slip is small (often lower than 2%), and it is difficult to reach a proper detection of the side band harmonics without increasing the frequency accuracy, and then the data acquisition time. Moreover, medium and large a) b) power motors working with applications of fluid control like air conditioners and water control often operate at medium power or even less, which implies very slow slip frequencies, and hence quite close harmonics in the side band spectrum, hindering the task of localizing and identifying faulty harmonics. In these cases of special difficulty to identify the harmonics, a double solution could be attempted: Using a high order analogue filter to separate the main frequency (or its harmonics) from the faulty frequencies, and then capturing and processing the sideband harmonics. Capturing the whole band around the main frequencies and then applying a mathematical algorithm to identify the faulty frequency. The first method implies a complex filter, which is difficult to implement due to the proximity between the frequencies in the band. On the other hand, the second solution also presents problems due to the dynamic range of the A/D converter, which should be adjusted Fig. 3. Stator current spectrum of a damaged induction motor: fifth harmonic sideband: a) fifth harmonic sideband spectrum; b) fifth harmonic sideband spectrum: details J. Cusido et al.: New Fault Detection Techniques For Induction Motors 41

4 Fig. 4. Stator current spectrum of a damaged induction motor driven by a power inverter. Base frequency of 50 Hz to the maximum excursion of the main current component, being then difficult to capture the harmonic component with the necessary accuracy. Both solutions lead to complex and expensive electronics, which hinder their application to a low cost industrial instrument. 3. PROPOSED METHOD The proposed method is a modification of the MCSA and can be useful in low power induction machines and small stator currents. Also, the method could be applied to high power induction machines with small slip frequency to simplify and to reduce the price of the data acquisition system. The basis is simple: applying a sinusoidal and three-phase signal on the stator for a limited time, and amplitude small enough so as to avoid undesirable effects in the normal operation of the machine, and then studying the sideband harmonics around the new frequencies that appear due to the signal composition between main and injected frequencies. It is possible to determine these new frequencies that appear due to a composition between the different frequencies. Obviously the serial transformer employed to inject the signal as a complex system has been studied, being careful with different effects as hysteresis effects on the magnetic nucleus and winding effects. Due to induction effects we expect to see the main frequency and the auxiliary frequency injected. However, as a contribution of the magnetic nucleus and the iron s hysteresis two additional compositions appear, defined by the following equations (5); f = 2 f + f f 2 = 2 f + f1 (5) c1 1 c On the other hand, it is possible to determine the effect of broken rotor bars in the motor s current spectrum, if the flux composition into the stator and the mechanical composition of frequencies like a speed composition have been studied. In the stator there are different magnetic fields due to the different signal injections. If the different fields are considered like different wheels moving around themselves with different angular speeds, relative speeds between one and the others will appear. Apart from this, if we introduce the rotor, with its mechanical speed, then it will be easy to define the different relative speeds between the rotor and all the different stator fields. Furthermore, obviously if the rotor has saliencies such as broken bars, this will have an effect on stator currents as an image. The relation equations between rotor currents and stator currents in an induction machine establish one as an image of the others. In an ideal induction machine, all the different current distributions will be sineshaped like the fields, but there are many effects that cause non-idealities. Moreover, the broken rotor bars is a non-ideal effect and will cause some marks in the current spectrum and around the different injected signals. To determine these different marks it is necessary to study the composition of the different frequencies, the different magnetic fields induced in the machine, and the relative speed between one and the others. The faulty mark in the current spectrum is defined by the equation (4) under the first harmonic. It is possible to refer to the equation under the injected frequency if the magnetic and mechanical relation speeds are considered. In the motor there are different flows and it is possible to establish a relationship between flows, rotor angular speed and marks caused by a rotor fault such as broken bars Considering the rotor frequency as in (6): n = f1 s (6) it is possible to determinate the synchronic speed of one frequency signal, as in the equation (7): n s f = p 2 (7) Immediately it appears a relationship between frequency and rotor s frequency as n s n. Defining electrical speed as function of pair of poles and harmonic number i, p n s 2 i ( n ) (8) 42 Electric Power Quality and Utilization, Magazine Vol. II, No 1, 2006

5 Under the stator s point of view it is necessary to add rotor s speed (relative speed between stator and rotor): ( ) p ns n pns p + n= + n 1 i i i (9) which can be expressed as electrical speed: s ( 2) pn + n i p / ( 1 )( 2) f + n s i p/ According to the first harmonic it is possible to find the equation, due to the fault s mark under the spectrum in the frequency side band as: Fig. 5. Flow density for 50Hz frequency (( ) ) f fault = f + f1 1 s ± ps/ 2 (10) A different injected frequency will produce different effects on the motor; several papers [6] introduce to the injection theories for sensorless control motors. These references consider the motor as a band-pass. In order to see this, it is possible to simulate the flow density of current and field on the stator and squirrel cage, using a software simulator and introducing rotor and stator design, and the frequency. Figure 5 shows current flow density for 50Hz frequency and Figure 6 shows current flow density for 200Hz frequency, both for the same voltage amplitude of the signal. Having a look at the figures we can see that for 200 Hz frequency there is a bigger current density, which confirms the idea that the motor could be considered as a band-pass with 200 Hz of central frequency of the band. Then, in order to take advantage of this behavior, we will inject the frequency as close as possible to 200 Hz. The experimental rig is based on the Normative EN , whose block diagram is shown in Figure 7. In the figure, the main generator represents the main supply of the motor, since the auxiliary supply supplies the variable amplitude and frequency voltage. This auxiliary voltage has amplitude of about 30% of the main voltage, and a frequency of about 75% to 125% of the main frequency. New current harmonic components result from the rotating magnetic fluxes compositions [7], which are supplied by equations (5). f = 2 f + f ; f 2 = 2 f + f1 (11) c1 1 c Then, a new sideband harmonic appears around these new frequencies, with specific faulty frequencies supplied by a 4 poles motor (12). f = f + 2f 1± s (12) fault 1 ( ) being p pairs of poles. Then, for p =2, f = f ± 2sf (13) fault c 12, 1 Although the amplitude of these new components is quite small, the ratio between the faulty frequency and the generating Fig. 6. Flow density for 200 Hz frequency Fig. 7. Experimental set-up for the injection of the signals on the motor J. Cusido et al.: New Fault Detection Techniques For Induction Motors 43

6 Fig. 8. High Band Current Spectrum at nominal speed. Healthy motor Fig. 9. High Band Current Spectrum at nominal speed. Faulty motor To detect a fault, the power of one of the sidebands around the frequency is monitored during a long time, looking for specific harmonic amplitude increment. If such harmonic increment appears but the load torque has not changed, then the fault will be detected. Compared with the standard MCSA method, the only drawback is that it is necessary to generate and apply the signal to the stator phases. The measurement of the current phases was already being applied in the MCSA method and for control purposes as well. The selection of the signal frequency is an important matter. Several studies introduce the idea of the signal injection into the supply voltage for sensorless control [6]. Here, it is suggested to apply these studies and results to the problem of detecting broken rotor bars and other motor faults. The selection of the frequency is a trade-off between several concerns. The carrier frequency must be high enough to create a deep bar effect that prevents the high frequency flux wave from substantially linking to the rotor bars, but it must also be low enough so that the skin effect in the rotor laminations does not repel the flux from penetrating below the rotor surface. In a practical case, a low pass filter model of the machine can be proposed, with the pole frequency in 400 Hz, hence the fact that the interaction between main and signal frequencies has to cause new harmonic components lower than this value to get good results. Fig. 10. High Band Current Spectrum in db at nominal speed. Faulty motor frequency is greater than the ratio found in the standard components used in the classical MCSA. Therefore, the proposed method consists in capturing and analyzing these new current spectral components which appear due to the signal composition between main and frequencies. 4. EXPERIMENTAL RESULTS Several s have been carried out taking the former considerations into account, which validate the idea of using auxiliary voltage signal and analyzing the side band harmonics for the detection of a faulty induction motor Case 1: motor running at nominal speed Main Supply, V phase = 230Vrms f = 50Hz Test voltage V phase = 70Vrms f =75Hz n = 1398 rpm I = A Figures 8, 9 and 10 show the current spectra with no fault and the case with broken bars, showing the frequencies around the side bands due to flux compositions. As it is clearly shown in the figures, new components appear at frequencies done by the equation (13), which are now: f = 2 f + f = = 175Hz c1 1 f = 2 f + f = = 200Hz c Electric Power Quality and Utilization, Magazine Vol. II, No 1, 2006

7 Moreover, faulty frequencies appear in fig. 9 due to broken rotor bars, the most important component being at a frequency of: f fault = fc 1 2sf1= Hz 1500 These components are good indicators for detecting faults in the motor. Once the side band to analyze has been located, the high ratio between faulty frequencies and reference harmonics in the side band makes acquisition, signal processing and identification of the faults in the motor easier Case 2: motor running at low speed Main Supply, V phase = 90Vrms f = 18Hz Test voltage V phase = 30Vrms f = 50Hz n = 418rpm I = A The is now performed with the motor running at low speed, and at a frequency of 18Hz, farther from the fundamental frequency than in the previous case. The results are shown in figures 11 and 12. The faulty components are also clearly shown in Fig. 12, and their value regarding the frequencies read in a healthy motor in the same side band is high enough to be easily measured and identified. Fig. 11. High Band Current Spectrum at low sped. Healthy motor 5. CONCLUSIONS Experimental results corroborate the main aim of the paper, and demonstrate the objective of locating and identifying new current components that appear as a consequence of motor failures, namely broken rotor bars, but also eccentricities and other faults. The location of these components depends on the frequency of the auxiliary voltage injected in the stator windings, and also on the motor load, which determines the slip frequency of operation. These new components are more evident and easier to measure than the faulty frequencies used in standard MCSA. Mention for VVVF converter supply: Although the injected voltage was obtained from an auxiliary generator through a serial transformer, there is no problem to generate a composed three-phase sine wave with the desired frequency by using a special modulation reference in the Space Vector Modulation block of the power inverter. For a practical implementation in industrial equipment, the frequency signal should be higher than the bandwidth of the current loop, especially when vectorial control is applied to IM. In that case, the choice of frequency signal will be the same as in sinusoidal application, more or less on the Hz band. In order to allow subharmonics due to the modulation we introduce a reactance high-pass filter between the drive and the VVVF converter, which cuts subharmonics due to an asyncron modulation. In this way, and considering that most modern systems include their own internal current sensors, the technique presented is promising for simple usage in modern fault detection equipment for induction machine drivers, although new experiments should be carried out using standard inverters instead of AC generators. ACKNOWLEDGMENT The authors would like to acknowledge the economic support received from the Ministerio de Ciencia y Tecnología de España (Spanish Ministry of Science and Technology) for carrying out this work under the DPI Research Project. Fig 12. High Band Current Spectrum at low sped. Faulty motor J. Cusido et al.: New Fault Detection Techniques For Induction Motors 45

8 REFERENCES 1. Alford T.: Motor Current Analysis and its Applications in Induction Motors Fault Diagnosis. ENTEK IRD International Corporation Cameron J.R., Thomson W.T., and D o w A. B. : Vibration and current monitoring for detecting airgap eccentricity in large induction motors, IEE Proceedings, pp , Vol.133, Pt. B, No.3, May Mohamed El Hachemi Benbouzid: A Review of Induction Motor Signature Analysis as a Medium for Faults Detection, IEEE Transactions on Industrial Electronics, Vol. 47, 5, Oct 2000, pp Thomson W.T., and Fenger M.: Case histories of current signature analysis to detect faults in induction motor drives, IEEE International Conference on Electric Machines and Drives, IEMDC 03, Vol. 3, pp , June Bachir S., Tnani S., Champenois G., and Trigeassou J.C.: Induction Motor Modelling of Broken Rotor Bars and Fault Detection by Parameter Estimation, IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives SDEMPED 2001, pp , Goritzia, Italy, September H o l t z J.: Sensorless Position Control of Induction Motors: an Emerging Technology, IEEE Transactions on Industrial Electronics, Vol. 45, nº 6, Dec 1998, pp Kostenko M., and Piotrovski L.: Electrical Machines. MIR Publishers, Moscow, Jordi Cusidó was born in Sabadell, Spain, in He received the Master engineering degree from the Technical University of Catalonia (UPC), in He has industrial experience in electro-mechanical engineering and is currently engineer and research consulter for ABB Automation Products SA, motor division. Javier A. Rosero García was born in Potosi (Colombia). He received the degree in Electrical Engineering from the Universidad del Valle, Cali, Colombia, in He is currently pursuing his Ph.D. degree at the Technical University of Catalonia (UPC), Terrassa, Spain. His research interests are focused on the areas supervision, diagnosis and control of electrical machines. Emiliano Aldabas was born in Teruel, Spain, in He received the engineering degree and Ph.D. degree from Universitat Politècnica de Catalunya (UPC), in 1992 and 2002, respectively. He joined the Electronic Engineering Department (DEE) of the UPC in 1993, where he first was an Assistant Professor. In 1998 he became Lecturer of the DEE. His research interests are power electronics, modulation, current controllers, adjustable-speed drives and highperformance drive systems. He is specially interested in the area of hysteresis current controllers for power inverters, where he has authored several technical papers. Dr. Aldabas is a member of the IEEE Industrial Electronics, IEEE Power Electronics and IEEE Education Societies. Juan A. Ortega received the M.S. Telecommunication Engineer and Ph.D. degrees in Electronics from the Universitat Politècnica de Catalunya (UPC) in 1994 and 1997, respectively. In 1994, he joined the UPC in the Department of Electronic Engineering, as full time Associate Lecturer teaching courses of microprocessors and signal processing. In 1998, he obtained a tenured position as an Associate Professor in the same UPC department. Since 2001 he belongs to the Motion Control and Industrial Applications research group. His current R&D areas include: Motor diagnosis, motion control, signal acquisition, smart sensors and embedded systems. Dr. Ortega is a member of the IEEE, the Institute of Electrical and Electronics Engineers. Luis Romeral was born in Asturias, Spain, in He received the MEng and the Ph.D degrees in electrical engineering from the Universitat Politècnica de Catalunya, (UPC) in 1985 and 1995 respectively. In 1988 he joined the Electronic Engineering Department of the UPC, where he is currently Associate Professor. His research interests include electric machines, power electronics converters and modulation strategies, variablespeed drive systems and fault detection algorithms. He has authored more over 50 scientific papers published in technical journals and conference proceedings. Over the last seven years, 5 Ph.D. dissertations have been completed under his supervision. His current activities include also teaching and consulting in electric drives and programmable electronics systems. He belongs to the Motion and Industrial Control Group at the Electronic Engineering Department of the UPC, which has in recent years established itself as one of the more active motor drives research group in the Technical Universities of Spain. The Group s major research activities concern the induction motor drive, enhanced efficiency drives, intelligent self-commissioning drives, direct torque controllers and sensorless vector drives. Dr. Romeral is a Member of the IEEE Industrial Electronics Society, European Power Electronics and Drives Association, and the International Federation of Automatic Control (IFAC). romeral@eel.upc.edu 46 Electric Power Quality and Utilization, Magazine Vol. II, No 1, 2006