Trends in Gear Fault Detection Using Electrical Signature Analysis in Induction Machine-Based Systems S. Hedayati Kia, H. Henao, Senior Member, IEEE, and G.-A. Capolino, Fellow, IEEE meshing speed fluctuations; 2) transverse vibration (axial or radial) which is mainly due to the response of mechanical structure to these external excitations [3]. Transducers such as accelerometers measure the mechanical transverse vibration whereas torque and speed sensors measure the torsional vibration. The transverse vibration analysis is preferred since it needs slight modification to the system installation and up to this time is considered as a most popular and efficient method for gear fault detection. The common gear faults are related to gear tooth irregularities namely chipped tooth, root crack, spalling, wear, pitting and tooth surface damage which are typical localized faults (Fig. 1) [4]. For a healthy gear, the most important frequency components in the vibration spectra are related to the tooth meshing frequency and its harmonics with sidebands due to the modulation effect of the mechanical system. When such faults take place, additional torsional vibrations related to fault-induced mechanical impacts are produced at the rotational frequency in the vibration signal [5]. The shape of mechanical impacts is related to both mechanical structure and torsional vibration resonances, excited by the tooth localized fault (TLF) when the damaged tooth is engaged. These last signatures appear in both transverse and torsional vibration transducers as it is illustrated in Fig. 2 [6]. This gives rise to magnitudes of sideband frequencies around mesh harmonics which spread in a wide range of the vibration spectrum and also harmonics of rotation frequencies which are mostly located at low frequency range of the vibration spectrum [7]. The sensitivity to the installation position and the background noise due to external mechanical excitations are main drawbacks of the vibration measurement for gear
1 Abstract – Vibration measurement and analysis have been used as a classical approach for health state assessment of gears in complex electromechanical systems for many years. Recently, several attempts have been performed for the detection of gear tooth localized faults using induction machine electrical signature analysis with promising results. These previous researches were mainly relied on the study of mechanical impacts effects, generated by gear localized faults, on the mechanical torque and consequently on the stator phase currents. This paper aims to investigate these recent advances with particular focus on the induction machinebased drive systems. Both analytical and modeling approaches will be considered which are helpful for a better understanding of observed phenomena and which leads to identifying both reliability and effectiveness of non-invasive methods for gear tooth localized fault detection.
Index Terms -- AC motor protection, Induction machine, Fault diagnosis, Gearbox, Monitoring, Motor Current Signature Analysis, Signal processing, Space vector, Stator current analysis, Vibration measurement.
I. INTRODUCTION
G
EARS are the main elements of mechanical power transmission in the majority of industrial applications. For instance, in wind turbines based on wound rotor induction generators (WRIG), the highest downtime is related to the multistage gearbox which links the rotor blades to the wind turbine generator through the main shaft [1]. Another example is the bogie of a railway traction system in which gearboxes connect traction motors to wheels. These elements are the main components of both railway traction and wind turbine systems and their health states promise the proper working condition of the entire scheme at its highest security and reliability levels [2]. In this regard, the early detection of incipient gear faults can systematically prevent any unexpected failures, increase considerably the train passenger safety, reduce wind turbines downtime and minimize financial consequences of gear damages. This can not be realized without using an efficient condition monitoring system. The vibration, acoustic emission and debris analysis are well known tools for mechanical engineers since they can provide the information which leads to the detection, diagnosis and prognosis of gear faults in any mechanical systems in which the electrical machine is involved as a prime mover. Particularly, vibrations in such systems are classified into: 1) torsional vibration which is directly related to periodic events such as rotating shafts and gear
(b)
(c)
(d)
(e)
(f)
(g)
1 The authors are with the Department of Electrical Engineering, University of Picardie “Jules Verne,” 80039 Amiens, France (e-mail:
[email protected];
[email protected];
[email protected])
978-1-4799-8899-0/15/$31.00 ©2015 IEEE
(a)
(h)
Fig. 1. Typical gear TLFs: a) Chipped tooth. b) Spalling. c) Scuffing. d) Wear. e) Surface damage. f) Root crack. g) Abrasion. h) Micropitting/Macro-pitting.
297
the stator current amplitude modulation according to the simplified model of the induction machine [17] whereas later this concept is linked to the stator current phase modulation [19]. Although, previous works have studied the load trouble in its general form, they could clarify the way that the induction machine electrical signatures can be affected by gear TLFs. This paper aims at analyzing the recent research activities on the topic of gear TLF diagnosis using electrical signature analysis. The modeling and analytical approaches which are two major aspects which are extensively studied in the literature will be discussed. The fault sensitivity analysis is also an important concern that should be considered since it can be affected by a number of mechanical parameters such as couplings, shafts torsional rigidities, bearings frictions, inertias and so on. These factors are able to mask the gear fault signature in the mechanical torque and hence in electrical signatures of the induction machine. In this regard, classical methods such as discrete Fourier transform (DFT) are not efficient to extract fault information from those signals which are dominated by supply frequencies. The modern signal processing techniques help to remove unrelated fault frequencies leading to a direct correlation between the electrical and mechanical signatures. This is for instance the case of amplitude and phase demodulation methods using Hilbert transform as it was mentioned [20]. All of above subjects which are studied recently will be discussed.
1 0.5
Vibration [g]
0 -0.5 -1 0
(a) 0.01
0.02
0.03
Fault signatures
0.04
0.05
0.06
0.07
0.08
0.09
0.06
0.07
0.08
0.09
0.06
0.07
0.08
0.09
0.06
0.07
0.08
0.09
0.1
Time [s]
1 0.5 0 -0.5 -1 0
1/fp 0.01
0.02
0.03
(b) 0.04
0.05
0.1
Time [s]
0.4 0.2
Torque [N.m]
0 -0.2 -0.4 0
(c) 0.01
0.02
0.03
0.04
0.05
0.1
Time [s]
Fault signatures 0.4 0.2 0 -0.2 -0.4 0
1/fp 0.01
0.02
0.03
(d) 0.04
0.05
0.1
Time [s] Fig. 2. Effect of gear TLF on vibration signals: Transverse vibration measured by an accelerometer for a healthy gear (a) and pinion tooth surface damage fault (b). Torsional vibration measured by a torque sensor for a healthy gear (c) and pinion tooth surface damage fault (d) [6]. (fp: pinion rotation frequency).
II. TORSIONAL VIBRATION EFFECTS ON MACHINE ELECTRICAL SIGNATURES
TLF detection [8]. Besides, the implementation of sensors faces two difficulties in those applications in which rotating parts of system are inaccessible or there are implementation constraints due to limited space and high temperature [9]. By contrast, the machine electrical signature analysis (MESA) uses non-invasive sensors and leads to fault diagnosis of both electrical and mechanical faults [10]. In this context, the gear TLF detection using MESA offers great advantages over invasive techniques principally due to its effective cost and the need of minimum changes in the system installation. An extensive research has been performed during recent years to study a possible gear TLF detection using non-invasive techniques e.g. using current and voltage measurements at drive side and/or a flux sensor in the vicinity of electrical machine [11]. The estimation of other quantities such as power factor, speed and electromagnetic torque give also valuable information concerning mechanical faults [2], [12]-[14]. Early works on this specific topic have reported only the influence of gear low speed shaft in which the rotation frequencies may overlap with broken rotor bar components in the stator current spectrum [15], [16]. Also, mechanical anomalies such as sinusoidal load torque oscillation, periodic and random dip of torque were studied through numerical simulations with the aim at finding a universal technique for non-invasive mechanical fault detection [17]. According to this initial study, it is illustrated that the crack in one of mill rolling wheel cause a chain of frequency lines in the spectrum of stator current [18]. Furthermore, the sinusoidal load torque oscillation effect is associated with
A. Analytical approach The effect of gearbox characteristic frequencies in the stator current of induction machine was studied initially based on the single frequency torsional vibration concept [8]. It was mentioned that the rotation frequencies namely input shaft, layer shaft and output shaft frequencies in a multi-stage gearbox appear in the electromagnetic torque signature. The sideband frequencies around the grid main frequency were related to the stator current amplitude modulation. The high-frequency torsional vibration, namely mesh frequencies which are supposed to be damped by the system inertia are well identified in the measured stator current [8], [21]. It was shown that magnitudes of some rotation and mesh related frequencies are sensitive to the gear tooth breakage fault. However, reasons for such increases or decreases in magnitude are not clearly explained. It was also mentioned that the severity of vibration is higher in case of one-tooth breakage fault rather than a tooth breakage fault with two or more teeth conducing to an important conclusion that the gear tooth fault detection may be much easier at an early stage [8]. Afterward, a more rigorous attempt was carried out to establish theoretical frameworks highlighting reasons for which the gear mechanical characteristic frequencies would be detected in the stator current spectrum [22]. It was principally relied on the observation of the mechanical torque spectrum experimented by the induction machine and the concept of stator current multicomponent phase modulation due to pinions and wheels transmission errors
298
and gear tooth mesh stiffness variations which are main sources of torsional vibrations in gears. It was shown that for a mono-stage gearbox, frequency components fG related to the gear mechanical characteristics in the stator phase currents can be written as: f G = f s ± kf p ± mf w ± nf mesh (1)
Wheel
Bearing Load
with k=0,1,2,3,…, m=0,1,2,3,…, n=0,1,2,3,… and fs, fp, fw and fmesh are supply, pinion rotation, wheel rotation and mesh frequencies respectively. The magnitudes of each frequency components in (1) are determined according to modulation indexes. It was mentioned that the magnitudes of mesh and mesh related frequencies are weak and thus difficult being localized in the stator current spectrum. Then, an effort was performed to use expression (1) as fault characteristic frequencies and their magnitude sensitivities to the gear tooth surface damage have been computed [23]. This type of gear fault (Fig. 1.e) is more realistic than one tooth and two or more teeth breakage faults which have been previously studied [8]. It was observed that within all eligible frequency components, |fs±fm+fp| is sensitive to the fault where the Welch method is used to reduce the effect of noise in the frequency spectrum. A new idea is recently introduced for non-invasive gear TLF detection [24]. In fact, a fault signature appears in the mechanical torque due to the occurrence of any TLF in gears, which can be identified thanks to the amplification introduced by the torsional mechanical resonance of the mechanical system. This particular shape introduces in the torque signal periodic impulses with a defined time interval related to the rotation frequency corresponding to the fault location in gear (pinion side or wheel side for a one-stage gear). This produces fault-related frequency components ffaulty in the stator current which can be formulated as: f faulty = f s ± hf fp (2)
Pinion
Pinion
Shaft Induction Machine
(a)
TL (t )
θ2 (t )
K (t )
J1 + J M , r1
θ1 ( t ) Te (t )
e(t )
dz
J 2 + J L , r2
(b)
Wheel
Fig. 3. Simplified model of the mechanical part using damping and stiffness coefficients: a) Mechanical configuration. b) Dynamic model for torsional vibration analysis [25].
and load are rigidly linked to the input and output shafts of the gearbox respectively (Fig. 3.a). The evolution of the contact between teeth of a gear is the reason of stiffness time variation (K(t) in Fig. 3). This phenomenon depends mainly on the teeth shape for which the exact estimation of the stiffness variation needs the finite element approach. However, this variation can be approximated by a rectangular shape which explains the phenomenon in an approximately way. The stiffness variation for each cycle can be considered with a minimum value when there is only a contact point between the pinion-wheel teeth and a maximum value when there are two contact points [25]. The presence of mesh and mesh-related frequencies in the stator current is due mainly to the stiffness variation in teeth contacts. The transmission error (e(t) in Fig. 3) in the gearbox as it is depicted in Fig. 5.b is related to the physical phenomena such as pinion and wheel eccentricities, tooth profile errors, pitch errors and so on [25]. This error produces pinion and wheel rotation sideband frequencies around the fundamental and mesh frequencies in the stator current spectrum. The same method is applied to study the influence of planetary gearbox torsional vibrations in the electrical signatures of wound rotor induction generator (WRIG) in WTs [28]. The gear TLF such as tooth pit and spall is assimilated to a hole with constant widths and included in the gear model. It is demonstrated that when the tooth defect comes into mesh, the contact between the teeth would be partially lost leading to a local reduction in mesh stiffness as it is depicted in Fig 4. These rapid variations in the mesh stiffness contact induce wide range of frequencies in the stator current spectrum [9]. The total effective mesh stiffness can also be determined based on Hertzian, bending, shear, and compressive mesh stiffness in order to modeling the gear tooth crack leading to similar mesh stiffness variation as it was shown [29]. The preliminary numerical simulations, which demonstrate the feasibility
where h=1,2,3,… and ffp is the fault profile frequency. It should be noted that the most sensitive frequency components in (2) are related to closest ones to the damped natural frequency [24]. It was also analytically proved that the stator current space vector instantaneous frequency (SCSVIF) and mechanical torque have similar information concerning gear TLFs.
B. Modeling Approach Recently, several studies have been carried out based on the use of lumped parameter dynamic model of the gearbox to investigate the effect of different types of gear TLF in the machine electrical signatures [9], [25]-[27]. The main goal was initially to validate the former theoretical developments through a modeling approach with a simple dynamic model for a healthy spur gear. In this regard, a wide range of gear dynamic models can be found in the literature which commonly needs detailed mechanical parameters of the gear. Nevertheless, a simplified model which considers a realistic behavior of gear and thus needs minimum number of gear mechanical parameters was used to study the effect of gear torsional vibrations on machine electrical signatures [25]. Fig. 3.b represents this model for an electromechanical system in which the induction machine
299
Mesh stiffness [108 N/m]
Mesh stiffness [108 N/m]
(a) t/tm
simplified differential equations for a defined electromechanical system [24]. The proposed approach can be applied to numerous applications such as railway traction systems and wind turbines which have the mechanical transmission models very similar to what was used in [2] and [33].
(b)
III. SIGNAL PROCESSING FOR GEAR FAULT DETECTION
t/tm
Static transmission error [m]
Fig. 4. Effect of gear tooth damage on the mesh stiffness (mesh stiffness versus the normalized time with tm the mesh period): a) no defect. b) 20 mm depth defect [9].
A. Time domain analysis The spectral analysis of vibration measurement may not be an effective approach for early fault detection of gear TLFs. Other techniques such as cepstrum, timesynchronous average and related techniques, timefrequency distribution techniques, cyclostationary analysis, signal modeling techniques, high-resolution spectral analysis techniques and advanced statistical methods [34]. The gear frequency characteristics spread in a wide frequency range of the stator current spectrum according to the expression (1). Therefore, the discrete wavelet transform method is combined with classical discrete Fourier transform in order to better identification of weak amplitude mesh and mesh-related frequency components which are generally localized at the higher frequency band of the stator current spectrum [8]. Then, it was shown that the stator current demodulation gives a similar signature in comparison with vibration measurements (transverse and torsional). For instance, the stator current amplitude demodulation technique was proposed to track the rotating shaft frequencies whereas the stator current phase demodulation was used for highlighting the mesh and mesh-related frequencies of a multi-stage gearbox [20]. In this last work Hilbert transform is used for stator current amplitude and phase demodulations. The Bedrosian theorem is a crucial condition that should be fulfilled to obtain meaningful results which was not verified in [20]. The space vector analysis is newly proposed which is able to furnish identical results to those obtained by Hilbert transform without considering Bedrosian theorem condition [35]. The torsional vibrations in any electromechanical system can be identified either in SCSVIA or in SCSVIF according to the induction machine response to any external sinusoidal load torque oscillation [13], [36]. In this regard, abc model of a WRIG whose parameters are mentioned in [1], is used to study the performance of both SCVSIF and SCSVIA to an external load torque oscillation. A sinusoidal load torque oscillation with 2% of rated load amplitude and with variable frequency is added to a mean load torque and applied to the model of WRIG. Then, the main frequency component amplitude is extracted from both SCSVIF and SCSVIA spectra. The numerical simulation results are shown in Fig. 6. It can be clearly observed that the stator current amplitude modulation is dominated over the stator current phase modulation for any load torque oscillation with a frequency less than 20Hz whereas the stator current phase modulation is dominated over the stator current amplitude modulation for the same load torque oscillation with a frequency upper than 20Hz when the WRIG works close to its rated load. In this regard, the synchronous signal averaging method which is a classical method for gear TLF detection using vibration measurement is applied to the
(a)
Global transmission error [m]
Time [s]
(b) Time [s]
Fig. 5. Transmission errors in case of gear tooth pit fault: a) Static transmission error. b) Global transmission error [27].
of MESA for gear TLF fault diagnosis, were carried out without providing any experimental results. Other relevant works which study a seeded gear tooth fault in a planetary gearbox and its effect on the electrical signatures of permanent magnet synchronous machines are presented [30], [31]. In [27], the first attempt has been performed to combine the numerical simulations with experiments in order to detect the tooth pitting fault in a multi-stage gear. In this last work, a low-degree of freedom model for gear dynamic model is employed that comparable to which was used earlier [25]. The tooth pitting is included in the model as a part of static transmission error function. It was assumed that at all times that the defective tooth was not in mesh with the drive gear the static transmission is equal to zero (i.e. ideal tooth profile) otherwise it can be described by a rapid variation which is shown in Fig. 5.a. These last variations induce a damping oscillation in the global transmission error e(t) (Fig. 5.b) and consequently in the load torque which produce relevant signatures in the instantaneous amplitude of stator current space vector. Such a model considers backlash nonlinearities in case of TLF in the model with fast pulsation on both mesh and transmission error functions as it was discussed [27]. The analysis of torsional resonance frequencies is an attractive approach which helps to detect gear faults in the complex electromechanical systems [32]. It is verified that the gear TLF may generate mechanical impacts which can be observed in the torque and hence in machine electrical signatures. The effect of the TLF is amplified in the mechanical torque in a bandwidth which depends of natural frequencies and damping factor of the concerned electromechanical system. This was shown through
300
SCSVIA with successful results (Fig . 7) [27]. Also, the gear backlash in motion servo drive is identified using the similar technique applied to the estimated disturbance torque [36]. Despite the effectiveness of the synchronous averaging technique in reducing the unrelated frequency components in machine electrical signatures, it needs a shaft rotation reference signal to synchronize different segment of the signal. This is the main drawback of this method for gear TLF detection in those electromechanical systems which are not equipped with speed sensors.
with which is obtained from the measured torque. In practice, some of the h×ffp frequency components (with h=1,2,3,...) exist even in healthy condition due to the inherent rotor eccentricity of the induction machine as well as the gear pinions and wheels eccentricities. Therefore, the reconstruction process can be realized with those h×ffp frequency components which have a great sensitivity to the fault. Since the successive fault impacts on the mechanical torque may excite the mechanical system torsional vibration, the sensitive fault harmonics are located around the equivalent damped natural frequency of the electromechanical system as it was mentioned [24].
B. Frequency domain analysis A classical approach for studying a discrete signal in the frequency domain is the DFT. This method is combined with a simple algorithm for fault signature reconstruction as it is shown in Fig. 8. This helps to remove unrelated fault frequencies and extract the fault profile from both SCSVIF and mechanical torque. The application of this method for gear tooth surface damage fault detection is illustrated in Fig. 9. It can be observed that the fault profile extracted from SCSVIF is similar both in magnitude and in phase
C. Real-time Implementation The real-time MESA guarantees the promptness of the fault diagnosis. Digital signal processors (DSPs) and field programmable arrays (FPGAs) have been widely used in electrical drives for torque, speed and position controls. Nevertheless, these last elements have been rarely employed for mechanical faults diagnosis. An algorithm is developed based on the SCSVIF analysis for on-line gear tooth surface damage fault detection which is implemented on a rugged reconfigurable real-time embedded system using a processor running a real-time operating system, a reconfigurable FPGA and an analog inputs module with an embedded signal conditioning system. The FPGA resources are used for the SCSVIF computation whereas real-time processor resources are used for determination of the
Amplitude [dB]
(a) 0 -20 -40 -60 -80 0
0 20 20
40 40
60
80
100 120
60 140
Frequency [Hz]
160
80 180
200
Input data
Load [%]
100
Discrete Fourier transform
kffp
…
Fig. 8. Reconstruction of fault profile in the time domain [6]. 0.2
-60 -80 0
0
(a)
0.1
20 20
0
40 40
60
80
100 120
60 140
Frequency [Hz]
160
80 180
200
Load [%]
100
Fig. 6. Response of WRIG to an external load toque oscillation: a) SCSVIF. b) SCSVIA.
-0.1 -0.2 0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Time [s]
0.2
(b)
0.1 0 -0.1
(a)
-0.2 0
0.01
0.02
0.03
0.04
0.05
Time [s]
0.06
0.07
0.08
0.09
0.2
0.1
(c)
0.1 0
SCSVIF [Hz]
Synchronous average SCSVIA [A]
Fault profile
-40
Torque [N.m]
Amplitude [dB]
-20
× ffp 2ffp
Selective filter
(b) 0
Inverse discrete Fourier transform
Angle [rad] Fault signatures
(b)
-0.1 -0.2 0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Time [s] 0.2
(d)
0.1 0 -0.1 -0.2 0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Time [s]
Angle [rad]
Fig. 9. Reconstruction of fault profile from the measured torque at rated load: a) Healthy gear. b) Faulty gear. Reconstruction of fault profile from the SCSVIF at rated load: c) Healthy gear. d) Faulty gear [6].
Fig. 7. Synchronous average SCSVIA computation for: a) Healthy gear. b) Gear with pitted tooth [27].
301
[5] (b)
40%
60%
80%
100%
Fault index [pu]
Fault index [pu]
(a) 20%
20%
Sample
40%
60%
80%
100%
[6]
[7] [8]
Sample
Fig. 10. Computation of fault index for load levels from 20% to 100% of rated-load: a) Healthy condition. b) Gear tooth damage fault [37].
[9]
fault index. The proposed algorithm needs online speed estimation in order to localize fault-related frequencies in the SCSVIF [37]. The fault index computation results in real-time for both healthy and faulty gears at different load levels are shown in Fig. 10. Although, the mean value of fault index is increased in gear faulty condition, it has a large standard deviation which makes decision making process complicated. Recently, a statistical tool called spectral Kurtosis with reference has been proposed to define the electromechanical system healthy state reference. This technique proves its effectiveness in case of load torque oscillation fault only [38].
[10]
[11] [12]
[13]
IV. CONCLUSION In this paper, a brief review of recent works on the topic of gear TLF detection using MESA is presented. The analytical and modeling approaches have been initially proposed to study the effect of gear torsional vibrations on the machine electrical signatures. It was mentioned that the rotating and mesh frequencies can be detected in the stator current due to the electromechanical modulation effect. The mesh and mesh-related frequencies are commonly difficult to be observed in the machine electrical signatures since their relevant modulation indexes are very weak. The stator current magnitude and phase demodulations, speed and electromagnetic torque estimations establish a solid link with the vibration measurement (transverse and torsional). Appearance of any incipient gear TLF may not systematically affect the amplitude of rotating and mesh frequencies in the electrical signatures but give rise to those of rotation harmonics which are adjacent to the damped natural frequency of the electromechanical system.
[14]
[15] [16]
[17]
[18] [19]
[20]
V. REFERENCES [1] [2]
[3] [4]
[21]
S.H. Kia, H. Henao, G.-A. Capolino, “Development of a test bench dedicated to condition monitoring of wind turbines,” in Proc. of IEEE-IECON’2014, Dallas (TX, USA), 28 Oct. - 1 Nov. 2014, 6pp. J. Guzinski, M. Diguet, Z. Krzeminski, A. Lewicki, H. Abu-Rub, “Application of speed and load torque observers in high speed train drive for diagnostic purpose,” IEEE Trans. Ind. Electron., vol. 56, no. 1, pp. 248–256, Jan. 2009. R. B. Randall, Vibration-based condition monitoring. John-Wiley Press, 2011. S. Jia, I. Howard, “Comparison of localised spalling and crack damage from dynamic modelling of spur gear vibrations,” Mech. Syst. Signal Process., vol. 20, no. 2, pp. 332–349, Feb. 2006.
[22]
[23]
[24]
302
G. Dalpiaz, A. Rivolta, R. Rubini, “Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears,” Mech. Syst. Signal Process., vol. 14, no. 3, pp. 387–412, 2000. S.H. Kia, H. Henao, G.-A. Capolino, “Gear tooth surface damage fault detection using induction machine electrical signature analysis,” in Proc. of SDEMPED’2013, Valencia (Spain), 27-30 Aug. 2013, pp. 358-364. W. Wang, “Early detection of gear tooth cracking using the resonance demodulation technique,” Mech. Syst. Signal Process., vol. 15, no. 5, pp. 887-903, 2001. A.R. Mohanty, C. Kar, “Monitoring gear vibrations through motor current signature analysis and wavelet transform,” Mech. Syst. Signal Process., vol. 20, no. 1, pp. 158–187, Jan. 2006. N. Feki, G. Clerc, Ph. Velex, “An integrated electro-mechanical model of motor-gear units - Applications to tooth fault detection by electric measurements,” Mech. Syst. Signal Process., vol. 29, pp. 377-390, May 2012. E. G. Strangas, “Response of electrical drives to gear and bearing faults - diagnosis under transient and steady state conditions,” in Proc. of Workshop on Electrical Machines Design Control and Diagnosis (WEMDCD’2013), invited paper, Paris (France), March 11-12, 2013, pp.289-297. H. Henao, S.H. Kia, G.-A. Capolino, “Torsional vibration assessment and gear fault diagnosis in railway traction system,” IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1707-1717, May 2011. I. Bogiatzidis, A. Safacas, E. Mitronikas, “Detection of backlash phenomena appearing in a single cement kiln drive using the current and the electromagnetic torque signature,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3441-3453, Aug. 2013. B. Trajin, J. Regnier, J. Faucher, “Comparison between stator current and estimated mechanical speed for the detection of bearing wear in asynchronous drives,” IEEE Trans. Ind. Electron., vol. 56, no. 11, pp. 4700-4709, Nov. 2009. A. Ibrahim, M. El-Badaoui, F. Guillet, F. Bonnardot, “A new bearing fault detection method in induction machines based on instantaneous power factor,” IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 42524259, Dec. 2008. W.T. Thomson, “On-line current monitoring to detect electrical and mechanical faults in three-phase induction motor drives,” in Proc. Int. Conf. Life Manage. Power Plants, Dec. 1994, pp. 66-73. M. Fenger, B.A. Llyod, W. T. Thomson, “Development of a tool to detect faults in induction motors via current signature analysis”, Cement Industry Technical Conference, IEEE IAS/PCA 2003, pp. 3746, 2003. G. Salles, F. Filippetti, C. Tassoni, G. Grellet, G. Franceschini, “Monitoring of induction motor load by neural network techniques,” IEEE Trans. on Power Electronics, vol. 15, no. 4, pp. 762-768, Jul. 2000. A. Bellini, et al., “Mechanical failures detection by means of induction machine current analysis: a case history,” in Proc. of SDEMPED’2003, Atlanta (USA), 24-26 Aug. 2003, pp. 322-326. M. Blödt, M. Chabert, J. Regnier, J. Faucher, “Mechanical load fault detection in induction motors by stator current time-frequency analysis,” IEEE Trans. Ind. Appl., vol. 42, no. 6, pp. 1454-1463, Nov./Dec. 2006. A.R. Mohanty, C. Kar, “Fault detection in a multistage gearbox by demodulation of motor current waveform,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1285-1297, June 2006. C. Kar, A.R. Mohanty, “Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform,” J. Sound Vib., vol. 311, no. 1/2, pp. 109–132, March 2008. S.H. Kia, H. Henao, G.-A. Capolino, “Analytical and experimental study of gearbox mechanical effect on the induction machine stator current signature,” IEEE Trans. Ind. Appl., vol. 45, no. 4, pp. 14051415, July/Aug. 2009. S.H. Kia, H. Henao, G.-A. Capolino, “A comparative study of acoustic, vibration and stator current signatures for gear tooth fault diagnosis,” in Proc. of ICEM’2012, Marseille (France), 2-5 Sept. 2012, pp. 1512-1517. S.H. Kia, H. Henao, G.A. Capolino, “Gear tooth surface damage fault detection using induction machine stator current space vector analysis,” IEEE Trans. on Ind. Electron., vol. 62, no. 3, pp. 18662001, Mar. 2015.
[25] S.H. Kia, H. Henao, G.-A. Capolino, “Torsional vibration effects on induction machine current and torque signatures in gearbox-based electromechanical system,” IEEE Trans. Ind. Electron., vol. 56, no. 11, pp. 4689-4699, Nov. 2009. [26] N. Feki, G. Clerc, Ph. Velex, “Gear and motor fault modeling and detection based on motor current analysis,” Electric Power System Research, vol. 95, pp. 28-37, Feb. 2013. [27] J.R. Ottewill, M. Orkisz, “Condition monitoring of gearboxes using synchronously averaged electric motor signals,” Mech. Syst. Signal Process., vol. 38, no. 2, pp. 482-498, Jul. 2013. [28] Z. Daneshi-Far, H. Henao, G.-A. Capolino, “Planetary gearbox effects on induction machine in wind turbine: modeling and analysis,” in Proc. of International Conference on Electrical Machines (ICEM), Marseille (France), 2-5 Sep. 2012, pp.1790-1796. [29] S. Wu, M. J. Zuo, A. Parey “Simulation of spur gear dynamics and estimation of fault growth,” Mech. Syst. Signal Process., vol. 317, no. 3-5, pp. 608-624, Nov. 2008. [30] L. Hong, J. S. Dhupia, “A time-domain fault detection method based on an electrical machine stator current measurement for planetary gear-sets,” in Proc. of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Wollongong (Australia), 9-12 Jul. 2013, pp. 1631-1636. [31] J. Zhang, J. S. Dhupia, C. J. Gajanayake, “Model based current analysis of electrical machines to detect faults in planetary gearboxes,” in Proc. of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Besançon (France), 8-11 Jul. 2014, pp. 1616-1621. [32] J.C. Wachel, F.R. Szenasi, “Analysis of torsional vibration in rotating machinery,” in Proc. of the 22th Turbomachinery Symposium, Texas (USA), 13-16 Sep. 1993, pp. 127-151. [33] I.P. Girsang, J.S. Dhupia, E. Muljadi, M. Singh, L.Y. Pao, “Gearbox and drive train models to study of dynamic effects of modern wind turbines,” IEEE Trans. Ind. Appl., 2014 (early access). [34] G. Dalpiaz, A. Rivola, R. Rubini, “Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears,” Mech. Syst. Signal Process., vol. 14, no. 3, May 2000, pp. 387-412. [35] B. Trajin, M. Chabert, J. Regnier, J. Faucher, “Hilbert versus Concordia transform for three-phase machine stator current timefrequency monitoring,” Mech. Syst. Signal Process., vol. 23, pp. 2648-2657, 2009. [36] K.-K. Huh, R.D. Lorenz, N.J. Nagel, “Gear fault diagnostics integrated in the motion servo drive for electromechanical actuators,” IEEE Trans. Ind. Appl., vol. 48, no. 1, pp. 142-150, July/Aug. 2012.
[37] S.H. Kia, H. Henao, G.A. Capolino, “A real-time platform dedicated to on-line gear tooth surface damage fault detection in induction machines,” in Proc. of IEEE-ICEM’2014, 2-5 Sep. 2014, Berlin (Germany), pp. 1478-1484. [38] E. Fournier, et al. “Current-based detection of mechanical unbalance in an induction machine using spectral kurtosis with reference,” IEEE Trans. on Ind. Electron., vol. 62, no. 3, pp. 1879-1887, Mar. 2015.
Biographies Shahin Hedayati Kia received the M.Sc. in electrical engineering from the Iran University of Science and Technology (IUST), Tehran, Iran, in 1998 and the M.Sc. and the Ph.D. in power electrical engineering from the University of Picardie “Jules Verne”, Amiens, France, in 2005 and 2009, respectively. From 2008 to 2009, he was a lecturer at INSSET de SaintQuentin, France. From September 2009 to September 2011 he was a postdoctoral associate at the School of Electronic and Electrical Engineering of Amiens (ESIEE Amiens). Since September 2011, he is an Assistant Professor at the University of Picardie “Jules Verne,” Amiens, France in the Department of Electrical Engineering. Humberto Henao (M’95–SM’05) received the M.Sc. degree in electrical engineering in 1983, the M.Sc. degree in power system planning in 1986, and the Ph.D. degree in electrical engineering in 1990. In 1994, he joined the Ecole Supérieure d’Ingénieurs en Electrotechnique et Electronique, Amiens, France, as an Associate Professor. In 1995, he joined the Department of Electrical Engineering, University of Picardie “Jules Verne,” Amiens, as an Associate Professor, where he has been a Full Professor since 2010. He leads the research activities in the field of condition monitoring and diagnosis for power electrical engineering. His main research interests are modeling, simulation, monitoring, and diagnosis of electrical machines and drives. Gérard-André Capolino (A’77–M’82–SM’89–F’02) was born in Marseille, France. He received the B.Sc. degree in electrical engineering from the Ecole Centrale de Marseille (ECM), Marseille in 1974, the M.Sc. degree from the Ecole Supérieure d’Electricité (Supelec), Paris, France, in 1975, the Ph.D. degree from the Aix-Marseille University (AUM), Marseille, in 1978, and the D.Sc. degree from the Institut Polytechnique de Grenoble (Grenoble INP), Grenoble, France, in 1987. He had several faculty positions in Yaoundé, Cameroun, Le Creusot, France and Marseille, France. In 1994, he joined the University of Picardie “Jules Verne,” Amiens, France, as a Full Professor and was appointed Chair Professor in 2013.
303 Powered by TCPDF (www.tcpdf.org)
