Bearing Faults Detection in Induction Machines Based on Statistical Processing of the Stray Fluxes Measurements

Transcription

1 Bearing Fault Detection in Induction Machine Baed on Statitical Proceing of the Stray Fluxe Meaurement Ciprian Harlişca, Loránd Szabó, Lucia Froini, Andrea Albini Φ Abtract -- Frequent defect of induction machine are due to divere bearing fault. The detection of uch fault in their incipient phae can deciively contribute to the prevention of unplanned breakdown in indutrial plant. In thi paper the detection of three type of bearing fault by mean of tatitical proceing of the tray fluxe meaurement i detailed. The developed noninvaive method require only both imple probe and eay computation. Numerou meaurement had been performed for all the combination of bearing fault, load and tray flux probe taken into tudy. All the reult emphaized the effectivene of the applied imple fault diagnoi method. Index Term--ac machine, ball bearing, electric machine, fault detection, fault diagnoi, induction motor, rotating machine. E I. INTRODUCTION lectrical machine with mall fault can till work, but thee fault will evolve in time and they may caue a complete breakdown. Preventive meaure hould be taken in order to protect the machine. Fault can occur in any part of the machine. Thee can be of electrical or mechanical origin. The main electrical fault can be in the tator and rotor winding (or cage) [1], [2]. Mechanical fault include bearing fault, air-gap eccentricity, gearbox fault, mialignment, etc. The mot of the failure (about 4%) are related to the bearing [3]. The bearing fault do not caue immediate breakdown, but they evolve in time until they produce a critical failure of the machine. Unfortunately thee failure finally reult both in cotly repair cot and long downtime. The bearing fault can be caued by material fatigue, overheating, harh environment, inadequate torage, contamination, corroion, wrong handling and intallation, unbalanced load, bearing current, etc. [4]. However the main caue of their failure i due to their poor lubrication, which can be eaily avoided by a correct maintenance plan. Φ Thi paper wa upported by the project "Improvement of the doctoral tudie quality in engineering cience for development of the knowledge baed ociety-qdoc" contract no. POSDRU/17/1.5/S/78534, project co-funded by the European Social Fund through the Sectorial Operational Program Human Reource C. Harlişca and L. Szabó are with Department of Electrical Machine and Drive, Technical Univerity of Cluj-Napoca, Romania ( Ciprian.Harlica@mae.utcluj.ro, Lorand.Szabo@emd.utcluj.ro). L. Froini and A. Albini are with Department of Electrical, Computer and Biomedical Engineering, Univerity of Pavia, Italy ( lucia@unipv.it, andrea.albini@unipv.it). In the literature the bearing fault are claified according to: the location of the fault: inner race, outer race, ball, and cage; the fault ignature: ingle-point defect and generalized roughne. Generalized roughne fault are the mot frequent caue of bearing failure. They uually occur in indutrial environment due to variou mechanical caue which lead to a fater wear of the component of the bearing, epecially of the raceway and ball. Such fault can be eaily determined becaue the bearing pin roughly or difficultly. The detection of ingle-point bearing defect i more difficult. If a moving component pae over a defected urface in the bearing, it create a ucceion of ocillation which repeat with each pa over the damaged area [5], [6]. The repetition frequency of the impact depend on the poition of the fault within the bearing and ha an indirect impact on the current, magnetic flux, noie and vibration of the machine. In thi paper the detection of three type of ingle-point bearing fault by mean of tatitical proceing of the tray fluxe meaurement i preented. The developed noninvaive method require only imple, even hand-made, magnetic flux probe and ome eay computation tep. II. BEARING FAULT DETECTION TECHNIQUES Upon performing a literature urvey on the bearing fault detection technique it can be tated that a huge number of method are propoed. A ignificant part of the paper are dealing with the rolling bearing fault detection baed on analyzing the tator current and the vibration of the electrical machine [7]. Thee method eentially are baed on finding ome well-defined pecific fault frequency component in the pectrum of the current or vibration ignal [8], [9]. Unfortunately vibration baed monitoring technique require expenive precie vibration enor and pecial equipment. They alo need direct acce to the machine under teting, which i not alway poible in indutrial environment. On the other hand current monitoring require only imple current enor [1]. The current monitoring baed technique can be ued to detect a large number of other fault, too: broken rotor bar, horted winding, air-gap eccentricity [11], load fault, etc /13/$ IEEE 47

2 Several reearch team tudied the detection of bearing fault in electrical machine by uing the tray flux around the motor [12], [13]. The tray flux of an electric machine i the magnetic flux that radiate outide the houing of the machine. It i reidual and undeirable, ince it i not participating in the torque generation [14]. The tray flux detection ued in electrical machine fault diagnoi i applied ince about 3 year a an effective technique for noninvaive diagnoi ince the enor can be outide the machine without being neceary to meaure voltage, current or other electric or non-electric quantitie [15]. Thi method can be applied for detecting alo other electrical machine fault, a tator winding fault [16], rotor defect [17] or voltage ource diymmetry [18]. Alo another diagnoi method, the Park' Vector Approach (PVA) i frequently ued in detecting bearing fault [19], [2]. In the lat year everal artificial intelligence (AI) baed bearing fault detection method were developed [21]. The mot ignificant reult were obtained by uing artificial neural network (ANN) [22], fuzzy logic [23], Support Vector Machine (SVM) approach [24], particle warm optimization (PSO) [25], Hilbert-baed bipectral analyi approach [26], etc. Several other fault detection method are baed on proceing the meaured ignal by mean of the wavelet tranform [27], [28]. greae. The induction machine i joined with a magnetic powder brake of 5 kw and 1 N m through an elatic couple. The load can be et and meaured by uing the control unit of the brake. The tandard meaurement (of the RMS value of the current, voltage and active power) were performed via a three phae power meter. The peed wa meaured with a mechanical tachometer. For the phae current meaurement a imple Hall-effect baed current probe wa ued. For the pecific tray flux meaurement two magnetic flux enor were applied (ee Fig. 3): a hand-made flux probe coniting of a emicircular ferrite core with a 44 mm outer and a 4 mm inner diameter. The ferrite core i wound with 3 turn of enameled copper of.112 mm diameter [13]. an indutrial one, of M-343F-124 type, produced by Emeron. It ha a circular form and conit of everal turn wound around an air-core. Thi probe can be ued to meaure the leakage axial flux and it i imilar to mot of the flux enor cited in the literature, e.g. [29]. III. THE EXPERIMENTAL SETUP The tet bench for performing the experimental tudy wa built up in the Laboratory of Electric Drive of Univerity of Pavia, Italy (ee Fig. 1). a) hand made Fig. 1. The experimental etup The grid connected three-phae 2445T45 type (FIR Elettromeccanica S.R.L) induction machine ha the following rated data: power 2.2 kw, current 8.7 / 5 A, peed 28 r/min. The induction machine ha two NSK 625Z type rolling ball bearing with nine ball, which are lubricated with b) commercial (Emeron) Fig. 2. The applied flux probe For a greater effectivene of the meaurement a firt order low pa RC filter (R = 1 kω, C = 82 F and a cut-off frequency of 1942 Hz) wa connected to each of the probe [3]. The flux and current probe were connected to a portable National Intrument data acquiition board and to a peronal computer with NI LabVIEW oftware. The ued data acquiition board i a NI USB 6212 type with 16 analog input, 16 bit reolution, an input range of ±1 V and a bu- 471

3 powered USB for high mobility. The data acquiition wa controlled through a pecial created virtual intrument, which allowed the imultaneou data acquiition from all the connected probe. IV. THE PERFORMED MEASUREMENTS The current probe wa placed on one of the phae and the flux probe were poitioned around the induction machine in variou place. The hand-made flux probe wa placed in three different location around the houing of the machine in order to meaure the axial body flux, the radial flux on the end winding and the radial body flux, repectively, a it i hown in Fig. 3a,b and c. The Emeron flux probe wa poitioned outide the fan end of the induction machine, with it axi coincident with the haft axi, a hown in Fig. 3d. a) hole in the outer race b) deformation of the eal Fig. 5. The faulted bearing a) axial body flux The induction machine wa teted in it healthy condition and having one of it bearing ubtituted by a faulty one. For each machine condition the motor wa teted at no-load, at 5% of the rated load and at the rated load. Exceptionally when the machine wa teted with a cracked bearing, the full load condition wa not poible to be achieved due to very trong vibration of the machine which could caue the detruction of the machine. During all the meaurement performed 5, ample were acquired at 1 khz ampling frequency. For each et of meaurement ten conecutive acquiition were collected, with an acquiition time of 5 econd each. b) radial flux on the end-winding V. c) radial body flux THE STATISTICAL DATA PROCESSING After finihing the meaurement all the aved data acquiition were proceed via FFT in order to obtain the harmonic pectrum of the ignal. Here, in Fig. 6, only a ingle reult et of the pectrum analyi i given. d) axial flux Fig. 3. Poition of the flux probe for meauring different tray fluxe Healthy Three bearing fault were experimentally imulated and tudied [9]: crack in the outer race, imilar to a fault caued by exceive wear (Fig. 4); hole in the outer race (Fig. 5a); deformation of the eal (Fig. 5b). Power denity Bearing fault Power denity Frequency Fig. 6. The power denity of the radial flux on the end-winding for the healthy machine and that with a hole in the outer race of one of it bearing at 5% of the rated load Fig. 4. The faulted bearing crack in the outer race 472

4 A it can be een, everal frequency component (mainly very cloe to the integer multiple of the fundamental) indicating a bearing fault can be clearly ditinguihed in the figure. The implementation of the propoed tatitical method follow the ubequent tep [3]: each integer multiple of the fundamental, between 1 and 1 Hz, i normalized with repect to the 5 Hz fundamental, a to be et at db. Only thee harmonic component were taken into tudy, ince they do not depend on the pecific parameter of the machine (e.g. number of rotor lot); for every harmonic component taken into account a mean value of the ten acquiition i computed, for both the healthy and the faulty condition of the machine (m h, m f, repectively); the difference of the mean value mentioned above (d = m h - m f ) i calculated; in order to evaluate the diagnotic content of the proceed data, the abolute value of the difference (= d ) i compared with the tandard deviation () of the harmonic in the cae of the healthy motor. Baed on thi comparion, three level of fault ignificance were conidered [3]: if >, the level of ignificance i low ( ); if > 2 it i medium ( ); if > 5 it i high ( ). If thee condition are not fulfilled an incipient bearing failure i impoible to be detected by mean of thi method. The propoed bearing fault detection method baed on tatitical computation hopefully i really applicable in indutrial environment a a truly accurate and robut fault indicator. The effectivene of the propoed tatitical computation baed method wa tudied for 2 cae, for all the combination of three bearing fault (crack in the outer race, hole in the outer race and deformation of the eal), three load (no-load, 5% of the rated load and the rated load) and the two tray flux probe in the poition taken into tudy (given in Fig. 3 ). From the huge amount of reult only four can be given here, thoe conidered the mot ignificant one. In the following table, for each experimental cae, the mean value of the logarithmic power pectral denity for the healthy and faulty machine, their difference, the tandard deviation, repectively the ignificance level of the diagnotic index for all the frequency component taken into tudy between 1 and 1 Hz at a 5 Hz tep are all given. TABLE I RESULTS FOR DAMAGED BEARING SEAL CONDITION AT 5% OF THE RATED LOAD OBTAINED BY MEANS OF MEASURING THE RADIAL FLUX ON THE END-WINDING f A it can be een in the table everal harmonic component indicate clearly the damaged bearing of the induction machine in tudy. f >2 TABLE II RESULTS FOR DAMAGED BEARING SEAL CONDITION AT THE RATED LOAD OBTAINED BY MEANS OF MEASURING THE RADIAL FLUX ON THE END-WINDING >2 >5 >5 473

5 f TABLE III RESULTS FOR DAMAGED BEARING SEAL CONDITION AT THE RATED LOAD OBTAINED BY MEANS OF MEASURING THE AXIAL BODY FLUX Upon the reult given in Table III it can be tated that alo the axial body flux ignal of the induction machine can be ued in fault detection, in a imilar way a the radial flux on the end-winding. f >2 TABLE IV RESULTS FOR DAMAGED OUTER BEARING RACE CONDITION AT THE RATED LOAD OBTAINED BY MEANS OF MEASURING THE RADIAL BODY FLUX >2 >5 >5 The detection method baed on meauring the radial flux body flux at the end winding can be alo ued to detect outer bearing fault, a a crack in the outer race (ee Table IV). VI. CONCLUSIONS Bearing fault are one of the mot frequent defect of induction machine. Therefore the detection of uch fault already in their incipient phae i quite important in the indutrial environment. In the paper a noninvaive bearing fault detection methodology i preented, which involve imple meaurement of the tray flux around the machine by mean of different flux probe in different poition. It wa proven that by applying the method three baic bearing fault type can be detected by imple meaurement and computation. The mot effective detection wa performed by meauring the radial tray flux of the induction machine' end-winding by uing the hand-made flux probe. Inignificant reult were obtained by meauring the axial tray flux of the machine via the Emeron flux probe. Alo the tator current were meaured and proceed in the frame of the tudy [3]. However the diagnotic content given by thi parameter wa le ignificant with repect to the information given by the tray flux, therefore they were not detailed in the paper. The tet performed at different load have hown noteworthy imilaritie, which mean that the developed fault detection method can be a ueful tool regardle of the teted machine loading. The fault detection method detailed in the paper eem to be quite effective. Moreover, the applied hand-made magnetic flux probe ued to meaure the tray flux round the induction machine i very imple and cheap, a compared with other expenive one preented in everal paper dealing with thi diagnoi method, e.g. [12]. In indutrial environment, the enitivity of the flux coil could be affected by the exitence of other poible magnetic field, epecially in cae of motor upplied by power converter and intalled near each other. In thi cae, it will be neceary to meaure the magnetic field around each motor in different poition in order to define the place in which the flux meaurement i le influenced by other nearby electrical drive. The increae of the harmonic multiple of the fundamental in preence of bearing defect ha not been yet theoretically jutified. However, generally, any aymmetry in an induction motor could excite thee harmonic. The problem conit in ditinguihing if thi increae i due to a bearing fault, or to another kind of fault. Future theoretical and experimental work will be focued to olve thi problem. All the reult are encouraging future work concerning the ue of other enor (for meauring current, tray fluxe and acceleration) alo for detecting other fault of the induction machine. 474

6 VII. REFERENCES [1] W.T. Thomon, "A review of on-line condition monitoring technique for three-phae quirrel-cage induction motor Pat preent and future," in Proceeding of the IEEE Sympoium on Diagnotic for Electrical Machine, Power Electronic and Drive (SDEMPED '99), Gijon (Spain), 1999, pp [2] M.E.H. Benbouzid, "A review of induction motor ignature analyi a a medium for fault detection," IEEE Tranaction on Indutrial Electronic, vol. 47, pp , 2. [3] Motor Reliability Working Group, "Report of large motor reliability urvey of indutrial and commercial intallation Part I and II," IEEE Tranaction on Indutry Application, vol. IA21, pp , [4] W. Saadaoui and K. Jelai, "Induction motor bearing damage detection uing tator current analyi," in Proceeding of the IEEE International Conference on Power Engineering, Energy and Electrical Drive (POWERENG '211), Malaga (Spain), 211. [5] A.A. Elfeky, M.I. Maoud, and I.F. El-Arabawy, "Fault ignature production for rolling element bearing in induction motor," in Proceeding of the IEEE Conference on Compatibility in Power Electronic (CPE' 27), Gdank (Poland), 27, pp [6] J. Antoni and R.B. Randall, "On the ue of the cyclic power pectrum in rolling element bearing diagnotic," Journal of Sound and Vibration, vol. 281, pp , 25. [7] F. Immovilli, A. Bellini, R. Rubini, and C. Taoni, "Diagnoi of bearing fault in induction machine by vibration or current ignal: a critical comparion," IEEE Tranaction on Indutry Application vol. 46, pp , 21. [8] R.R. Schoen, T.G. Habetler, F. Kamran, and R.G. Bartheld, "Motor bearing damage detection uing tator current monitoring," IEEE Tranaction on Indutry Application, vol. 31, pp , [9] L. Froini and E. Bai, "Stator current and motor efficiency a indicator for different type of bearing fault in induction motor," IEEE Tranaction on Indutrial Electronic, vol. 57, pp , 21. [1] R.B. Randall and J. Antoni, "Rolling element bearing diagnotic - A tutorial," Mechanical Sytem and Signal Proceing, vol. 25, pp , 211. [11] D.G. Dorrell and W.T. Thomon, "Analyi of airgap flux, current, and vibration ignal a a function of the combination of tatic and dynamic airgap eccentricity in 3-phae induction motor," IEEE Tranaction on Indutry Application, vol. 33, pp , [12] O. Vitek, M. Janda, V. Hajek, and P. Bauer, "Detection of eccentricity and bearing fault uing tray flux monitoring," in Proceeding of the IEEE International Sympoium on Diagnoi for Electrical Machine, Power Electronic & Drive (SDEMPED '211), Bologna (Italy), 211, pp [13] L. Froini, A. Borin, L. Girometta, and G. Venchi, "Development of a leakage flux meaurement ytem for condition monitoring of electrical drive," in Proceeding of the IEEE International Sympoium on Diagnotic for Electric Machine, Power Electronic & Drive (SDEMPED '211), Bologna (Italy), 211, pp [14] H. Henao, C. Demian, and G.A. Capolino, "A frequency-domain detection of tator winding fault in induction machine uing an external flux enor," IEEE Tranaction on Indutry Application, vol. 39, pp , 23. [15] J. Penman, H.G. Sedding, B.A. Lloyd, and W.T. Fink, "Detection and location of interturn hort circuit in the tator winding of operating motor," IEEE Tranaction on Energy Converion, vol. 9, pp , [16] S.-B. Han, D.-H. Hwang, S.-H. Yi, and D.-S. Kang, "Development of diagnoi algorithm for induction motor uing flux enor," in Proceeding of the International Conference on Condition Monitoring and Diagnoi (CMD '28), Beijing (China),, 28, pp [17] A. Bellini, S. Concari, G. Francechini, C. Taoni, and A. Tocani, "Vibration, current and tray flux ignal to ae induction motor rotor condition," in Proceeding of the 32 nd IEEE Annual Conference on Indutrial Electronic (IECON '26), Pari (France), 26, pp [18] H. Henao, T. Aaf, and G.A. Capolino, "Detection of voltage ource diymmetry in an induction motor uing the meaurement of axial leakage flux," in Conference Record of the International Conference on Electrical Machine (ICEM '2) Epoo (Finland), 2, pp [19] C. Harlişca and L. Szabó, "Wavelet analyi and Park' Vector baed condition monitoring of induction machine," Journal of Computer Science and Control Sytem, vol. 4, pp , 211. [2] N. Mehala and R. Dahiya, "Detection of bearing fault of induction motor uing Park Vector Approach," International Journal of Engineering and Technology, vol. 2, pp , 21. [21] W.-Y. Chen, J.-X. Xu, and S.K. Panda, "Application of artificial intelligence technique to the tudy of machine ignature," in Proceeding of the XX IEEE International Conference on Electrical Machine (ICEM' 212), Mareille (France), 212, pp [22] B.K.N. Rao, P. Pai Srinivaa, and T.N. Nagabhuhana, "Failure diagnoi and prognoi of rolling-element bearing uing Artificial Neural Network: A critical overview," in Journal of Phyic: Conference Serie, 212. [23] M.S. Ballal, Z.J. Khan, H.M. Suryawanhi, and R.L. Sonolikar, "Induction motor: fuzzy ytem for the detection of winding inulation condition and bearing wear," Electric Power Component and Sytem, vol. 34, pp , 26. [24] K.C. Gryllia and I.A. Antoniadi, "A Support Vector Machine approach baed on phyical model training for rolling element bearing fault detection in indutrial environment," Engineering Application of Artificial Intelligence, vol. 25, pp , 211. [25] B. Samanta and C. Nataraj, "Ue of particle warm optimization for machinery fault detection," Engineering Application of Artificial Intelligence, vol. 22, pp , 29. [26] D.-M. Yang, "The application of artificial neural network to the diagnoi of induction motor bearing condition uing Hilbert-baed bipectral analyi," in Proceeding of the 5th IEEE Conference on Indutrial Electronic and Application (ICIEA '21), Taichung (Taiwan), 21, pp [27] K.S. Gaeid, H.W. Ping, M.K. Maood, and L. Szabó, "Survey of wavelet fault diagnoi and tolerant of induction machine with cae tudy," International Review of Electrical Engineering (IREE), vol. 7, pp , 212. [28] E. Ayaz, A. Ozturk, and S. Seker, "Continuou Wavelet Tranform for bearing damage detection in electric motor," in Proceeding of the IEEE Mediterranean Electrotechnical Conference (MELECON '26), Malaga (Spain), 26, pp [29] H. Henao, G.A. Capolino, and C.S. Marţiş, "On the tray flux analyi for the detection of the three-phae induction machine fault," Conference Record of the IEEE Indutry Application Conference IAS '23 (38 th IAS Annual Meeting), vol. 2, pp , 23. [3] L. Froini, A. Borin, L. Girometta, and G. Venchi, "A novel approach to detect hort circuit in low voltage induction motor by tray flux meaurement," in Proceeding of the XX IEEE International Conference on Electrical Machine (ICEM '212), Mareille (France), 212, pp