Load oscillations of induction motors can be the consequence of electrical or mechanical faults. One of the most important techniques to diagnose load oscillations is the so-called motor current signature analysis (MCSA). It has won widespread popularity owing to its non-intrusive and cost-effective performance. The common practice of MCSA is to detect the characteristic harmonic components of stator current caused by load fluctuations. However, in certain scenarios, the characteristic components can be hardly tracked from the current spectrum. Power frequency interference, nonstationary randomness of the sampled signals, and other factors could all limit the use of classical current spectrum analysis. The presented solution is the joint application of variational mode decomposition (VMD), delay addition method, multiple signal classification (MUSIC), and neural network. Specifically, VMD, delay addition and MUSIC are combined to decompose the raw signals, eliminate the power frequency leakage interference and extract the weak oscillation features with enhanced visualization. Meanwhile, a neural network is generated to describe the statistical features of the processed signals, and further realize the automatic evaluation of the load oscillation severity level. Theoretical analysis and experimental verification have been conducted to support this article. The results suggest that the proposed algorithm contributes to a more sensitive detection of the presence of load oscillations, and a more reliable evaluation of their severity with respect to traditional MCSA techniques.
KimKParlosAGBharadwajRM. Sensorless fault diagnosis of induction motors. IEEE Trans Ind Electron2003; 50: 1038–1051.
2.
WenliaoDZhiqiangGXiaoyunG, et al.Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis. Proc IMechE Part C: J Mechanical Engineering Science2017; 231: 4139–4149.
3.
LvDWangHCheC. Fault diagnosis of rolling bearing based on multimodal data fusion and deep belief network. Proc IMechE Part C: J Mechanical Engineering Science2021; 235: 6577–6585.
4.
ZhangJZhangQQinX, et al.An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions. Proc IMechE Part C: J Mechanical Engineering Science2021; 235: 8025–8038.
5.
PoncelasORoseroJACusidoJ, et al.Motor fault detection using a Rogowski sensor without an integrator. IEEE Trans Ind Electron2009; 56: 4062–4070.
6.
HenaoHFatemiSMJRCapolinoGA, et al.Wire rope fault detection in a hoisting winch system by motor torque and current signature analysis. IEEE Trans Ind Electron2011; 58: 1727–1736.
7.
PopalenyPDuyarAOzelC, et al.Electrical submersible pumps condition monitoring using motor current signature analysis. In: Abu Dhabi international petroleum exhibition & conference, Abu Dhabi, The United Arab Emirates, 12 November–15 November 2018.
8.
LuoYHanYZhangF. Research on the operation condition indicator for centrifugal pump based on sensorless monitoring technology. Proc IMechE Part E: J Process Mechanical Engineering2021; 235: 514–526.
9.
SunHYuanSLuoY, et al.Unsteady characteristics analysis of centrifugal pump operation based on motor stator current. Proc IMechE Part A: J Power and Energy2017; 231: 689–705.
10.
AliMZShabbirMNSKLiangX, et al.Machine learning based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals. IEEE Trans Ind Appl2019; 55: 2378–2391.
11.
HuangXGabetlerTGHarleyRG. Detection of rotor eccentricity faults in a closed-loop drive-connected induction motor using an artificial neural network. In: 2004 IEEE 35th annual power electronics specialists conference, Aachen, Germany, 20 June–25 June 2004.
12.
ManikandanSDuraiveluK. Fault diagnosis of various rotating equipment using machine learning approaches – A review. Proc IMechE Part E: J Process Mechanical Engineering2021; 235: 629–642.
13.
LuoYYuanSYuanJ, et al.Induction motor current signature for centrifugal pump load. Proc IMechE Part C: J Mechanical Engineering Science2016; 230: 1890–1901.
14.
StackJRHabetlerTGHarleyRG. Bearing fault detection via autoregressive stator current modeling. In: 38th IAS annual meeting on conference record of the industry applications conference, Salt Lake City, USA, 12 October–16 October 2003.
15.
ObaidRRHabetlerTG. Current-based algorithm for mechanical fault detection in induction motors with arbitrary load conditions. In: 38th IAS annual meeting on conference record of the industry applications conference, Salt Lake City, USA, 12 October–16 October 2003.
16.
ZhaoFMeiXTaoT, et al.Fault diagnosis of a machine tool rotary axis based on a motor current test and the ensemble empirical mode decomposition method. Proc IMechE Part C: J Mechanical Engineering Science2011; 225: 1121–1129.
17.
CusidÓCusidoJRomeralLOrtegaJA, et al.Fault detection in induction machines using power spectral density in wavelet decomposition. IEEE Trans Ind Electron2008; 55: 633–643.
18.
SinghSKumarN. Detection of bearing faults in mechanical systems using stator current monitoring. IEEE Trans Ind Inf2017; 13: 1341–1349.
19.
SunHYuanSLuoY. Characterization of cavitation and seal damage during pump operation by vibration and motor current signal spectra. Proc IMechE Part A: J Power and Energy2019; 233: 132–147.
20.
ChaiNYangMNiQ. Gear fault diagnosis based on dual parameter optimized resonance-based sparse signal decomposition of motor current. IEEE Trans Ind Appl2018; 54: 3782–3792.
21.
GyftakisKNSpyropoulosDVMitronikasED. Advanced detection of rotor electrical faults in induction motors at start-up. IEEE Trans Energy Convers2021; 36: 1101–1109.
22.
Garcia-BracamonteJERamirez-CortesJMRangel-MagdalenoJJ, et al.An approach on MCSA-based fault detection using independent component analysis and neural networks. IEEE Trans Instrum Meas2019; 68: 1353–1361.
23.
TremlAFlauzinoRBritoGJr. EMD and MCSA improved via Hilbert transform analysis on asynchronous machines for broken bar detection using vibration analysis. In: 2019 IEEE Milan PowerTech, Milan, Italy, 23 June–27 June 2019.
24.
SchoenRHabetlerT. Effects of time-varying loads on rotor fault detection in induction machines. IEEE Trans Ind Appl1995; 31: 900–906.
25.
SallesGFilippettiFHabetlerG, et al.Monitoring of induction motor load by neural network techniques. IEEE Trans Power Electron2000; 15: 762–768.
26.
BlodtMChabertMRegnierJ, et al.Fault indicators for stator current based detection of torque oscillations in induction motors at variable speed using time-frequency analysis. In: The 3rd IET international conference on power electronics, machines and drives, 4 April–6 April 2006.
27.
BoseBK. Power electronics and AC drives. NJ, USA: Prentice Hall, Englewood Cliffs, 1986.
28.
PintoJOBoseBKda SilvaLEB. A stator-flux-oriented vector-controlled induction motor drive with space-vector PWM and flux-vector synthesis by neural networks. IEEE Trans Ind Appl2001; 37: 1308–1318.
ImaichiKYoshidaYTsujimotoY. An analysis of unsteady torque on a two-dimensional radial impeller. J Fluids Eng1982; 104: 228–234.
31.
BlödtMChabertMRegnierJ, et al.Mechanical load fault detection in induction motors by stator current time-frequency analysis. IEEE Trans Ind Appl2006; 6: 1454–1463.
32.
LuoYHanYDongJ. Research of unsteady characteristics of torque in centrifugal pumps. In: ASME-JSME-KSME 2019 8th joint fluids engineering conference, San Francisco, USA, 28 July–01 August.
33.
Puche-PanaderoRPineda-SanchezMRiera-GuaspM, et al.Improved resolution of the MCSA method via Hilbert transform, enabling the diagnosis of rotor asymmetries at very low slip. IEEE Trans Energy Convers2009; 24: 52–59.
34.
RillingGFlandrinPGonçalvesP.On empirical mode decomposition and its algorithms. In: IEEE-EURASIP workshop on nonlinear signal and image processing. NSIP-03, Grado, Italy, 2003.
35.
DragomiretskiyKZossoD. Variational mode decomposition. IEEE Trans Signal Process2014; 62: 531–544.
36.
Zamudio-RamírezIRamirez-NúñezJAAntonino-DaviuJ, et al.Automatic diagnosis of electromechanical faults in induction motors based on the transient analysis of the stray flux via MUSIC methods. IEEE Trans Ind Appl2020; 56: 3604–3613.
37.
KiraKRendellL. A practical approach to feature selection. In: Proceedings of the ninth conference in machine learning, Aberdeen, Scotland, UK, 1 July–3 July 1992.
38.
KononenkoI. Estimating attributes: analysis and extensions of RELIEF. PhD Thesis, University of Ljubljana, Republika Slovenija, 1994.
39.
HuangYMcCullaghPJBlackND. An optimization of ReliefF for classification in large datasets. Data Knowl Eng2009; 68: 1348–1356.
40.
NandiSToliyatHALiX. Condition monitoring and fault diagnosis of electrical motors—A review. IEEE Trans Energy Convers2005; 20: 719–729.
41.
SchoenRRHabetlerTGKamranF, et al.Motor bearing damage detection using stator current monitoring. IEEE Trans Ind Appl1995; 31: 1274–1279.
42.
YaziciBKlimanGBPremerlaniWJ, et al.An adaptive, on-line, statistical method for bearing fault detection using stator current. In: Proc. IEEE industry applications soc. annual meeting conf., New Orleans, USA, 05–09 October 1997.
43.
NandiSBharadwajRMToliyatHA, et al.Performance analysis of a three-phase induction motor under mixed eccentricity condition. IEEE Trans Energy Convers2002; 17: 392–399.
44.
AndriamalalaRNRazikHBaghliL, et al.Eccentricity fault diagnosis of a dual-stator winding induction machine drive considering the slotting effects. IEEE Trans Ind Electron2008; 55: 4238–4251.
45.
DrifMCardosoAJM. Airgap-eccentricity fault diagnosis, in three-phase induction motors, by the complex apparent power signature analysis. IEEE Trans Ind Electron2008; 55: 1404–1410.
46.
HenaoHFatemiSMJRCapolinoGA, et al.Wire rope fault detection in a hoisting winch system by motor torque and current signature analysis. IEEE Trans Ind Electron2011; 58: 1727–1736.
47.
CameronJRThomsonWTDowAB. Vibration and current monitoring for detecting airgap eccentricity in large induction motors. Electric Power Applications Iee Proceedings B1986; 133: 155–163.
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