Advertisement

Diagnostics of mechanical and electrical faults in induction motors using wavelet-based features of vibration and current through support vector machine algorithms for various operating conditions

  • Purushottam Gangsar
  • Rajiv TiwariEmail author
Technical Paper
  • 18 Downloads

Abstract

Fault diagnosis of induction motors (IMs) is always a challenging task in the practical industrial field, and it is even more challenging in the case of inadequate information of IM working conditions. In this paper, a new methodology for fault detection has been proposed for IMs to detect various electrical and mechanical faults as well as their severities, where the data are unavailable at required operating conditions (i.e., speed and load) based on wavelet and support vector machine (SVM). For this, the radial, axial and tangential vibrations, and three-phase current signals are acquired from IMs having different faults. The acquired time domain signal is then transformed to time–frequency signals using continuous wavelet transform (CWT). Ten different base wavelets are used to investigate the impact of different wavelet function on the fault diagnosis of IMs. Statistical features are extracted based on the CWT, and then appropriate feature(s) are selected using the wrapper model. These features are fed to the SVM to detect whether a defect has occurred. The fault detection is performed for identical speed and load case using a number of mother wavelets. To analyze the robustness of the present system, diagnosis is attempted for various operational conditions of IMs. The result showed that the feature(s) selected using the Shannon wavelet diagnose, the fault categories of IM more accurately as compared to other wavelets, and remarkably found to be robust at all working conditions of IMs. The work is finally extended to perform the fault diagnosis when limited information is available for the training. From the results, it is observed that the proposed methodology does not only take care of the practical problem of unavailability of data at different operating conditions, but also shows good performance and takes low computation time, which are vital requirements of a condition monitoring and diagnostic system.

Keywords

Induction motor (IM) fault Continuous wavelet transform (CWT) Multi-fault diagnostic Support vector machine (SVM) Intermediary working condition 

Abbreviations

ABCA

Artificial-bee-colony algorithm

AI

Artificial intelligence

ANN

Artificial neural network

BF

Bearing fault

BP

Back propagation

BR

Bowed rotor

BRB

Broken rotor bar

CACO

Chaotic ant colony optimization algorithm

CF

Crest factor

CWC

Continues wavelet coefficient

CWT

Continues wavelet transform

CV

Cross-validation

DAG

Direct acyclic graph

DAQ

Data acquisition system

DWT

Discrete wavelet transform

FDI

Fault diagnosis and isolation

FL

Fuzzy logic

FFT

Fast Fourier transform

FNN

Fuzzy neural network

GA

Genetic algorithm

IM

Induction motor

LIBSVM

A library for support vector machine

MCSA

Motor current signature analysis

SLBS

Single-level basis selection

MFS

Machine fault simulator

MLP

Multilayer perception

MR

Misaligned rotor

ND

No defect condition of induction motor

OVA

One versus all

OVO

One versus one

PSO

Particle swarm optimization

PUF

Phase unbalance fault

PUF1

Phase unbalance fault level-1

PUF2

Phase unbalance fault level-2

RBF

Radial basis function

RMS

Root mean square

RUWPT

Recursive un-decimated wavelet packet transform

RWE

Relative wavelet energy

SOM

Self-organizing map

SVM

Support vector machine

SWF

Stator winding fault

SWF1

Stator winding fault level-1

SWF2

Stator winding fault level-2

SVs

Support vectors

UR

Unbalanced rotor

VFD

Variable frequency drive

WPT

Wavelet packet transform

WT

Wavelet transform

Notes

Acknowledgements

The authors are thankful to Dr. Dhruba Jyoti Bordoloi, Technical Officer at IIT Guwahati for his support during experimentation.

References

  1. 1.
    Bazzi AM, Krein PT (2010) Review of methods for real-time loss minimization in induction machines. IEEE Trans Ind Appl 46(6):2319–2328CrossRefGoogle Scholar
  2. 2.
    Henao H, Capolino GA, Fernandez-Cabanas M, Filippetti F, Bruzzese C, Strangas E, Hedayati-Kia S (2014) Trends in fault diagnosis for electrical machines: a review of diagnostic techniques. IEEE Ind Electron Mag 8(2):31–42CrossRefGoogle Scholar
  3. 3.
    Nandi S, Toliyat HA, Li X (2005) Condition monitoring and fault diagnosis of electrical motors—a review. IEEE Trans Energy Convers 20(4):719–729CrossRefGoogle Scholar
  4. 4.
    Lee S, Bryant MD, Karlapalem L (2006) Model-and information theory-based diagnostic method for induction motors. J Dyn Syst Meas Control 128(3):584–591CrossRefGoogle Scholar
  5. 5.
    Alsaedi MA (2015) Fault diagnosis of three-phase induction motor: a review. Opt Spec Issue Appl Opt Signal Process 4(1–1):1–8Google Scholar
  6. 6.
    Thomson WT, Orpin P (2002) Current and vibration monitoring for fault diagnosis and root cause analysis of induction motor drives. In: Proceedings of the thirty-first turbomachinery symposium, pp 61–67Google Scholar
  7. 7.
    Seshadrinath J, Singh B, Panigrahi BK (2014) Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets. IEEE Trans Power Electron 29(2):936–945CrossRefGoogle Scholar
  8. 8.
    Li W, Mechefχe CK (2006) Detection of induction motor faults: a comparison of stator current, vibration and acoustic methods. J Vib Control 12(2):165–188CrossRefGoogle Scholar
  9. 9.
    Chebil J, Noel G, Mesbah M, Deriche M (2009) Wavelet decomposition for the detection and diagnosis of faults in rolling element bearings. Jordan J Mech Ind Eng 3(4):260–267Google Scholar
  10. 10.
    Rafiee J, Rafiee MA, Tse PW (2010) Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Syst Appl 37(6):4568–4579CrossRefGoogle Scholar
  11. 11.
    Bordoloi DJ, Tiwari R (2014) Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time–frequency vibration data. Measurement 55:1–14CrossRefGoogle Scholar
  12. 12.
    Yaqub MF, Gondal I, Kamruzzaman J (2011) Envelope-wavelet packet transform for machine condition monitoring. World Acad Sci Eng Technol 59:1597–1603Google Scholar
  13. 13.
    Zhao H, Yao R, Xu L, Yuan Y, Li G, Deng W (2018) Study on a novel fault damage degree identification method using high-order differential mathematical morphology gradient spectrum entropy. Entropy 20(9):682CrossRefGoogle Scholar
  14. 14.
    Silva VAD, Pederiva R (2013) Fault detection in induction motors based on artificial intelligence. In: Surveillance 7, international conference—October 29–30, 2013, Institute of Technology of Chartres, FranceGoogle Scholar
  15. 15.
    Samanta B (2004) Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mech Syst Signal Process 18(3):625–644CrossRefGoogle Scholar
  16. 16.
    Deng W, Chen R, He B, Liu Y, Yin L, Guo J (2012) A novel two-stage hybrid swarm intelligence optimization algorithm and application. Soft Comput 16(10):1707–1722CrossRefGoogle Scholar
  17. 17.
    Deng W, Zhao H, Liu J, Yan X, Li Y, Yin L, Ding C (2015) An improved CACO algorithm based on adaptive method and multi-variant strategies. Soft Comput 19(3):701–713CrossRefGoogle Scholar
  18. 18.
    Deng W, Zhao H, Yang X, Xiong J, Sun M, Li B (2017) Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment. Appl Soft Comput 59:288–302CrossRefGoogle Scholar
  19. 19.
    Gangsar P, Tiwari R (2017) Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms. Mech Syst Signal Process 94:464–481CrossRefGoogle Scholar
  20. 20.
    Filippetti F, Franceschini G, Tassoni C, Vas P (2000) Recent developments of induction motor drives fault diagnosis using AI techniques. IEEE Trans Ind Electron 47(5):994–1004CrossRefGoogle Scholar
  21. 21.
    Zhao H, Sun M, Deng W, Yang X (2016) A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy 19(1):14CrossRefGoogle Scholar
  22. 22.
    Deng W, Yao R, Zhao H, Yang X, Li G (2017) A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm. Soft Comput.  https://doi.org/10.1007/s00500-017-2940-9 CrossRefGoogle Scholar
  23. 23.
    Peng ZK, Chu FL (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18(2):199–221CrossRefGoogle Scholar
  24. 24.
    Konar P, Chattopadhyay P (2011) Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Appl Soft Comput 11(6):4203–4211CrossRefGoogle Scholar
  25. 25.
    Yan R (2007) Base wavelet selection criteria for non-stationary vibration analysis in bearing health diagnosis. University of Massachusetts Amherst, ProQuest Dissertations Publishing, 3275786Google Scholar
  26. 26.
    Rafiee J, Rafiee MA, Prause N, Tse PW (2009) Application of Daubechies 44 in machine fault diagnostics. In: 2nd international conference on computer, control and communication. IC4 2009. IEEE, pp 1–6Google Scholar
  27. 27.
    Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using continuous wavelet transform. Appl Soft Comput 11(2):2300–2312CrossRefGoogle Scholar
  28. 28.
    Chattopadhyay P, Konar P (2014) Feature extraction using wavelet transform for multi-class fault detection of induction motor. J Inst Eng India Ser B 95(1):73–81CrossRefGoogle Scholar
  29. 29.
    Vishwakarma, H. O., Sajan, K. S., Maheshwari, B., & Dhiman, Y. D. (2015, August). Intelligent bearing fault monitoring system using support vector machine and wavelet packet decomposition for induction motors. In Power and Advanced Control Engineering (ICPACE), 2015 International Conference on (pp. 339-343). IEEEGoogle Scholar
  30. 30.
    Deng W, Zhang S, Zhao H, Yang X (2018) A novel fault diagnosis method based on integrating empirical wavelet transform and fuzzy entropy for motor bearing. IEEE Access 6(1):35042–35056CrossRefGoogle Scholar
  31. 31.
    Zgarni S, Keskes H, Braham A (2018) Nested SVDD in DAG SVM for induction motor condition monitoring. Eng Appl Artif Intell 71:210–215CrossRefGoogle Scholar
  32. 32.
    Palácios RHC, da Silva IN, Goedtel A, Godoy WF (2015) A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electr Power Syst Res 127:249–258CrossRefGoogle Scholar
  33. 33.
    Bessam B, Menacer A, Boumehraz M, Cherif H (2016) Detection of broken rotor bar faults in induction motor at low load using neural network. ISA Trans 64:241–246CrossRefGoogle Scholar
  34. 34.
    Sadooghi MS, Khadem SE (2018) Improving one class support vector machine novelty detection scheme using nonlinear features. Pattern Recogn 83:14–33CrossRefGoogle Scholar
  35. 35.
    Baccarini LMR, Silva VVR, De Menezes BR, Caminhas WM (2011) SVM practical industrial application for mechanical faults diagnostic. Expert Syst Appl 38(6):6980–6984CrossRefGoogle Scholar
  36. 36.
    Zhao H, Zuo S, Hou M, Liu W, Yu L, Yang X, Deng W (2018) A novel adaptive signal processing method based on enhanced empirical wavelet transform technology. Sensors 18(10):3323CrossRefGoogle Scholar
  37. 37.
    Devi NR, Sarma DS, Rao PR (2016) Diagnosis and classification of stator winding insulation faults on a three-phase induction motor using wavelet and MNN. IEEE Trans Dielectr Electr Insul 23(5):2543–2555CrossRefGoogle Scholar
  38. 38.
    Keskes H, Braham A (2015) Recursive undecimated wavelet packet transform and DAG SVM for induction motor diagnosis. IEEE Trans Ind Inf 11(5):1059–1066CrossRefGoogle Scholar
  39. 39.
    Yan R, Gao RX, Chen X (2014) Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process 96:1–15CrossRefGoogle Scholar
  40. 40.
    Da Silva AM, Povinelli RJ, Demerdash NA (2008) Induction machine broken bar and stator short-circuit fault diagnostics based on three-phase stator current envelopes. IEEE Trans Ind Electron 55(3):1310–1318CrossRefGoogle Scholar
  41. 41.
    Gangsar P, Tiwari R (2018) Multifault diagnosis of induction motor at intermediate operating conditions using wavelet packet transform and support vector machine. Trans ASME J Dyn Syst Meas Control 140(8):081014CrossRefGoogle Scholar
  42. 42.
    Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999CrossRefGoogle Scholar
  43. 43.
    Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167CrossRefGoogle Scholar
  44. 44.
    Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425CrossRefGoogle Scholar
  45. 45.
    Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol TIST 2(3):27:1–27:27Google Scholar
  46. 46.
    Junsheng C, Dejie Y, Yu Y (2007) Application of an impulse response wavelet to fault diagnosis of rolling bearings. Mech Syst Signal Process 21(2):920–929CrossRefGoogle Scholar
  47. 47.
    Tiwari R (2017) Rotor systems: analysis and identification. CRC Press, Taylor and Francis Group, Boca RatonGoogle Scholar
  48. 48.
    Du W, Tao J, Li Y, Liu C (2014) Wavelet leaders multifractal features based fault diagnosis of rotating mechanism. Mech Syst Signal Process 43(1):57–75CrossRefGoogle Scholar
  49. 49.
    Muralidharan V, Sugumaran V, Pandey G (2011) SVM based fault diagnosis of monoblock centrifugal pump using stationary wavelet features. Int J Des Manuf Technol IJDMT 2(1):1–6Google Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia
  2. 2.Department of Mechanical EngineeringShri G S Institute of Technology and ScienceIndoreIndia

Personalised recommendations