[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Condition Monitoring and Fault Diagnosis of Induction Motors: A Review

  • Original Paper
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

There is a constant call for reduction of operational and maintenance costs of induction motors (IMs). These costs can be significantly reduced if the health of the system is monitored regularly. This allows for early detection of the degeneration of the motor health, alleviating a proactive response, minimizing unscheduled downtime, and unexpected breakdowns. The condition based monitoring has become an important task for engineers and researchers mainly in industrial applications such as railways, oil extracting mills, industrial drives, agriculture, mining industry etc. Owing to the demand and influence of condition monitoring and fault diagnosis in IMs and keeping in mind the prerequisite for future research, this paper presents the state of the art review describing different type of IM faults and their diagnostic schemes. Several monitoring techniques available for fault diagnosis of IM have been identified and represented. The utilization of non-invasive techniques for data acquisition in automatic timely scheduling of the maintenance and predicting failure aspects of dynamic machines holds a great scope in future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Goyal D, Pabla B (2016) The vibration monitoring methods and signal processing techniques for structural health monitoring: a review. Arch Comput Methods Eng 23(4):585–594

    MathSciNet  MATH  Google Scholar 

  2. Goyal D, Pabla B (2015) Condition based maintenance of machine tools-a review. CIRP J Manuf Sci Technol 10:24–35

    Google Scholar 

  3. Van Hecke B, Yoon J, He D (2016) Low speed bearing fault diagnosis using acoustic emission sensors. Appl Acoust 105:35–44

    Google Scholar 

  4. Glowacz A, Glowacz Z (2017) Diagnosis of stator faults of the single-phase induction motor using acoustic signals. Appl Acoust 117:20–27

    Google Scholar 

  5. SaravanaKumar R, Kumar KV, Roy K (2009) Fuzzy logic based fault detection in induction machines using lab view. Int J Comput Sci Netw Secur 9(9):226–243

    Google Scholar 

  6. Henao H, Capolino G-A, Fernandez-Cabanas M, Filippetti F, Bruzzese C, Strangas E, Pusca R, Estima J, Riera-Guasp M, Hedayati-Kia S (2014) Trends in fault diagnosis for electrical machines: a review of diagnostic techniques. IEEE Ind Electr Mag 8(2):31–42

    Google Scholar 

  7. Picazo-Rodenas M, Royo R, Antonino-Daviu J, Roger-Folch J (2011) Energy balance and heating curves of electric motors based on infrared thermography. In: 2011 IEEE International Symposium on industrial electronics (ISIE). IEEE, pp 591–596

  8. Medoued A, Metatla A, Boukadoum A, Bahi T, Hadjadj I (2009) Condition monitoring and diagnosis of faults in the electric induction motor. Am J Appl Sci 6(6):1133

    Google Scholar 

  9. Ilonen J, Kamarainen J-K, Lindh T, Ahola J, Kalviainen H, Partanen J (2005) Diagnosis tool for motor condition monitoring. IEEE Trans Ind Appl 41(4):963–971

    Google Scholar 

  10. Munoz-Ornelas O, Elvira-Ortiz DA, Osornio-Rios RA, Romero-Troncoso RJ, Morales-Hernandez LA (2016) Methodology for thermal analysis of induction motors with infrared thermography considering camera location. In: Industrial electronics society, IECON 2016-42nd annual conference of the IEEE, pp 7113–7118, IEEE

  11. Younus AM, Yang B-S (2012) Intelligent fault diagnosis of rotating machinery using infrared thermal image. Exp Syst Appl 39(2):2082–2091

    Google Scholar 

  12. Wong W-K, Loo C-K, Lim W-S, Tan P-N (2010) Thermal condition monitoring system using log-polar mapping, quaternion correlation and max-product fuzzy neural network classification. Neurocomputing 74(1):164–177

    Google Scholar 

  13. Zhang W, Jia M.-P, Zhu L, Yan X.-A (2017) Comprehensive overview on computational intelligence techniques for machinery condition monitoring and fault diagnosis. Chin J Mech Eng 30(4):1–14

    Google Scholar 

  14. Palmero GS, Santamaria JJ, de la Torre EM, González JP (2005) Fault detection and fuzzy rule extraction in ac motors by a neuro-fuzzy art-based system. Eng Appl Artif Intell 18(7):867–874

    Google Scholar 

  15. Gindy N, Al-Habaibeh A (1997) Condition monitoring of cutting tools using artificial neural networks. In: Proceedings of the thirty-second international matador conference, pp 299–304. Springer, Berlin

    Google Scholar 

  16. Hwang Y-R, Jen K-K, Shen Y-T (2009) Application of cepstrum and neural network to bearing fault detection. J Mech Sci Technol 23(10):2730–2737

    Google Scholar 

  17. Sugumaran V, Ramachandran K (2011) Effect of number of features on classification of roller bearing faults using SVM and PSVM. Exp Syst Appl 38(4):4088–4096

    Google Scholar 

  18. Milne R (1987) Artificial intelligence for online diagnosis. In: IEE proceedings D (control theory and applications), vol 134, pp 238–244, IET

  19. Rao B, Pai PS, Nagabhushana T (2012) Failure diagnosis and prognosis of rolling-element bearings using artificial neural networks: a critical overview. J Phys Conf Ser 364:012023

    Google Scholar 

  20. Thorsen OV, Dalva M (1999) Failure identification and analysis for high-voltage induction motors in the petrochemical industry. IEEE Trans Ind Appl 35(4):810–818

    Google Scholar 

  21. Stone GC, Boulter EA, Culbert I, Dhirani H (2004) Electrical insulation for rotating machines: design, evaluation, aging, testing, and repair, vol 21. Wiley, New York

    Google Scholar 

  22. Tavner PJ, Penman J (1987) Condition monitoring of electrical machines, vol 1. Research Studies Pre

  23. Vas P (1993) Parameter estimation, condition monitoring, and diagnosis of electrical machines, vol 27. Oxford University Press

  24. Kohler JL, Sottile J, Trutt FC (1992) Alternatives for assessing the electrical integrity of induction motors. IEEE Trans Ind Appl 28(5):1109–1117

    Google Scholar 

  25. Li W, Mechefske CK (2006) Detection of induction motor faults: a comparison of stator current, vibration and acoustic methods. J Vib Control 12(2):165–188

    MATH  Google Scholar 

  26. Tavner P (2008) Review of condition monitoring of rotating electrical machines. IET Electr Power Appl 2(4):215–247

    Google Scholar 

  27. Gol O (2007) Condition monitoring of large electrical machines. Ph.D. thesis, Branzowy Osrodek Badawczo-Rozwojowy Maszyn Elektryczncyh’Komel’

  28. Finley WR, Burke RR (1994) Troubleshooting motor problems. IEEE Trans Ind Appl 30(5):1383–1397

    Google Scholar 

  29. Singh G, Al Kazzaz SAS (2003) Induction machine drive condition monitoring and diagnostic research—a survey. Electr. Power Syst Res 64(2):145–158

    Google Scholar 

  30. Ranga C, Chandel AK (2015) Advanced tool based condition monitoring of induction machines by using labviewa review. In: 2015 IEEE UP Section Conference on electrical computer and electronics (UPCON), pp 1–6, IEEE

  31. Choqueuse V, Benbouzid M (2015) Condition monitoring of induction motors based on stator currents demodulation. Int Rev Electr Eng IREE 10(6):704–715

    Google Scholar 

  32. Irfan M, Saad N, Ibrahim R, Asirvadam VS, Alwadie AS, Sheikh MA (2017) An assessment on the non-invasive methods for condition monitoring of induction motors. In: Fault diagnosis and detection, InTech

    Google Scholar 

  33. Patel RA, Bhalja BR (2016) Condition monitoring and fault diagnosis of induction motor using support vector machine. Electr Power Compon Syst 44(6):683–692

    Google Scholar 

  34. Patel R, Giri V (2017) Condition monitoring of induction motor bearing based on bearing damage index. Arch Electr Eng 66(1):105–119

    Google Scholar 

  35. Kande M, Isaksson AJ, Thottappillil R, Taylor N (2017) Rotating electrical machine condition monitoring automationa review. Machines 5(4):24

    Google Scholar 

  36. Nunez J, Velazquez L, Hernandez L, Troncoso R, Osornio-Rios R (2016) Low-cost thermographic analysis for bearing fault detection on induction motors. J Sci Ind Res 75:412–415

    Google Scholar 

  37. Tandon N, Yadava G, Ramakrishna K (2007) A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings. Mech Syst Signal Process 21(1):244–256

    Google Scholar 

  38. Halem N, Zouzou S, Srairi K, Guedidi S, Abbood F (2013) Static eccentricity fault diagnosis using the signatures analysis of stator current and air gap magnetic flux by finite element method in saturated induction motors. Int J Syst Assur Eng Manag 4(2):118–128

    Google Scholar 

  39. Sheikh MA, Nor NM, Ibrahim T, bin Hamdan MF (2016) A new method for detection of unbalanced voltage supply through rotor harmonics and symbolic state dynamics. In: 2016 6th international conference on intelligent and advanced systems (ICIAS), pp 1–6, IEEE

  40. Glowacz A, Glowacz Z (2016) Diagnostics of stator faults of the single-phase induction motor using thermal images, moasos and selected classifiers. Measurement 93:86–93

    Google Scholar 

  41. Heising C (2007) Ieee recommended practice for the design of reliable industrial and commercial power systems. IEEE Inc., New York

    Google Scholar 

  42. Albrecht P, Appiarius J, McCoy R, Owen E, Sharma D (1986) Assessment of the reliability of motors in utility applications-updated. IEEE Trans Energy Convers 1:39–46

    Google Scholar 

  43. Thorsen OV, Dalva M (1995) A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals, and oil refineries. IEEE Trans Ind Appl 31(5):1186–1196

    Google Scholar 

  44. O’Donnell P, Heising C, Singh C, Wells S (1987) Report of large motor reliability survey of industrial and commercial installations. iii. IEEE Trans Ind Appl 23(1):153–158

  45. Hänninen S (1991) Analysis of failure and maintenance experiences of large electrical motors. Teknillinen korkeakoulu

  46. Da Silva AM (2006) Induction motor fault diagnostic and monitoring methods. Ph.D. thesis, Marquette University

  47. Kanović Ž, Matić D, Jeličić Z, Rapaić M, Jakovljević B, Kapetina M (2013) Induction motor broken rotor bar detection using vibration analysisa case study. In: 2013 9th IEEE international symposium on diagnostics for electric machines, power electronics and drives (SDEMPED), pp 64–68, IEEE

  48. Guedidi S, Zouzou S, Laala W, Sahraoui M, Yahia K (2011) Broken bar fault diagnosis of induction motors using MCSA and neural network. In: 2011 IEEE international symposium on diagnostics for electric machines, power electronics and drives (SDEMPED), pp 632–637, IEEE

  49. Garcia-Ramirez AG, Morales-Hernandez LA, Osornio-Rios RA, Benitez-Rangel JP, Garcia-Perez A, de Jesus Romero-Troncoso R (2014) Fault detection in induction motors and the impact on the kinematic chain through thermographic analysis. Electr Power Syst Res 114:1–9

    Google Scholar 

  50. Jeffali F, Kihel B, Nougaoui A, Delaunois F (2015) Monitoring and diagnostic misalignment of asynchronous machines by infrared thermography. J Mater Environ Sci 6:4

    Google Scholar 

  51. Verma AK, Sarangi S, Kolekar MH (2013) Misalignment fault detection in induction motor using rotor shaft vibration and stator current signature analysis. Int J Mechatron Manuf Syst 6(5–6):422–436

    Google Scholar 

  52. Verma AK, Sarangi S, Kolekar M (2013) Shaft misalignment detection using stator current monitoring. Int J Adv Comput Res 3(8):305

    Google Scholar 

  53. Ahamed S, Mitra M, Sengupta S, Sarkar A (2012) Identification of mass-unbalance in rotor of an induction motor through envelope analysis of motor starting current at no load. J Eng Sci Technol Rev 5(1):83–89

  54. Sadeghi I, Ehya H, Faiz J (2017) Eccentricity fault indices in large induction motors an overview. In: Power electronics, drive systems and technologies conference (PEDSTC), 2017 8th, pp 329–334, IEEE

  55. Samaga R, Vittal K, Vikas J (2011) Effect of unbalance in voltage supply on the detection of mixed air gap eccentricity in an induction motor by motor current signature analysis. In: Innovative smart grid technologies-India (ISGT India), 2011 IEEE PES, pp 108–113, IEEE

  56. Jagasics S (2010) Comprehensive analysis on the effect of static air gap eccentricity on cogging torque. In: 2010 IEEE 19th international workshop on robotics in Alpe-Adria-Danube Region (RAAD), pp 447–449, IEEE

  57. Samaga BR, Vittal K (2011) Air gap mixed eccentricity severity detection in an induction motor. In: Recent advances in intelligent computational systems (RAICS), 2011 IEEE, pp 115–119, IEEE

  58. Ahmed M, Imran K, JunaidAkhtar S (2011) Detection of eccentricity faults in machine usingfrequency spectrum technique. Int J Comput Electr Eng 3(1):111

    Google Scholar 

  59. Lopez-Perez D, Antonino-Daviu J (2017) Application of infrared thermography to failure detection in industrial induction motors: case stories. IEEE Trans Ind Appl

  60. Eftekhari M, Moallem M, Sadri S, Hsieh M-F (2013) A novel indicator of stator winding inter-turn fault in induction motor using infrared thermal imaging. Infrared Phys Technol 61:330–336

    Google Scholar 

  61. Mortazavizadeha SA, Vahedib A, Zohouric A (2012) Detection of stator winding inter-turn short circuit in induction motor using vibration specified harmonic amplitude

  62. Amaral T, Pires V, Martins J, Pires A, Crisostomo M (2007) Image processing to a neuro-fuzzy classifier for detection and diagnosis of induction motor stator fault. In: Industrial electronics society, 2007. IECON 2007. 33rd Annual conference of the IEEE, pp 2408–2413, IEEE

  63. Chattopadhyaya A, Chattopadhyay S, Sengupta S (2013) Stator current assessment of an induction motor at crawling in clarke plane 1

  64. Bapat A (2003) Ground fault detection and protection for motors

  65. Fantidis J, Karakoulidis K, Lazidis G, Potolias C, Bandekas D (2013) The study of the thermal profile of a three-phase motor under different conditions. ARPN J Eng Appl Sci 8(11):892–899

    Google Scholar 

  66. Tita MC, Bitoleanu A (2012) Technologies and pollution factors in electrical machines factory. In: 2012 international conference on applied and theoretical electricity (ICATE), pp 1–6, IEEE

  67. Harris TA (2001) Rolling bearing analysis. Wiley, New York

    Google Scholar 

  68. Ramirez-Nunez JA, Morales-Hernandez LA, Osornio-Rios RA, Antonino-Daviu JA, Romero-Troncoso RJ (2016) Self-adjustment methodology of a thermal camera for detecting faults in industrial machinery. In: Industrial electronics society, IECON 2016-42nd annual conference of the IEEE, pp 7119–7124, IEEE

  69. Othman MS, Nuawi MZ, Mohamed R (2016) Experimental comparison of vibration and acoustic emission signal analysis using kurtosis-based methods for induction motor bearing condition monitoring. Przeglad Elektrotechniczny 92(11):208–212

    Google Scholar 

  70. Kumar S, Goyal D, Dhami SS (2018) Statistical and frequency analysis of acoustic signals for condition monitoring of ball bearing. Mater Today Proc 5(2):5186–5194

    Google Scholar 

  71. Othman MS, Nuawi MZ, Mohamed R. Vibration and acoustic emission signal monitoring for detection of induction motor bearing fault

  72. Patel V, Tandon N, Pandey R (2012) Defect detection in deep groove ball bearing in presence of external vibration using envelope analysis and duffing oscillator. Measurement 45(5):960–970

    Google Scholar 

  73. Choudhury A, Tandon N (2000) Application of acoustic emission technique for the detection of defects in rolling element bearings. Tribol Int 33(1):39–45

    Google Scholar 

  74. Al-Dossary S, Hamzah RR, Mba D (2009) Observations of changes in acoustic emission waveform for varying seeded defect sizes in a rolling element bearing. Appl Acoust 70(1):58–81

    Google Scholar 

  75. Alfredson R, Mathew J (1985) Frequency domain methods for monitoring the condition of rolling element bearings. Transactions of the Institution of Engineers, Australia. Mech Eng 10(2):108–112

    Google Scholar 

  76. Eren L, Devaney MJ (2001) Motor bearing damage detection via wavelet analysis of the starting current transient. In: Instrumentation and measurement technology conference, 2001. IMTC 2001. Proceedings of the 18th IEEE, vol 3, pp 1797–1800, IEEE

  77. Pires VF, Foito D, Martins J, Pires A (2015) Detection of stator winding fault in induction motors using a motor square current signature analysis (mscsa). In: 2015 IEEE 5th international conference on power engineering, energy and electrical drives (POWERENG), pp 507–512, IEEE

  78. Mousavi S, Kar NC, Timusk M (2017) A novel parallel modelling-wavelet based mechanical fault detection using stator current signature of induction machine under variable load conditions. J Electr Eng Electr Technol 6(2):2–9

    Google Scholar 

  79. Hildebrand L (1930) Quiet induction motors. Trans Am Inst Electr Eng 49(3):848–852

    Google Scholar 

  80. Garcia-Guevara FM, Villalobos-Piña FJ, Alvarez-Salas R, Cabal-Yepez E, Gonzalez-Garcia MA (2016) Stator fault detection in induction motors by autoregressive modeling. Math Probl Eng 2016

  81. Treetrong J (2010) Fault detection and diagnosis of induction motors based on higher-order spectrum. In: Proceedings of the international multiconference of engineers and computer scientists, vol 2

  82. Siddiqui KM, Sahay K, Giri V (2015) Rotor broken bar fault detection in induction motor using transformative techniques. J Electr Eng 15(1):135–141

    Google Scholar 

  83. Hernandez-Vargas M, Cabal-Yepez E, Garcia-Perez A, Romero-Troncoso R (2012) Novel methodology for broken-rotor-bar and bearing faults detection through SVD and information entropy

  84. Glowacz A, Glowacz Z (2017) Diagnosis of the three-phase induction motor using thermal imaging. Infrared Phys Technol 81:7–16

    Google Scholar 

  85. Miljković D (2015) Brief review of motor current signature analysis. HDKBR INFO Magazin 5(1):14–26

    Google Scholar 

  86. Faiz J, Moosavi S (2016) Eccentricity fault detection-from induction machines to DFIG—a review. Renew Sustain Energy Rev 55:169–179

    Google Scholar 

  87. Vaithilingam C, Thio G (2015) Health monitoring of induction motor for vibration. J Electr Eng 1–8

  88. Shnibha R, Albarbar A, Abouhnik A, Ibrahim G (2012) A more reliable method for monitoring the condition of three-phase induction motors based on their vibrations. ISRN Mech Eng 2012

  89. Sudhakar I, AdiNarayana S, AnilPrakash M, Condition monitoring of a 3-\(\phi\) induction motor by vibration spectrum anaylsis using FFT analyser—a case study. Mater Today Proc

  90. Janier JB, Zaharia MFZ (2011) Condition monitoring system for induction motor using fuzzy logic tool. In: 2011 first international conference on informatics and computational intelligence (ICI), pp 3–7, IEEE

  91. Goyal D, Pabla Vanraj B, Dhami S (2017) Condition monitoring parameters for fault diagnosis of fixed axis gearbox: a review. Arch Comput Methods Eng 24(3):543–556

    MathSciNet  MATH  Google Scholar 

  92. Goyal D, Pabla B, Dhami S, Lachhwani K (2016) Optimization of condition-based maintenance using soft computing. Neural Comput Appl 1–16

  93. Goyal D, Pabla B (2016) Development of non-contact structural health monitoring system for machine tools. J Appl Res Technol 14(4):245–258

    Google Scholar 

  94. Vanraj, Goyal D, Saini A, Dhami S, Pabla B (2016) Intelligent predictive maintenance of dynamic systems using condition monitoring and signal processing techniquesa review. In International conference on advances in computing, communication, and automation (ICACCA)(Spring), pp 1–6, IEEE, 2016

  95. Benbouzid MEH (2000) A review of induction motors signature analysis as a medium for faults detection. IEEE Trans Ind Electr 47(5):984–993

    Google Scholar 

  96. Yamamoto GK, da Costa C, da Silva Sousa JS (2016) A smart experimental setup for vibration measurement and imbalance fault detection in rotating machinery. Case studies. Mech Syst Signal Process 4:8–18

    Google Scholar 

  97. Wadhwani S, Gupta S, Kumar V (2006) Vibration based fault diagnosis of induction motor. IETE Tech Rev 23(3):151–162

    Google Scholar 

  98. Tandon N, Choudhury A (1999) A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribol Int 32(8):469–480

    Google Scholar 

  99. Donnellan P, Condition monitoring of cooling tower fan gearboxes. In: IMECHE conference transactions 7:195–204. Professional Engineering Publishing; 1998, 2000

  100. Raghavendra K, Karabasanagouda B (2014) Frequency response analysis of deep groove ball bearing. Int J Sci Res 3(8):1920–1926

    Google Scholar 

  101. Al-Ghamd AM, Mba D (2006) A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mech Syst Signal Process 20(7):1537–1571

    Google Scholar 

  102. Gu DS, Choi BK (2011) Machinery faults detection using acoustic emission signal. In: Acoustic waves-from microdevices to helioseismology, InTech

  103. Niknam SA, Songmene V, Au YJ (2013) Proposing a new acoustic emission parameter for bearing condition monitoring in rotating machines. Trans Canad Soc Mech Eng 37(4):1105–1114

    Google Scholar 

  104. Krondl M (1933) Noise of electrical machinery. CIGRE, Paris, France (in French)

    Google Scholar 

  105. Jordan H (1950) Low noise electric motors. Verlag W, Girardet Essen

    Google Scholar 

  106. Golaski L, Gebski P, Ono K (2002) Diagnostics of reinforced concrete bridges by acoustic emission. J Acous Emission 20(2002):83–89

    Google Scholar 

  107. Kim K, Parlos AG (2002) Induction motor fault diagnosis based on neuropredictors and wavelet signal processing. IEEE/ASME Trans Mechatron 7(2):201–219

    Google Scholar 

  108. Entezami M, Stewart E, Tutcher J, Driscoll W, Ellis R, Yeo G, Zhang Z, Roberts C, Kono T, Bayram S (2014) Acoustic analysis techniques for condition monitoring of roller bearings

  109. Tan C (1990) Application of acoustic emission to the detection of bearing failures. In: International tribology conference 1990, Brisbane 2–5 December 1990: Putting Tribology to Work; Reliability and Maintainability through Lubrication and Wear Technology; Preprints of Papers, p 110, Institution of Engineers, Australia

  110. Hsu JS (1995) Monitoring of defects in induction motors through air-gap torque observation. IEEE Trans Ind Appl 31(5):1016–1021

    Google Scholar 

  111. Thomas VV, Vasudevan K, Kumar VJ (2001) Use of air-gap torque spectra for squirrel cage rotor fault identification. In: 2001 4th IEEE international conference on power electronics and drive systems, 2001. Proceedings., vol 2, pp 484–488, IEEE

  112. Thomas VV, Vasudevan K, Kumar VJ (2003) Online cage rotor fault detection using air-gap torque spectra. IEEE Trans Energy Convers 18(2):265–270

    Google Scholar 

  113. da Silva AM, Povinelli RJ, Demerdash NA (2013) Rotor bar fault monitoring method based on analysis of air-gap torques of induction motors. IEEE Trans Ind Inform 9(4):2274–2283

    Google Scholar 

  114. Kumar MJR, Banakara B (2017) Finite element analysis in the estimation of air-gap torque and surface temperature of induction machine. In: Materials science and engineering conference series, vol 225, p 012116

  115. Pillay P, Xu Z (1996) Motor current signature analysis. In: Industry applications conference, 1996. Thirty-first IAS annual meeting, IAS’96., conference record of the 1996 IEEE, vol 1, pp 587–594, IEEE

  116. Singhal A, Khandekar MA (2013) Bearing fault detection in induction motor using motor current signature analysis. Int J Adv Res Electr Electron Instrum Eng 2(7):3258–3264

    Google Scholar 

  117. Thomson WT (2009) On-line motor current signature analysis prevents premature failure of large induction motor drives. Maint Asset Manag 24(3):30–35

    Google Scholar 

  118. Thomson WT, Gilmore RJ (2003) Motor current signature analysis to detect faults in induction motor drives–fundamentals, data interpretation, and industrial case histories. In: Proceedings of the thirty-second turbomachinery symposium, pp 145–156

  119. Mehala N, Dahiya R (2007) Motor current signature analysis and its applications in induction motor fault diagnosis. Int J Syst Appl Eng Dev 2(1):29–35

    Google Scholar 

  120. Kumar KV (2011) A review of voltage and current signature diagnosis in industrial drives. Int J Power Electron Drive Syst 1(1):75

    Google Scholar 

  121. Miceli R, Gritli Y, Di Tommaso A, Filippetti F, Rossi C (2014) Vibration signature analysis for monitoring rotor broken bar in double squirrel cage induction motors based on wavelet analysis. COMPEL Int J Comput Math Electr Electron Eng 33(5):1625–1641

    Google Scholar 

  122. Thomson WT, Fenger M (2001) Current signature analysis to detect induction motor faults. IEEE Ind Appl Mag 7(4):26–34

    Google Scholar 

  123. Singh S, Kumar A, Kumar N (2014) Motor current signature analysis for bearing fault detection in mechanical systems. Proc Mater Sci 6:171–177

    Google Scholar 

  124. Granda D, Aguilar WG, Arcos-Aviles D, Sotomayor D (2017) Broken bar diagnosis for squirrel cage induction motors using frequency analysis based on MCSA and continuous wavelet transform. Math Comput Appl 22(2):30

    Google Scholar 

  125. Schulz R, Verstockt S, Vermeiren J, Loccufier M, Stockman K, Van Hoecke S (2014) Thermal imaging for monitoring rolling element bearings. In: 12th International conference on quantitative infrared thermography, pp 7–11

  126. Shehata SA, El-Goharey HS, Marei MI, Ibrahim AK (2013) Detection of induction motors rotor/stator faults using electrical signatures analysis. In: Proceedings international conference on renewable energies and power quality. Bilbao, Spain, vol 16

  127. Gaeid KS, Ping HW, Khalid M, Salih AL (2011) Fault diagnosis of induction motor using MCSA and FFT. Electr Electron Eng 1(2):85–92

    Google Scholar 

  128. Pöyhönen S (2004) Support vector machine based classification in condition monitoring of induction motors. Helsinki University of Technology

  129. Ishimwe R, Abutaleb K, Ahmed F (2014) Applications of thermal imaging in agriculturea review. Adv Remote Sens 3(03):128

    Google Scholar 

  130. Akula A, Ghosh R, Sardana H (2011) Thermal imaging and its application in defence systems. In AIP conference proceedings, vol 1391, pp 333–335, AIP

  131. Rinker J (1975) Airborne infrared thermal detection of caves and crevasses. Photogramm Eng Remote Sens 44(11)

  132. Wang M-H, Wu P-C, Jiang W-J (2015) Application of infrared thermography and extension recognize method to intelligent fault diagnosis of distribution panels. IEEJ Trans Electr Electron Eng 10(4):479–486

    Google Scholar 

  133. Ring E, Ammer K (2012) Infrared thermal imaging in medicine. Physiol Meas 33(3):R33

    Google Scholar 

  134. Feig SA, Shaber GS, Schwartz GF, Patchefsky A, Libshitz HI, Edeiken J, Nerlinger R, Curley RF, Wallace JD (1977) Thermography, mammography, and clinical examination in breast cancer screening: review of 16,000 studies. Radiology 122(1):123–127

    Google Scholar 

  135. Allred LG, Howard TR (1996) Thermal imaging is the sole basis for repairing circuit cards in the f-16 flight control panel. In: AUTOTESTCON’96, Test Technology and Commercialization. Conference Record, pp 418–424, IEEE,

  136. Sonan R, Harmand S, Pellé J, Leger D, Fakès M (2008) Transient thermal and hydrodynamic model of flat heat pipe for the cooling of electronics components. Int J Heat Mass Transf 51(25):6006–6017

    MATH  Google Scholar 

  137. da Costa Bortoni E, Yamachita RA, Guimarães JM, de Castro Santos MC (2014) Losses estimation in induction motors using infrared thermography techniques. In Proceedings of the 12th international conference on quantitative infrared thermography (QIRT 2014)

  138. Chaturvedi D, Iqbal MS, Pratap M (2015) Intelligent health monitoring system for three phase induction motor using infrared thermal image. In: 2015 international conference on energy economics and environment (ICEEE), pp 1–6, IEEE,

  139. Abdulshahed AM, Longstaff AP, Fletcher S, Myers A (2015) Thermal error modelling of machine tools based on anfis with fuzzy c-means clustering using a thermal imaging camera. Appl Math Modell 39(7):1837–1852

    Google Scholar 

  140. Singh G, Kumar TCA, Naikan V (2016) Fault diagnosis of induction motor cooling system using infrared thermography. In: 2016 IEEE 6th international conference on power systems (ICPS), pp 1–4, IEEE

  141. Singh G, Kumar TCA, Naikan V (2016) Induction motor inter turn fault detection using infrared thermographic analysis. Infrared Phys Technol 77:277–282

    Google Scholar 

  142. Mayr J, Jedrzejewski J, Uhlmann E, Donmez MA, Knapp W, Härtig F, Wendt K, Moriwaki T, Shore P, Schmitt R, Brecher C, Würz T, Wegener K (2012) Thermal issues in machine tools. CIRP Ann Manuf Technol 61(2):771–791

    Google Scholar 

  143. Khare S, Negi S (2007) Thermal (infrared) imaging sensors. Def Sci J 57(3):173

    Google Scholar 

  144. Cardone D, Pinti P, Merla A (2015) Thermal infrared imaging-based computational psychophysiology for psychometrics. In: Computational and mathematical methods in medicine, vol 2015

    Google Scholar 

  145. Bagavathiappan S, Lahiri B, Saravanan T, Philip J, Jayakumar T (2013) Infrared thermography for condition monitoring—a review. Infrared Phys Technol 60:35–55

    Google Scholar 

  146. Chien C-L, Tseng D-C (2011) Color image enhancement with exact hsi color model. Int J Innov Comput Inf Control 7(12):6691–6710

    Google Scholar 

  147. Almeida CAL, Braga AP, Nascimento S, Paiva V, Martins HJ, Torres R, Caminhas WM (2009) Intelligent thermographic diagnostic applied to surge arresters: a new approach. IEEE Trans Power Deliv 24(2):751–757

    Google Scholar 

  148. Wild W (2007) Application of infrared thermography in civil engineering. Proc Estonian Acad Sci Eng 13(4):436–444

    Google Scholar 

  149. Antonopoulos V (2006) Water movement and heat transfer simulations in a soil under ryegrass. Biosyst Eng 95(1):127–138

    Google Scholar 

  150. Al-Karawi J, Schmidt J (2004) Application of infrared thermography to the analysis of welding processes. In: 7th international conference on quantitative infrared thermography, Belgium

  151. Jadin MS, Taib S (2012) Recent progress in diagnosing the reliability of electrical equipment by using infrared thermography. Infrared Phys Technol 55(4):236–245

    Google Scholar 

  152. NFP Association (2009) NFPA 70B: recommended practice for electrical equipment maintenance. National Fire Protection Association

  153. Johnson E, Hyer P, Culotta P, Clark I (1998) Evaluation of infrared thermography as a diagnostic tool in CVD applications. J Cryst Growth 187(3):463–473

    Google Scholar 

  154. Unal M, DEmetgul M, Onat M, Kucuk H (2013) Fault diagnosis of rolling bearing based on feature extraction and neural network algorithm. Recent Adv Electr Eng Ser 10

  155. Bin G, Gao J, Li X, Dhillon B (2012) Early fault diagnosis of rotating machinery based on wavelet packetsempirical mode decomposition feature extraction and neural network. Mech Syst Signal Process 27:696–711

    Google Scholar 

  156. Saravanan N, Ramachandran K (2010) Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Exp Syst Appl 37(6):4168–4181

    Google Scholar 

  157. Unal M, Onat M, Demetgul M, Kucuk H (2014) Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement 58:187–196

    Google Scholar 

  158. Leung FH-F, Lam H-K, Ling S-H, Tam PK-S (2003) Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans Neural Netw 14(1):79–88

    Google Scholar 

  159. Abdulghafour M, El-Gamal M (1996) A fuzzy logic system for analog fault diagnosis. In: 1996 IEEE international symposium on circuits and systems, 1996. ISCAS’96, Connecting the World, vol 1, pp 97–100, IEEE

  160. Nejjari H, Benbouzid MEH (1999) Application of fuzzy logic to induction motors condition monitoring. IEEE Power Eng Rev 19(6):52–54

    Google Scholar 

  161. Benbouzid M, Nejjari H (2001) A simple fuzzy logic approach for induction motors stator condition monitoring. In: Electric machines and drives conference, 2001. IEMDC 2001. IEEE international, pp 634–639, IEEE

  162. Wang J, Hu H (2006) Vibration-based fault diagnosis of pump using fuzzy technique. Measurement 39(2):176–185

    Google Scholar 

  163. Noreesuwan T, Suksawat B (2010) Propose of unsealed deep groove ball bearing condition monitoring using sound analysis and fuzzy logic. In: 2010 international conference on control automation and systems (ICCAS), pp 409–413, IEEE

  164. Mini V, Setty S, Ushakumari S (2010) Fault detection and diagnosis of an induction motor using fuzzy logic. In: 2010 IEEE region 8 international conference on computational technologies in electrical and electronics engineering (SIBIRCON), pp 459–464, IEEE

  165. Mini V, Ushakumari S (2011) Incipient fault detection and diagnosis of induction motor using fuzzy logic. In: Recent advances in intelligent computational systems (RAICS), 2011 IEEE, pp. 675–681, IEEE

  166. Chakrabarti B, Gupta KN, Yadava GS (1995) Diagnosing turbo-generator faults with a rule-based expert system. Maintenance-Farnham 10(5):12–18

    Google Scholar 

  167. Yang B-S, Oh M-S, Tan ACC (2009) Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Exp Syst Appl 36(2):1840–1849

    Google Scholar 

  168. Ballal M, Khan Z, Suryawanshi H, Sonolikar R (2006) Induction motor: fuzzy system for the detection of winding insulation condition and bearing wear. Electr Power Compon Syst 34(2):159–171

    Google Scholar 

  169. Ballal MS, Khan ZJ, Suryawanshi HM, Sonolikar RL (2007) Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear faults in induction motor. IEEE Trans Ind Electron 54(1):250–258

    Google Scholar 

  170. Lei Y, He Z, Zi Y, Hu Q (2007) Fault diagnosis of rotating machinery based on multiple anfis combination with gas. Mech Syst Signal Process 21(5):2280–2294

    Google Scholar 

  171. Lou X, Loparo KA (2004) Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech Syst Signal Process 18(5):1077–1095

    Google Scholar 

  172. Zhang L, Xiong G, Liu H, Zou H, Guo W (2010) Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Exp Syst Appl 37(8):6077–6085

    Google Scholar 

  173. Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using machine learning methods. Exp Syst Appl 38(3):1876–1886

    Google Scholar 

  174. Widodo A, Yang B-S (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 21(6):2560–2574

    Google Scholar 

  175. Boudiaf A, Moussaoui A, Dahane A, Atoui I (2016) A comparative study of various methods of bearing faults diagnosis using the case western reserve university data. J Fail Anal Prevent 16(2):271–284

    Google Scholar 

  176. Amel B, Laatra Y, Sami S, Nourreddine D (2013) Classification and diagnosis of broken rotor bar faults in induction motor using spectral analysis and SVM. In: 2013 8th international conference and exhibition on ecological vehicles and renewable energies (EVER), pp 1–5, IEEE

  177. Das S, Koley C, Purkait P, Chakravorti S (2010) Wavelet aided SVM classifier for stator inter-turn fault monitoring in induction motors. In: Power and energy society general meeting, 2010 IEEE, pp 1–6

  178. Armaki MG, Roshanfekr R (2010) A new approach for fault detection of broken rotor bars in induction motor based on support vector machine. In: 2010 18th Iranian conference on electrical engineering (ICEE), pp 732–738, IEEE

  179. Kurek J, Osowski S (2008) Support vector machine for diagnosis of the bars of cage inductance motor. In: 15th IEEE international conference on electronics, circuits and systems, 2008. ICECS 2008. pp. 1022–1025, IEEE

  180. Fang R, Ma H (2006) Application of MCSA and SVM to induction machine rotor fault diagnosis. In: The sixth world congress on intelligent control and automation, 2006. WCICA 2006. vol 2, pp 5543–5547, IEEE

  181. Dong S, Luo T (2013) Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement 46(9):3143–3152

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, 160019, India

    Anurag Choudhary & Sudha Letha Shimi

  2. Department of Mechanical Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, 160019, India

    Deepam Goyal

  3. Department of Computational Instrumentation, CSIR-Central Scientific Instruments Organization, Chandigarh, 160030, India

    Aparna Akula

Authors
  1. Anurag Choudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deepam Goyal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sudha Letha Shimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aparna Akula
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deepam Goyal.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Choudhary, A., Goyal, D., Shimi, S.L. et al. Condition Monitoring and Fault Diagnosis of Induction Motors: A Review. Arch Computat Methods Eng 26, 1221–1238 (2019). https://doi.org/10.1007/s11831-018-9286-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-018-9286-z

Search

Navigation