Skip to main content

2021 | OriginalPaper | Buchkapitel

An Automatic Approach to Diagnose Bearing Defects Using Time-Domain Analysis of Vibration Signal

verfasst von : Om Prakash Yadav, G. L. Pahuja

Erschienen in: Advances in Electrical and Computer Technologies

Verlag: Springer Nature Singapore

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Bearing defects are the most frequent occurring faults in any electrical machine. In this perspective, this paper presents a novel time-domain methods incorporating feature reduction method and back propagation feedforward neural network (BPNN) to identify bearing defects. For this, thirty-six standard vibration datasets related to healthy, inner raceway, and ball defects were derived from the Case Western Reserve University (CWRU) website. Four single point defects levels as 7, 14, 21, and 28 mils of inner raceway and ball defects were investigated for effective diagnosis of bearing defects. Initially, nine time-domain features were extracted from each vibration datasets, and then these features were ranked using Fisher’s ranking method to selected top four most discriminating features for effective classification of bearing conditions using BPNN algorithm. The effectiveness of proposed scheme to diagnose bearing defects was corroborated using performance parameters as accuracy (ACC), sensitivity (SE), and specificity (SP). The proposed algorithm has achieved maximum fault classification ACC as 94.87%.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat S. Nandi, H.A. Toliyat, X. Li, Condition monitoring and fault diagnosis of electrical motors–a review. IEEE Trans. Energy Convers. 20(4), 719–729 (2005)CrossRef S. Nandi, H.A. Toliyat, X. Li, Condition monitoring and fault diagnosis of electrical motors–a review. IEEE Trans. Energy Convers. 20(4), 719–729 (2005)CrossRef
2.
Zurück zum Zitat P.O. Donnell, C. Heising, C. Singh, S.J. Wells, Report of large motor reliability survey of industrial and commercial installations: part 3. IEEE Trans. Ind. Appl. IA-23(1), 153–158 (1987) P.O. Donnell, C. Heising, C. Singh, S.J. Wells, Report of large motor reliability survey of industrial and commercial installations: part 3. IEEE Trans. Ind. Appl. IA-23(1), 153–158 (1987)
3.
Zurück zum Zitat I. Howard, A Review of Rolling Element Bearing Vibration ‘Detection, Diagnosis and Prognosis’, in DSTO Aeronautical and Maritime Research Laboratory (1994) I. Howard, A Review of Rolling Element Bearing Vibration ‘Detection, Diagnosis and Prognosis’, in DSTO Aeronautical and Maritime Research Laboratory (1994)
4.
Zurück zum Zitat N. Tandon, A. Choudhury, A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribol. Int. 32(8), 469–480 N. Tandon, A. Choudhury, A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribol. Int. 32(8), 469–480
5.
Zurück zum Zitat N. Tandon, G.S. Yadava, K.M. Ramakrishna, 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 (2007)CrossRef N. Tandon, G.S. Yadava, K.M. Ramakrishna, 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 (2007)CrossRef
6.
Zurück zum Zitat P. Zhang, Y. Du, T.G. Habetler, B. Lu, A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans. Ind. Appl. 47(1), 34–46 (2011)CrossRef P. Zhang, Y. Du, T.G. Habetler, B. Lu, A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans. Ind. Appl. 47(1), 34–46 (2011)CrossRef
7.
Zurück zum Zitat M. Blodt, P. Granjon, B. Raison, G. Rostaing, Models for bearing damage detection in induction motors using stator current monitoring. IEEE Trans. Ind. Electron. 55(4), 1813–1822 (2008)CrossRef M. Blodt, P. Granjon, B. Raison, G. Rostaing, Models for bearing damage detection in induction motors using stator current monitoring. IEEE Trans. Ind. Electron. 55(4), 1813–1822 (2008)CrossRef
8.
Zurück zum Zitat F. Immovilli, A. Bellini, R. Rubini, C. Tassoni, Diagnosis of bearing faults in induction machines by vibration or current signals: a critical comparison. IEEE Trans. Ind. Appl. 46(4), 1350–1359 (2010)CrossRef F. Immovilli, A. Bellini, R. Rubini, C. Tassoni, Diagnosis of bearing faults in induction machines by vibration or current signals: a critical comparison. IEEE Trans. Ind. Appl. 46(4), 1350–1359 (2010)CrossRef
9.
Zurück zum Zitat J.R. Stack, T.G. Habetler, R.G. Harley, Fault classification and fault signature production for rolling element bearings in electric machines, in IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2003—Proceedings, pp. 172–176 (2013) J.R. Stack, T.G. Habetler, R.G. Harley, Fault classification and fault signature production for rolling element bearings in electric machines, in IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2003—Proceedings, pp. 172–176 (2013)
10.
Zurück zum Zitat S. Prabhakar, A.R. Mohanty, A.S. Sekhar, Application of discrete wavelet transform for detection of ball bearing race faults. Tribol. Int. 35(12), 793–800 (2002)CrossRef S. Prabhakar, A.R. Mohanty, A.S. Sekhar, Application of discrete wavelet transform for detection of ball bearing race faults. Tribol. Int. 35(12), 793–800 (2002)CrossRef
11.
Zurück zum Zitat S. Abbasion, A. Rafsanjani, A. Farshidianfar, N. Irani, Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mech. Syst. Signal Process. 21(7), 2933–2945 (2007)CrossRef S. Abbasion, A. Rafsanjani, A. Farshidianfar, N. Irani, Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mech. Syst. Signal Process. 21(7), 2933–2945 (2007)CrossRef
12.
Zurück zum Zitat P.D. McFadden, J.D. Smith, Model for the vibration produced by a single point defect in a rolling element bearing. J. Sound Vib. 96(1), 69–82 (1984)CrossRef P.D. McFadden, J.D. Smith, Model for the vibration produced by a single point defect in a rolling element bearing. J. Sound Vib. 96(1), 69–82 (1984)CrossRef
13.
Zurück zum Zitat D. Dyer, R.M. Stewart, Detection of rolling element bearing damage by statistical vibration analysis. J. Mech. Des. 100(2), 229 (1978) D. Dyer, R.M. Stewart, Detection of rolling element bearing damage by statistical vibration analysis. J. Mech. Des. 100(2), 229 (1978)
14.
Zurück zum Zitat F. Xi, Q. Sun, G. Krishnappa, Bearing diagnostics based on pattern recognition of statistical parameters. J. Vib. Control 6, 375–392 (2000)CrossRef F. Xi, Q. Sun, G. Krishnappa, Bearing diagnostics based on pattern recognition of statistical parameters. J. Vib. Control 6, 375–392 (2000)CrossRef
15.
Zurück zum Zitat M.D. Prieto, G. Cirrincione, A.G. Espinosa, J.A. Ortega, H. Henao, Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Trans. Ind. Electron. 60(8), 3398–3407 (2013)CrossRef M.D. Prieto, G. Cirrincione, A.G. Espinosa, J.A. Ortega, H. Henao, Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Trans. Ind. Electron. 60(8), 3398–3407 (2013)CrossRef
16.
Zurück zum Zitat P.E. William, M.W. Hoffman, Identification of bearing faults using time domain zero-crossings. Mech. Syst. Signal Process. 25(8), 3078–3088 (2011)CrossRef P.E. William, M.W. Hoffman, Identification of bearing faults using time domain zero-crossings. Mech. Syst. Signal Process. 25(8), 3078–3088 (2011)CrossRef
17.
Zurück zum Zitat X. Niu, L. Zhu, H. Ding, New statistical moments for the detection of defects in rolling element bearings. Int. J. Adv. Manuf. Technol. 26(11–12), 1268–1274 (2005)CrossRef X. Niu, L. Zhu, H. Ding, New statistical moments for the detection of defects in rolling element bearings. Int. J. Adv. Manuf. Technol. 26(11–12), 1268–1274 (2005)CrossRef
18.
Zurück zum Zitat B.R. Nayana, P. Geethanjali, Analysis of statistical time-domain features effectiveness in identification of bearing faults from vibration signal. IEEE Sens. J. 17(17), 5618–5625 (2017)CrossRef B.R. Nayana, P. Geethanjali, Analysis of statistical time-domain features effectiveness in identification of bearing faults from vibration signal. IEEE Sens. J. 17(17), 5618–5625 (2017)CrossRef
19.
Zurück zum Zitat B. Li, M.Y. Chow, Y. Tipsuwan, J.C. Hung, Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electron. 47(5), 1060–1069 (2000)CrossRef B. Li, M.Y. Chow, Y. Tipsuwan, J.C. Hung, Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electron. 47(5), 1060–1069 (2000)CrossRef
20.
Zurück zum Zitat O.P. Yadav, D. Joshi, G.L. Pahuja, Support vector machine based bearing fault detection of induction motor. Indian J. Adv. Electron. Eng. 1(1), 34–39 (2013) O.P. Yadav, D. Joshi, G.L. Pahuja, Support vector machine based bearing fault detection of induction motor. Indian J. Adv. Electron. Eng. 1(1), 34–39 (2013)
21.
Zurück zum Zitat P. Henriquez, J.B. Alonso, M.A. Ferrer, C.M. Travieso, Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Trans. Syst. Man Cybern. Syst. 44(5), 642–652 (2014)CrossRef P. Henriquez, J.B. Alonso, M.A. Ferrer, C.M. Travieso, Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Trans. Syst. Man Cybern. Syst. 44(5), 642–652 (2014)CrossRef
22.
Zurück zum Zitat Y. Lei, Z. He, Y. Zi, A new approach to intelligent fault diagnosis of rotating machinery. Expert Syst. Appl. 35(4), 1593–1600 (2008)CrossRef Y. Lei, Z. He, Y. Zi, A new approach to intelligent fault diagnosis of rotating machinery. Expert Syst. Appl. 35(4), 1593–1600 (2008)CrossRef
23.
Zurück zum Zitat S. Fu, K. Liu, Y. Xu, Y. Liu, Rolling bearing diagnosing method based on time domain analysis and adaptive fuzzy C-means clustering. Shock Vib. (2016) S. Fu, K. Liu, Y. Xu, Y. Liu, Rolling bearing diagnosing method based on time domain analysis and adaptive fuzzy C-means clustering. Shock Vib. (2016)
24.
Zurück zum Zitat P. Konar, P. Chattopadhyay, Bearing fault detection of induction motor using wavelet and support vector machines (SVMs). Appl. Soft Comput. 11(6), 4203–4211 (2011)CrossRef P. Konar, P. Chattopadhyay, Bearing fault detection of induction motor using wavelet and support vector machines (SVMs). Appl. Soft Comput. 11(6), 4203–4211 (2011)CrossRef
25.
Zurück zum Zitat T.W. Rauber, F. De Assis Boldt, F.M. Varejão, Heterogeneous feature models and feature selection applied to bearing fault diagnosis. IEEE Trans. Ind. Electron. 62(1), 637–646 (2015) T.W. Rauber, F. De Assis Boldt, F.M. Varejão, Heterogeneous feature models and feature selection applied to bearing fault diagnosis. IEEE Trans. Ind. Electron. 62(1), 637–646 (2015)
26.
Zurück zum Zitat W.A. Smith, R.B. Randall, Rolling element bearing diagnostics using the case western reserve university data: a benchmark study. Mech. Syst. Signal Process. 64–65, 100–131 (2015)CrossRef W.A. Smith, R.B. Randall, Rolling element bearing diagnostics using the case western reserve university data: a benchmark study. Mech. Syst. Signal Process. 64–65, 100–131 (2015)CrossRef
28.
Zurück zum Zitat O.P. Yadav, G.L. Pahuja, Stator winding faults monitoring using advanced classification algorithm. Int. J. Adv. Sci. Technol. 29(06), 6047–6059 (2020) O.P. Yadav, G.L. Pahuja, Stator winding faults monitoring using advanced classification algorithm. Int. J. Adv. Sci. Technol. 29(06), 6047–6059 (2020)
29.
Zurück zum Zitat O.P. Yadav, G.L. Pahuja, Bearing fault detection using logarithmic wavelet packet transform and support vector machine. Int. J. Image Graph. Signal Process. 11(5), 21–33 (2019) O.P. Yadav, G.L. Pahuja, Bearing fault detection using logarithmic wavelet packet transform and support vector machine. Int. J. Image Graph. Signal Process. 11(5), 21–33 (2019)
Metadaten
Titel
An Automatic Approach to Diagnose Bearing Defects Using Time-Domain Analysis of Vibration Signal
verfasst von
Om Prakash Yadav
G. L. Pahuja
Copyright-Jahr
2021
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-15-9019-1_106

Neuer Inhalt