Skip to main content
SpringerLink
Log in

ANN- and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Condition monitoring of roller element bearing defects has been one of the biggest problems in predictive maintenance since bearing failures may give catastrophic results on the machining operations and may cause downtime. Two of the well-established and widely used methods for bearing fault detection and diagnosis are the artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). For this aim, a multi-staged decision algorithm was developed in this study based on ANN and ANFIS models. Both time and frequency domain parameters extracted from the vibration and current signals were used to train the ANN and ANFIS models, which were then used to detect and diagnose the severity of the bearing fault. Experimental data collected from a shaft-bearing mechanism were used to test the performances of the two schemes. The system was operated under four different speeds, for a brand-new bearing and bearings with artificially introduced local defects with various sizes. The experimental results showed that the proposed scheme is an effective means for detecting and diagnosing bearing faults. Furthermore, the results revealed that ANFIS-based scheme was superior to the ANN-based one especially in diagnosing fault severity.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Yang H, Mathew J, Ma L (2005) Fault diagnosis of rolling element bearings using basis pursuit. Mech Syst Signal Process 19:341–356

    Article  Google Scholar 

  2. EPRI (1982) Improved motors for utility applications, EL-2678, vol 1, p 1763-1

  3. Konar P, Chattopadhyay P (2011) Bearing fault detection of induction motors using wavelet and support vector machines (SVMs). Appl Soft Comput 11:4203–4211

    Article  Google Scholar 

  4. McFadden PD, Smith JD (1984) Model for the vibration produced by a single point defect in a rolling element bearing. J Sound Vib 96(1):69–82

    Article  Google Scholar 

  5. McFadden PD, Smith JD (1985) The vibration produced by multiple point defects in a rolling element bearing. J Sound Vib 98(2):263–273

    Article  Google Scholar 

  6. McFadden PD, Smith JD (1984) Vibration monitoring of rolling element bearings by high frequency resonance technique—a review. Tribol Int 77:3–10

    Article  Google Scholar 

  7. Ocak H, Loparo KA (2004) Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data. Mech Syst Signal Process 18:515–533

    Article  Google Scholar 

  8. Ertunc HM, Ocak H, Merdoglu M, Bayram S, Cavus M (2011) Vibration analysis based localized bearing fault diagnosis under different load conditions. 12th International Workshop on Research and Education in Mechatronics, REM2011, Kocaeli, Turkey, pp 201–208

  9. McInerny SA, Dai Y (2003) Basic vibration signal processing for bearing fault detection. IEEE Transact Edu 46(1):149–156

    Article  Google Scholar 

  10. Randall RB, Antony J (2011) Rolling element bearing diagnostics—a tutorial. Mech Signal Process 25:485–520

    Article  Google Scholar 

  11. Samanta B, Nataraj C (2008) Prognostics of machine condition using soft computing. Robot Comput Integr Manuf 24:816–823

    Article  Google Scholar 

  12. Chow M, Tipsuwan Y, Hung JC (2000) Neural-network-based motor rolling bearing fault diagnosis. IEEE Transact Ind Electron 47(5):1060–1069

    Article  Google Scholar 

  13. Kowalski CT, Kowalska TO (2003) Neural network application for induction motor fault diagnosis. Math Comput Simul 63:435–448

    Article  MATH  Google Scholar 

  14. Goode PV, Chow MY (1995) Using a neural/fuzzy system to extract heuristic knowledge of incipient faults in induction motors: Part I and II. IEEE Trans Indust Electron 42(2):131–146

    Article  Google Scholar 

  15. Goode PV, Chow MY (1993) Adaptation of neural/fuzzy fault detection system. Proc IEEE Conf Decision Control 2:1733–1738

    Google Scholar 

  16. Marichal GN, Artes M, Prada JCG, Casanova O (2011) Extraction of rules for faulty bearing classification by a Neuro-Fuzzy approach. Mech Syst Signal Process 25:2073–2082

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Ballal MS, Khan ZJ, Suryawanshi HM, Sonolika 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

    Article  Google Scholar 

  19. Samhouri M, Al-Ghandoor A, Ali SA, Hinti I, Massad W (2009) An intelligent machine condition monitoring system using time-based analysis: neuro-fuzzy versus neural network. Jordan J Mech Ind Eng 3:294–305

    Google Scholar 

  20. Hong-Hee L, Nguyen NT, Kwon JM (2007) Bearing fault diagnosis using fuzzy inference optimized by neural network and genetic algorithm. J Elec Eng Technol 2(3):289–414

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Zareive J, Poshtan J (2007) Bearing fault detection using wavelet packet transform of induction motor stator current. Tribol Int 40:763–769

    Article  Google Scholar 

  23. Haykin S (1994) Neural networks—a comprehensive foundation. McMillan, New Jersey

    MATH  Google Scholar 

  24. Jang J (1993) ANFIS: adaptive network-based fuzzy inference systems. IEEE Trans Syst Man Cybernet 23:665–685

    Article  Google Scholar 

Download references

Acknowledgments

The work presented in this paper is supported by TÜBİTAK under the project code 106M280. Besides, the authors appreciate Dr. Mehmet Ucar and Dr. Abdulkadir Cengiz for invaluable contributions to the study.

Author information

Authors and Affiliations

  1. Department of Mechatronics Engineering, Kocaeli University, Umuttepe Campus, 41380, Kocaeli, Turkey

    Huseyin Metin Ertunc, Hasan Ocak & Cuneyt Aliustaoglu

Authors
  1. Huseyin Metin Ertunc
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hasan Ocak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cuneyt Aliustaoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huseyin Metin Ertunc.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ertunc, H.M., Ocak, H. & Aliustaoglu, C. ANN- and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults. Neural Comput & Applic 22 (Suppl 1), 435–446 (2013). https://doi.org/10.1007/s00521-012-0912-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-0912-7

Keywords

Search

Navigation