Skip to main content

Advertisement

SpringerLink
Log in

Bearing faults classification based on wavelet transform and artificial neural network

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

The most common types of induction rotating machine failures are the mechanical faults induced by misalignment, mechanical imbalance and bearing fault. It is well known that the vibration is the best and the earliest indicator of arising mechanical defect. Thus, this paper presents a novel practical bearing fault diagnosis method based on wavelet package decomposition (WPD) associated with neural network. Firstly, the raw signal is segmented by the use of WPD to a set of sub-signals (coefficients futures). Then, the energy related to the most sensible coefficients that contained the greatest dominant fault information is selected as a distinctive feature fault. The analysis results show that this fault indicator varies under different loads and states (healthy or defective). In order to automate the detection and the location of bearing defect, this feature can be used as an input to the artificial neural network. The proposed approach is capable of discriminating faults from four conditions of rolling bearing, the healthy bearing and the three different types of defected bearings: outer race, inner race, and ball. The experimental results prove the effectiveness of this approach.

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
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Aditi BP, Jitendra AG, Jayant VK (2016) Bearing fault diagnosis using discrete wavelet transform and artificial neural network. In: IEEE conference on applied and theoretical computing and communication technology (iCATccT), pp 394–405.

  • Ahmed H, Nandi AK (2018) Compressive sampling and fature ranking framework for bearing fault classification with vibration signals. IEEE Access 6:44731–44746

    Article  Google Scholar 

  • Asfani DA, Muhammad AK, Syafaruddin PMH, Hiyama T (2012) Temporary short circuit detection in induction motor winding using combination of wavelet transform and neural network. Expert Syst Appl 39(5):5367–5375

    Article  Google Scholar 

  • Bai H, Fu H, Wang J, Ma K, Wu T, Ma J (2018) A prediction model of ionospheric foF2 based on extreme learning machine. Radio Sci 53:1292–1301

    Article  Google Scholar 

  • Benbouzid M, Kliman G (2003) What stator current processing-based technique to use for induction motor rotor faults diagnosis. IEEE Trans Energy Convers 18:238–244

    Article  Google Scholar 

  • Bouzid MBK, Champenois G, Bellaaj NM, Signac L, Jelassi K (2007) An effective neural approach for the automatic location of stator interturn faults in induction motor. IEEE Trans Industr Electron 55:4277–4289

    Article  Google Scholar 

  • Case Western university, Bearing Data Center https://csegroups.case.edu/bearingdatacenter/home.

  • Chandra NH, Sekhar AS (2016) Fault detection in rotor bearing systems using time frequency techniques. Mech Syst Signal Pr 72–73:105–133

    Article  Google Scholar 

  • Deng W, Zhang S, Huimin Z, Yang X (2018) A novel fault diagnosis method based on integrating empirical wavelet transform and fuzzy entropy for motor bearing. IEEE J Magaz 6:35042–35056

    Google Scholar 

  • 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 59:288–302

    Article  Google Scholar 

  • Diaz M, Henriquez P, Miguel AF, Jesus BA, Giuseppe P, Donato I (2015) novel method for arly bearing fault detection based on dynamic stability easure. IEEE conference on secutity technologie, pp 43–47.

  • Ebrahimi BM, Faiz J, Lotfi FS, Pillay P (2012) Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform. Mech Syst Signal Pr 30:131–145

    Article  Google Scholar 

  • Fan Z, Huaizhong L (2015) A hybrid approach for fault diagnosis of planetary bearings using an in ternal vibration sensor. Measurement 64:71–80

    Article  Google Scholar 

  • Fan W, Zhou Q, Li J, Zhu Z (2018) A wavelet-based statistical approach for monitoring and diagnosis of compound faults with application to rolling bearings. IEEE Trans Autom Sci Eng 15:1563–1572

    Article  Google Scholar 

  • Farajzadeh MZ, Roozbeh RF, Saif M, Luis R (2016) Efficient feature extraction of vibration signals for diagnosing bearing defects in induction motors. In: IEEE international joint conference on neural networks (IJCNN), pp 4504–4511.

  • Feng Z, Zhou Y, Zuo MJ, Chu F, Chen X (2017) Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples. Measurement 103:106–132

    Article  Google Scholar 

  • Hak IG, Ki UC, Chang KC (2012) Characterizations of gun barrel vibrations of during firing based on shock response analysis and short-time Fourier transform. J Mech Sci Technol 26:1463–1470

    Article  Google Scholar 

  • Henry OO, Stephan PH (2018) Fault detection in roller bearing operating at low speed and varying loads using Bayesian robust new hidden Markov model. J Mech Sci Technol 32:4025–4036

    Article  Google Scholar 

  • Hu Q, Qin A, Zhang Q, He J, Sun G (2018) Fault diagnosis based on weighted extreme learning machine with wavelet packet decomposition and KPCA. IEEE Sens J 18:8472–8483

    Article  Google Scholar 

  • Hui L, Yuping Z, Haiqi Z (2010) Bearing fault detection and diagnosis based on order tracking and Teager-Huang transform. J Mech Sci Technol 24:811–822

    Article  Google Scholar 

  • Huo Z, YU, Pierre F, Lei S, Ianfeng J (2017) Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures. IEEE Access 5: 19442–19456. https://doi.org/10.1109/access.2017.2661967.

    Article  Google Scholar 

  • Gu YK, Zhou XQ, Yu DP, Shen YJ (2018) Fault diagnosis method of rolling bearing using principal component analysis and support vector machine. J Mech Sci Technol 32:5079–5088

    Article  Google Scholar 

  • Kai C, Xin CZ, Jun Q F, Peng FZ, Jun W (2017) Fault feature extraction and diagnosis of gearbox based on EEMD and deep briefs network. International J Rotating Mach.

  • Kia SH, Henao H, Capolino GA (2009) Diagnosis of broken bar fault in induction machines using discrete wavelet transform without slip estimation. IEEE Trans Ind Appl 45:1395–1404

    Article  Google Scholar 

  • Khunarsa P (2017) Single-signal entity approach for sung word recognition with artificial neural network and time–frequency audio features. J Eng 12:634–645

    Article  Google Scholar 

  • Kordestani M, Samadi MF, Saif M, Khorasani K (2018) A new fault diagnosis of multifunctional spoiler system using integrated artificial neural network and discrete wavelet transform methods. IEEE Sens J 18:4990–5001

    Article  Google Scholar 

  • Liu D, Cheng W, Wen W (2018) Rolling bearing fault diagnosis via ConceFT-based time-frequency reconfiguration order spectrum analysis. IEEE Access 6:67131–67143

    Article  Google Scholar 

  • Liu R, Yang B, Zio E, Chen X (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Signal Process 108:33–47

    Article  Google Scholar 

  • Sheng L, Yue S, Lanyong Z (2018) A novel fault diagnosis method based on noise-assisted MEMD and functional neural fuzzy network for rolling element bearings. IEEE Access 6:27048–27068

    Article  Google Scholar 

  • Lubo L, Maokang L, Li L (2018) Instantaneous frequency estimation based on the Wigner-Ville distribution associated with linear canonical transform (WDL). Chin J Electronics 27:1–23

    Google Scholar 

  • Mufutau A, Samuel K, Jaouher B (2017) A data-driven approach based health indicator for remaining useful life estimation of bearings. IEEE Int Conf Sci Tech Automatic Control Comput Eng 18:21–23

    Google Scholar 

  • Ptuchar R, Savakis AE (2014) LGE-KSVD: robust sparse representation classification. IEEE Trans Image Process 23:1737–1750

    Article  MATH  Google Scholar 

  • Rahul D, Dheeraj A (2015) Bearing fault classification using ANN-based Hilbert footprint analysis. IET Sci Meas Technol 9:1016–1022

    Article  Google Scholar 

  • Rai VK, Mohanty AR (2007) Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert Huang transform. Mech Syst Signal Process 21:2607–2615

    Article  Google Scholar 

  • Robert BR (2017) A history of cepstrum analysis and its application to mechanical problems. Mech Syst Signal Process 97:3–19

    Article  Google Scholar 

  • Rodriguez PH, Alonso JB, Ferrer MA, Travieso CM (2013) Application of the teager-kaiser energy operator in bearing fault diagnosis. ISA Trans 52:278–284

    Article  Google Scholar 

  • Ruquiang Y, Robert XG (2006) Hilbert-Huang transform-based vibration signal analysis for machine health monitoring. IEEE Trans Instrumentation Meas 55:2320–2329

    Article  Google Scholar 

  • Siegel D, Ly C, Jay L (2012) Methodology and framework for predicting helicopter rolling element bearing failure IEEE Trans Reliability 61(4):846–857

    Article  Google Scholar 

  • Shakya P, Kulkarni MS, Darpe AK (2015) Bearing diagnosis based on MahalanobiseTaguchieGrameSchmidt method. J Sound Vib 337:342–362

    Article  Google Scholar 

  • Soualhi A, Medjaher K, Zerhouni N (2015) Health monitoring based on Hilbert-Huang transform, support vector machine, and regression.IEEE Trans Instrumentation Meas 64:52–62

    Article  Google Scholar 

  • Sun M, Deng W and Yang X.H (2017) A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy, vol 19.

  • Toufik B, Mohamed B (2018) Bearing faults diagnosis using fuzzy expert system relying on an improved range overlaps and similarity. Expert Syst Appl, pp 134–142.

  • Wang BW, Gu XD, Ma L, Yan SS (2017) Temperature error correction based on BP neural network in meteorological WSN. Int J Sens Netw 23:265–278

    Article  Google Scholar 

  • Wei Y, Xu M, Li Y, Huang W (2016) Gearbox fault diagnosis based on local mean decomposition, permutation entropy and extreme. J Vibroeng 18:1459–1473

    Article  Google Scholar 

  • Yan RQ, Gao RX, Chen XF (2014) Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process 96:1–15

    Article  Google Scholar 

  • Yuan H, Chen J, Dong G (2017) An improved initialization method of D-KSVD algorithm for bearing fault diagnosis. J Mech Sci Technol 31:5161–5172

    Article  Google Scholar 

  • Yu J, Ding B, He Y (2018) Rolling bearing fault diagnosis based on mean multi granulation decision-theoretic rough set and non-naive Bayesian classifier. J Mech Sci Technol 32:5201–5211

    Article  Google Scholar 

  • Zhang XY, Zhou JZ (2013) Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mech Syst Signal Pr 41:127–140

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Département de Génie Électrique, Laboratoire de Génie Électrique de Biskra (LGEB), Université de Biskra, BP 145 RP, 07000, Biskra, Algérie

    Widad Laala

  2. Département de Génie Électrique, Laboratoire de Modélisation Des Systèmes Énergétiques (LMSE), Université de Biskra, BP 145 RP, 07000, Biskra, Algérie

    Asma Guedidi & Abderrazak Guettaf

Authors
  1. Widad Laala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asma Guedidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abderrazak Guettaf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Widad Laala.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Laala, W., Guedidi, A. & Guettaf, A. Bearing faults classification based on wavelet transform and artificial neural network. Int J Syst Assur Eng Manag 14, 37–44 (2023). https://doi.org/10.1007/s13198-020-01039-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-020-01039-x

Keywords

Search

Navigation