Skip to main content
SpringerLink
Log in

Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier

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

Abstract

Bearings are the principal component in the induction motor responsible for 50–60% of faults in an induction motor. Hence, detecting and diagnosing bearing faults in an induction motor is essential for reliable operation. Some soft computing techniques like artificial intelligence-based classifiers are always useful in fault diagnosis. This research diagnoses the bearing fault under three vibration signal conditions: raw vibration signal, filtered vibration signal, and wavelet-based denoised vibration signal. The statistical features such as RMS, kurtosis, standard deviation, variance, etc., are extracted from each condition. The db2 wavelet is selected based on the minimum Shannon entropy criteria for the wavelet denoising. Vibration signal data is collected from the experimental setup for four bearing conditions: healthy, outer race defect, ball defect, and cage defect. Total 1600 samples are collected from 2,000,000 data points for each condition. An artificial neural network and discriminant classifier are trained and tested for fault identification. Two other classifiers from each pedigree, i.e., support vector machine and radial basis function neural network, are also analyzed to compare the classification performance. It is observed that the ANN classifier stands the best among all, with a classification accuracy of 99.58% and a minimum computational time of 1.62 s.

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

Availability of data and material

The statistical values of the features are mentioned in the manuscript, while the raw vibration dataset will be shared based upon the request.

References

  • Abid A, Khan MT, Khan MS (2017) Multidomain features-based GA optimized artificial immune system for bearing fault detection. IEEE Trans Syst Man Cybern Syst 50:348–359

    Article  Google Scholar 

  • Afia A, Rahmoune C, Benazzouz D, Merainani B, Fedala S (2020) New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network. Adv Mech Eng 12:1687814020916593

    Article  Google Scholar 

  • Amarnath M, Sugumaran V, Kumar H (2013) Exploiting sound signals for fault diagnosis of bearings using decision tree. Measurement 46:1250–1256

    Article  Google Scholar 

  • Anagnostopoulos C, Tasoulis DK, Adams NM, Pavlidis NG, Hand D (2012) Online linear and quadratic discriminant analysis with adaptive forgetting for streaming classification. Stat Anal Data Min ASA Data Sci J 5:139–166

    Article  MathSciNet  MATH  Google Scholar 

  • Bessam B, Menacer A, Boumehraz M, Cherif H (2017) Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor. Int J Syst Assur Eng Manag 8:478–488

    Article  Google Scholar 

  • Bnou K, Raghay S, Hakim A (2020) A wavelet denoising approach based on unsupervised learning model. EURASIP J Adv Signal Process 2020:1–26

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Chouhan A, Gangsar P, Porwal R, Mechefske CK (2021) Artificial neural network–based fault diagnosis for induction motors under similar, interpolated and extrapolated operating conditions. Noise Vib Worldwide. https://doi.org/10.1177/09574565211030709

    Article  Google Scholar 

  • Climente-Alarcon V, Antonino-Daviu JA, Riera-Guasp M, Vlcek M (2013) Induction motor diagnosis by advanced notch FIR filters and the Wigner-Ville distribution. IEEE Trans Ind Electron 61:4217–4227

    Article  Google Scholar 

  • Djebala A, Babouri MK, Ouelaa N (2015) Rolling bearing fault detection using a hybrid method based on empirical mode decomposition and optimized wavelet multi-resolution analysis. Int J Adv Manuf Technol 79:2093–2105

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Farsi MA, Hosseini SM (2019) Statistical distributions comparison for remaining useful life prediction of components via ANN. Int J Syst Assur Eng Manag 10:429–436

    Article  Google Scholar 

  • Faysal A, Ngui WK, Lim M (2021) Noise Eliminated Ensemble Empirical Mode Decomposition for Bearing Fault Diagnosis. J Vib Eng Technol:1–17

  • Fernández-Francos D, Martínez-Rego D, Fontenla-Romero O, Alonso-Betanzos A (2013) Automatic bearing fault diagnosis based on one-class ν-SVM. Comput Ind Eng 64:357–365

    Article  Google Scholar 

  • Filippetti F, Franceschini G, Tassoni C, Vas P (2000) Recent developments of induction motor drives fault diagnosis using AI techniques. IEEE Trans Ind Electron 47:994–1004

    Article  Google Scholar 

  • Frosini L, Harlişca C, Szabó L (2014) Induction machine bearing fault detection by means of statistical processing of the stray flux measurement. IEEE Trans Ind Electron 62:1846–1854

    Article  Google Scholar 

  • Gao H, Liang L, Chen X, Xu G (2015) Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization. Chin J Mech Eng 28:96–105

    Article  Google Scholar 

  • Glowacz A et al (2019) Detection of deterioration of three-phase induction motor using vibration signals. Meas Sci Rev 19:241–249

    Article  Google Scholar 

  • Vijay GS, Srinivasa Pai P, Sriram NS, Raj Rao BKN (2013) Radial basis function neural network based comparison of dimensionality reduction techniques for effective bearing diagnostics. Proc Inst Mech Eng Part J J Eng Tribol 227:640–653

    Article  Google Scholar 

  • Gupta P, Pradhan M (2017) Fault detection analysis in rolling element bearing: a review. Mater Today Proc 4:2085–2094

    Article  Google Scholar 

  • Gupta SD (1980) Discriminant analysis. In: Fisher RA (ed) An appreciation. Springer, Boston, pp 161–170

    Chapter  Google Scholar 

  • Hoang D-T, Kang H-J (2019) Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cogn Syst Res 53:42–50

    Article  Google Scholar 

  • Istiaque A, Islam MS (2020) Classification of Bearing Fault Using Radial Basis Neural Network and Genetic Algorithm. In: 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), IEEE, pp 503–508

  • Jin X, Zhao M, Chow TW, Pecht M (2013) Motor bearing fault diagnosis using trace ratio linear discriminant analysis. IEEE Trans Ind Electron 61:2441–2451

    Article  Google Scholar 

  • Jin Z, Han Q, Zhang K, Zhang Y (2020) An intelligent fault diagnosis method of rolling bearings based on Welch power spectrum transformation with radial basis function neural network. J Vib Control 26:629–642

    Article  MathSciNet  Google Scholar 

  • Kane P, Andhare A (2016) Application of psychoacoustics for gear fault diagnosis using artificial neural network. J Low Freq Noise Vib Act Control 35:207–220

    Article  Google Scholar 

  • Kang M, Kim J, Choi B-K, Kim J-M (2015) Envelope analysis with a genetic algorithm-based adaptive filter bank for bearing fault detection. J The Journal of the Acoustical Society of America 138:EL65-EL70

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

    Article  Google Scholar 

  • Karmakar S, Chattopadhyay S, Mitra M, Sengupta S (2016) Induction motor fault diagnosis: approach through current signature analysis. Springer

    Book  Google Scholar 

  • Laala W, Guedidi A, Guettaf A (2020) Bearing faults classification based on wavelet transform and artificial neural network. J Int J Syst Assur Eng Manag 1–8

  • Lee H-H, Nguyen N-T, Kwon J-M (2007) Bearing diagnosis using time-domain features and decision tree. In: International conference on intelligent computing, Springer, pp 952–960

  • Li B, Liu P-y, Hu R-x, Mi S-s, Fu J-p (2012) Fuzzy lattice classifier and its application to bearing fault diagnosis. Appl Soft Comput 12:1708–1719

    Article  Google Scholar 

  • Li C, Sanchez V, Zurita G, Lozada MC, Cabrera D (2016) Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time–frequency ridge enhancement. ISA Trans 60:274–284

    Article  Google Scholar 

  • Li M, Wang T, Kong Y, Chu F (2021) Synchro-reassigning transform for instantaneous frequency estimation and signal reconstruction. IEEE Trans Ind Electron 69(7):7263–7274

    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 

  • Liu Y, Xiong Z (2022) A generalized stochastic resonance based instantaneous frequency estimation method under low SNR. Mech Syst Signal Process 164:108269

    Article  Google Scholar 

  • Malik H, Alotaibi MA, Almutairi A (2022) A new hybrid model combining EMD and neural network for multi-step ahead load forecasting. J Intell Fuzzy Syst 42(2):1099–1114

    Article  Google Scholar 

  • Mao W, Feng W, Liu Y, Zhang D, Liang X (2021) A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis. Mech Syst Signal Process 150:107233

    Article  Google Scholar 

  • Mishra C, Samantaray AK, Chakraborty G (2017) Rolling element bearing fault diagnosis under slow speed operation using wavelet denoising. Measurement 103:77–86

    Article  Google Scholar 

  • Muralidharan V, Sugumaran V (2013) Selection of discrete wavelets for fault diagnosis of monoblock centrifugal pump using the J48 algorithm. Appl Artif Intell 27:1–19

    Article  Google Scholar 

  • Nandi S, Toliyat HA (199) Fault diagnosis of electrical machines-a review. In: IEEE international electric machines and drives conference. IEMDC'99. Proceedings (Cat. No. 99EX272), IEEE, pp 219–221

  • Nguyen G et al (2019) Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artif Intell Rev 52:77–124

    Article  Google Scholar 

  • Nikula R-P, Karioja K, Pylvänäinen M, Leiviskä K (2020) Automation of low-speed bearing fault diagnosis based on autocorrelation of time domain features. Mech Syst Signal Process 138:106572

    Article  Google Scholar 

  • Osman S, Wang W (2016) A morphological Hilbert-Huang transform technique for bearing fault detection. IEEE Trans Instrum Meas 65:2646–2656

    Article  Google Scholar 

  • Pan H, He X, Tang S, Meng F (2018) An improved bearing fault diagnosis method using one-dimensional CNN and LSTM. Strojniski Vestnik/J Mech Eng 64:443–452

    Google Scholar 

  • Panda AK, Rapur JS, Tiwari R (2018) Prediction of flow blockages and impending cavitation in centrifugal pumps using Support Vector Machine (SVM) algorithms based on vibration measurements. Measurement 130:44–56

    Article  Google Scholar 

  • Pandarakone SE, Masuko M, Mizuno Y, Nakamura H (2018) Deep neural network based bearing fault diagnosis of induction motor using fast Fourier transform analysis. In: 2018 IEEE energy conversion congress and exposition (ECCE). IEEE, pp 3214–3221

  • Pandhare V, Singh J, Lee J (2019) Convolutional neural network based rolling-element bearing fault diagnosis for naturally occurring and progressing defects using time-frequency domain features. In: 2019 Prognostics and system health management conference (PHM-Paris). IEEE, pp 320–326

  • Pandya D, Upadhyay S, Harsha S (2012) ANN based fault diagnosis of rolling element bearing using time-frequency domain feature. Int J Eng Sci Technol 4(6):2878–2886

    Google Scholar 

  • Parmar U, Pandya D (2021) Experimental investigation of cylindrical bearing fault diagnosis with SVM. Mater Today Proc 44:1286–1290

    Article  Google Scholar 

  • Patel RK, Giri V (2016) Feature selection and classification of mechanical fault of an induction motor using random forest classifier. Perspect Sci 8:334–337

    Article  Google Scholar 

  • Peng Z, Peter WT, Chu F (2005) A comparison study of improved Hilbert-Huang transform and wavelet transform: application to fault diagnosis for rolling bearing. Mech Syst Signal Process 19:974–988

    Article  Google Scholar 

  • Qin Y (2017) A new family of model-based impulsive wavelets and their sparse representation for rolling bearing fault diagnosis. IEEE Trans Industr Electron 65:2716–2726

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Riera-Guasp M, Antonino-Daviu JA, Capolino G-A (2014) Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: state of the art. IEEE Trans Ind Electron 62:1746–1759

    Article  Google Scholar 

  • Sakthivel N, Sugumaran V, Babudevasenapati S (2010) Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Syst Appl 37:4040–4049

    Article  Google Scholar 

  • Salau AO, Jain S (2019) Feature extraction: a survey of the types, techniques, applications. In: 2019 International Conference on Signal Processing and Communication (ICSC). IEEE, pp 158–164

  • Samanta B, Al-Balushi K (2003) Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech Syst Signal Process 17:317–328

    Article  Google Scholar 

  • Samanta B, Al-Balushi K, Al-Araimi S (2003) Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng Appl Artif Intell 16:657–665

    Article  Google Scholar 

  • Sassi S, Badri B, Thomas M (2007) A numerical model to predict damaged bearing vibrations. J Vib Control 13:1603–1628

    Article  MATH  Google Scholar 

  • Shah AK, Yadav A, Malik H (2018) EMD and ANN based intelligent model for bearing fault diagnosis. J Intell Fuzzy Syst 35:5391–5402

    Article  Google Scholar 

  • Sharma A, Jigyasu R, Mathew L, Chatterji S (2019) Bearing fault diagnosis using frequency domain features and artificial neural networks. In: Information and communication technology for intelligent systems. Springer, pp 539–547

  • Sharma V, Parey A (2016) A review of gear fault diagnosis using various condition indicators. J Procedia Eng 144:253–263

    Article  Google Scholar 

  • Shi J, Liang M, Necsulescu D-S, Guan Y (2016) Generalized stepwise demodulation transform and synchrosqueezing for time–frequency analysis and bearing fault diagnosis. J Sound Vib 368:202–222

    Article  Google Scholar 

  • Singru P, Krishnakumar V, Natarajan D, Raizada A (2018) Bearing failure prediction using Wigner-Ville distribution, modified Poincare mapping and fast Fourier transform. J Vibroengineering 20:127–137

    Article  Google Scholar 

  • Sreejith B, Verma A, Srividya A (2008) Fault diagnosis of rolling element bearing using time-domain features and neural networks. In: 2008 IEEE region 10 and the third international conference on industrial and information systems. pp 1–6, IEEE

  • Tian J, Morillo C, Azarian MH, Pecht M (2015) Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with K-nearest neighbor distance analysis. IEEE Trans Industr Electron 63:1793–1803

    Article  Google Scholar 

  • Toma RN, Prosvirin AE, Kim J-M (2020) Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors 20:1884

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Wang Q, Liu YB, He X, Liu SY, Liu JH (2014) Fault diagnosis of bearing based on KPCA and KNN method. In: Advanced Materials Research, Trans Tech Publ, pp 1491–1496

  • Widodo A, Yang B-S, Han T (2007) Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Syst Appl 32:299–312

    Article  Google Scholar 

  • William PE, Hoffman MW (2011) Identification of bearing faults using time domain zero-crossings. Mech Syst Signal Process 25:3078–3088

    Article  Google Scholar 

  • Wu C, Chen T, Jiang R, Ning L, Jiang Z (2015) ANN based multi-classification using various signal processing techniques for bearing fault diagnosis. Int J Control Autom 8:113–124

    Article  Google Scholar 

  • Wu S-D, Wu P-H, Wu C-W, Ding J-J, Wang C-C (2012) Bearing fault diagnosis based on multiscale permutation entropy and support vector machine. Entropy 14:1343–1356

    Article  MATH  Google Scholar 

  • Yahia K, Cardoso AJM, Zouzou S, Gueddidi S (2012) Broken rotor bars diagnosis in an induction motor fed from a frequency converter: experimental research. Int J Syst Assur Eng Manag 3:40–46

    Article  Google Scholar 

  • Yu X, Ding E, Chen C, Liu X, Li L (2015) A novel characteristic frequency bands extraction method for automatic bearing fault diagnosis based on Hilbert Huang transform. Sensors 15:27869–27893

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zarei J, Tajeddini MA, Karimi HR (2014) Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics 24:151–157

    Article  Google Scholar 

  • Zhang S, Zhang S, Wang B, Habetler TG (2020) Deep learning algorithms for bearing fault diagnostics—a comprehensive review. IEEE Access 8:29857–29881

    Article  Google Scholar 

  • Zhang X, Liang Y, Zhou J (2015) A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement 69:164–179

    Article  Google Scholar 

  • Zhang X, Liu Z, Wang J, Wang J (2019) Time–frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets. ISA Trans 87:225–234

    Article  Google Scholar 

  • Zhou Y, Yan S, Ren Y, Liu S (2021) Rolling bearing fault diagnosis using transient-extracting transform and linear discriminant analysis. Measurement 178:109298

    Article  Google Scholar 

  • Ziani R, Felkaoui A, Zegadi R (2017) Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion. J Intell Manuf 28:405–417

    Article  Google Scholar 

Download references

Acknowledgements

The author would like to thank the Department of Mechanical Engineering, Visvesvaraya National Institute of Technology, Nagpur, for extending the facilities for this work.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, 440010, India

    Swapnil K. Gundewar & Prasad V. Kane

Authors
  1. Swapnil K. Gundewar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prasad V. Kane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prasad V. Kane.

Ethics declarations

Conflict of interest

The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gundewar, S.K., Kane, P.V. Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier. Int J Syst Assur Eng Manag 13, 2876–2894 (2022). https://doi.org/10.1007/s13198-022-01757-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-022-01757-4

Keywords

Search

Navigation