International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.12, No.3, Jun. 2020

Bearing Health Assessment Using Time Domain Analysis of Vibration Signal

Full Text (PDF, 1125KB), PP.27-40

Views:15   Downloads:1


Om Prakash Yadav, G. L. Pahuja

Index Terms

Bearing fault index based on time domain features (BFIT);Box/whisker plot;Inner raceway and ball defects;Interquartile range;Time domain features


Objective: Bearing defects are the most frequently occurring fault in any electrical machine. In this perspective, this manuscript proposed a novel statistical time-domain approach utilizing the vibration signal to detect incipient faults of rolling-element bearing used in three-phase induction motor. 
Methodology: To detect bearing defects, six time-domain features (TDFs) namely Mean Value (µ), Peak, Root Mean Square (RMS), Crest Factor (CRF), Skewness (SKW) and Kurtosis (K) were extracted from the standard database of the vibration signal. The standard databases of vibration signals were taken from the publicly available datacenter website of Case Western Reserve University (CWRU) relating to healthy, inner raceway and ball defects of bearing. Initially, the mean and standard deviation analysis of each considered TDFs of vibration signals were performed to discriminate the health conditions of bearing. Then, the box or whisker plot method was applied to visualize the variation in each TDF in terms of median and interquartile range (IQR) value for better analysis of bearing defects. Finally, a new index parameter termed as bearing fault index (BFIT) was also computed and this parameter predicts the bearing defects based on the mean of all considered TDFs.
Results: The results of the “mean±σ” analysis have depicted that all considered TDFs except µ feature are almost independent to operating loads, and have discerning potential to diagnose bearing defects. The computations of these TDFs are mathematically very simple. The box plot representation of TDFs of vibration databases have shown that peak, RMS, and skewness features outperforms to demarcate bearing health conditions in terms of median and IQR value. The results of quantitative analysis of BFIT parameter have shown that if the magnitude of this parameter is higher than 1.8 then bearing is supposed to be faulty at all operating loads of machine. Thus, the BFIT analysis of TDFs is more simple and reliable to discriminate the health conditions of bearing. As most of the available techniques rely on the multi-processing of vibration data that requires large processing time and complicated mathematical model, so the proposed method prove to be simple and reliable in identifying the incipient bearing defects. 

Cite This Paper

Om Prakash Yadav, G. L. Pahuja, " Bearing Health Assessment Using Time Domain Analysis of Vibration Signal", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.12, No.3, pp. 27-40, 2020.DOI: 10.5815/ijigsp.2020.03.04


[1]Nandi, S., Toliyat, H. A., Li, X.: Condition monitoring and fault diagnosis of electrical motors - A review. IEEE Transactions on Energy Conversion, 20 (4), 719–729 (2005).

[2]Donnell, P. O., Heising, C., Singh, C., Wells, S. J.: Report of Large Motor Reliability Survey of Industrial and Commercial Installations: Part 3, IEEE Transactions on Industry Applications, 23(1), 153–158 (1987).

[3]Howard, I.:  A Review of Rolling Element Bearing Vibration Detection, Diagnosis and Prognosis, DSTO Aeronautical and Maritime Research Laboratory (1994).

[4]Tandon N., Choudhury, A.: A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings,” Tribology International, 32 (8), 469–480 (1999).

[5]Tandon, N., Yadava, G. S., Ramakrishna, K. M.: A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings, Mechanical System and Signal Processing, 21(1), 244–256 (2007).

[6]Zhang, P,  Du, Y., Habetler, T. G., Lu, B.: A survey of condition monitoring and protection methods for medium-voltage induction motors, IEEE Transactions on Industry Applications, 47(1), 34–46 (2011).

[7]B. Van Hecke, J. Yoon, and D. He, “Low speed bearing fault diagnosis using acoustic emission sensors,” Appl. Acoust., 105, 35–44 (2016).

[8]O. Janssens et al., “Thermal image based fault diagnosis for rotating machinery,” Infrared Phys. Technol., 73, 78–87 (2015).

[9]M. Kumar, P. Shankar Mukherjee, and N. Mohan Misra, “Advancement and current status of wear debris analysis for machine condition monitoring: a review,” Ind. Lubr. Tribol., 65 (1), 3–11 (2013).

[10]T. J. Harvey, R. J. K. Wood, and H. E. G. Powrie, “Electrostatic wear monitoring of rolling element bearings,” Wear, 263(7-12), SPEC. ISS., 1492–1501 (2007).

[11]Blodt, M., Granjon, P., Raison, B., Rostaing, G.: Models for bearing damage detection in induction motors using stator current monitoring, IEEE Transaction on Industrial Electronics, 55(4), 1813–1822 (2008).

[12]Henriquez, P., Alonso, J. B., Ferrer, M. A., Travieso, C. M.: Review of automatic fault diagnosis systems using audio and vibration signals, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44 (5), 642–652 (2014).

[13]Immovilli, F., Bellini, A., Rubini, R., Tassoni, C.: Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparison, IEEE Transactions on Industry Applications, 46(4), 1350–1359 (2010).

[14]Stack, J. R., Habetler, T. G., Harley, R. G.: Fault classification and fault signature production for rolling  element bearings in electric machines, IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2003 - Proceedings, 172–176 (2003).

[15]Prabhakar, S., Mohanty, A. R., Sekhar, A. S.: Application of discrete wavelet transform for detection of ball bearing race faults, Tribology International, 35(12), 793–800 (2002).

[16]Abbasion, S., Rafsanjani, A.,  Farshidianfar, A., Irani, N.: Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine, Mech. Syst. Signal Process., 21(7), 2933–2945 (2007).

[17]Konar, P., Chattopadhyay, P.: Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs), Applied Soft Computing., 11(6), 4203–4211 (2011).

[18]Singh, R. S, Saini, B. S, Sunkaria R. K.: Time-varying spectral coherence investigation of cardiovascular signals based on energy concentration in healthy young and elderly subjects by the adaptive continuous Morlet wavelet transform, Innovation and Research in Biomedical Engineering, Elsevier, 39(1), 54–68 (2018).

[19]Dyer, D., Stewart, R. M.: Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis, J. Mech. Des., 100(2), 229-235 (1978).

[20]Xi, F., Sun, Q., Krishnappa, G.: Bearing Diagnostics Based on Pattern Recognition of Statistical Parameters, 2000, Journal of Vibration and Control, 6, 375–392 (2000).

[21]William, P. E., Hoffman, M. W.: Identification of bearing faults using time domain zero-crossings, Mechanical Systems and Signal Processing, 25 (8), 3078–3088 (2011).

[22]Niu, X., Zhu, L., Ding, H.: New statistical moments for the detection of defects in rolling element bearings, The International Journal of Advanced Manufacturing Technology, 26 (11), 1268–1274 (2005).

[23]Prieto, M. D., Cirrincione, G., Espinosa, A. G., Ortega, J. A., Henao, H.: 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).

[24]Yadav, O. P., Joshi, D., Pahuja, G. L.: Support Vector Machine based Bearing Fault Detection of Induction Motor, Indian J. Adv. Electron. Eng., 1(1), 34–39 (2013).

[25]Lei, Y., He, Z., Zi, Y.: A new approach to intelligent fault diagnosis of rotating machinery, Expert Syst. Appl., 35(4), 1593–1600 (2008).

[26]Ali, J. B., Fnaiech, N., Saidi, L., Morello, B. C., Fnaiech, F.: Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals,” Appl. Acoust., 89, 16–27, (2015).

[27]Nayana, B. R., Geethanjali, P.: Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults from Vibration Signal, IEEE Sensors Journal, 17 (17), 5618–5625 (2017).

[28]L. S. Dhamande and M. B. Chaudhari, “Compound gear-bearing fault feature extraction using statistical features based on time-frequency method,” Meas. J. Int. Meas. Confed.,125, 63–77, (2018).

[29]Fu, S., Liu, K., Xu, Y., Liu, Y.: Rolling bearing diagnosing method based on time domain analysis and adaptive fuzzy C -means clustering, Shock Vib., 1-9 (2016).

[30]Rauber, T. W., Assis Boldt, De, F., Varejão, F. M.: Heterogeneous feature models and feature selection applied to bearing fault diagnosis, IEEE Transaction on Industrial Electronics, 62(1), 637–646 (2015).

[31]O. P. Yadav and G. L. Pahuja, "Bearing fault detection using logarithmic wavelet packet transform and support vector machine", International Journal of Image, Graphics and Signal Processing (IJIGSP), 11(5), 21-33, (2019).

[32]W. A. Smith and 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).