Abstract
Early fault detection in rotary machines can reduce the maintenance cost and avoid unexpected failure in the production line. Vibration analysis can diagnose some of the common faults inside the rolling element bearings; however, the vibration measurement should be taken from a transducer that is located on the bearing or very close to the supporting structure, which is sometimes not feasible. This study compares acoustic and vibration signature-based methods for detecting faults inside the bearings. It uses both time and frequency based fault indicators (i.e. RMS, Kurtosis and envelope analysis) for investigating the condition of the system. Experiments were carried out on a belt-drive system with three different bearing conditions (normal, corroded and outer race fault). The experimental results show acoustic signature-based methods can detect the system’s fault from close distances, and even for relatively far distance, some bearing conditions are still detectable.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amini A, Entezami M, Papaelias M (2016) Onboard detection of railway axle bearing defects using envelope analysis of high frequency acoustic emission signals. Case Stud Nondestr Test Eval 6:8–16
Bhende AR, Awari GK, Untawale SP (2014) Comprehensive bearing condition monitoring algorithm for incipient fault detection using acoustic emission. J Tribol 2:1–30
Dwyer RF (1984) Use of the Kurtosis statistic in the frequency domain as an aid in detecting random signals. IEEE J Oceanic Eng 9
Gu DS, Choi BK (2011) Machinery faults detection using acoustic emission signal—from microdevices to helioseismology. INTECH Open Access Publisher
Hariharan V, Srinivasan P (2009) New approach of classification of rolling element bearing fault using artificial neural network. J Mech Eng ME 40:119–130
Hemmati F, Orfali W, Gadala MS (2016) Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation. Appl Acoust 104:101–118
Kamiel B, Forbes G, Entwistle R, Mazhar I, Howard I (2005) Impeller fault detection for a centrifugal pump using principal component analysis of time domain vibration features. Department of Mechanical Engineering, Universitiey Muhammadiyah Yogyakarta, Indonesia
Kamiel B, Mckee K, Entwistle R, Mazhar I, Howard I (2005) Multi fault diagnosis of the centrifugal pump using the wavelet transform and principal component analysis. In: Proceedings of the 9th IFToMM international conference on rotor dynamics. Springer, pp 555–566
Mba D, Bannister RH, Findlay GE (1999) Condition monitoring of low-speed rotating machinery using stress waves Part 1 & 2. Proc Inst Mech Eng Part E: J Process Mech Eng 213:153–185
Mckee KK, Forbes G, Mazhar I, Entwistle R, Hodkiewicz M, Howard I (2015) Cavitation sensitivity parameter analysis for centrifugal pumps based on spectral methods. Engineering Asset Management-Systems, Professional Practices and Certification. Springer
Quistgaard O (2012) Using selective envelope detection (SED) for the early detection of faults in rolling-element bearings. In: Kjær B (ed) BPT0046-EN-11. Brüel & Kjær
Roque AA, Silva TAN, Calado JMF, Dias JCQ (2009) An approach to fault diagnosis of rolling bearings. WSEAS Trans Syst Control 4:188–197
Sandoval HMU, Ramirez CAP, Méndez JEQ (2013) Acoustic emission-based early fault detection in tapered roller bearings. IngenierÃa E Investigación 33:5–10
Sato I (1990) Rotating machinery diagnosis with acoustic emission techniques. Electr Eng Japan 110:115–127
Sawalhi N, Randall RB (2004) The application of spectral kurtosis to bearing diagnostics. In: Proceeding of ACOUSTICS, November 2004 Gold Coast, Australia, pp 393–398
Sawalhi N, Randall RB (2013) Localized fault detection and diagnosis in rolling element bearings: a collection of the state of art processing algorithms. In: 15th Australian international aerospace congress
Shi DF, Wang WJ, Qu LS (2004) Defect detection for bearings using envelope spectra of wavelet transform. J Vib Acoust 126:567–573
Wang H, Cheni P (2008) Fault diagnosis for a rolling bearing used in a reciprocating machine by adaptive filtering technique and fuzzy neural network. WSEAS Trans Syst Control 7:1–6
Wang Y, **ang J, Markert R, Liang M (2016) Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: a review with applications. Mech Syst Signal Process 66–67:679–698
Yaqub M, Loparo KA (2016) An automated approach for bearing damage detection. J Vib Control 22:3253–3266
Acknowledgements
This work has been supported by Woodside Energy, CISCO and Curtin University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Najafi Amin, A., McKee, K., Mazhar, I., Bredin, A., Mullins, B., Howard, I. (2019). Acoustic Signature Based Early Fault Detection in Rolling Element Bearings. In: Mathew, J., Lim, C., Ma, L., Sands, D., Cholette, M., Borghesani, P. (eds) Asset Intelligence through Integration and Interoperability and Contemporary Vibration Engineering Technologies. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-95711-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-95711-1_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95710-4
Online ISBN: 978-3-319-95711-1
eBook Packages: EngineeringEngineering (R0)