Condition monitoring can help to detect faults of rotating machinery early and thereby prevent failures. Rolling element bearings are one of the most important machine elements to be monitored. This study focusses on rolling element bearing fault detection and localization using high-frequency, structure-borne sound, so-called acoustic emissions (AE) sensors on a dedicated roller bearing test bench. One the one hand, the high-frequency signals (range 20–1000 kHz) are analyzed and on the other hand, a demodulation algorithm is employed to down-sample the signals to frequency range of common bearing frequencies (≤10 kHz) to allow a state-of-the-art fault localization using spectral analysis of these signals. The AE results are also compared to the commonly used spectral analysis of vibration signals using conventional, piezo-electric acceleration sensors (≤10 kHz). The results show that AE is on par with vibration signals for fault localization and outperforms vibration in detecting very small surface damages and starved-lubrication conditions.
BonnettAYungC. Increased efficiency versus increased reliability. IEEE Ind Appl Mag2008; 14: 29–36.
2.
PengBBiYXueB, et al.A survey on fault diagnosis of rolling bearings. Algorithms2022; 15: 347.
3.
GuptaPPradhanM. Fault detection analysis in rolling element bearing: a review. Mater Today Proc2017; 4: 2085–2094.
4.
SharmaAAmarnathMKankarPK. Life assessment and health monitoring of rolling element bearings: an experimental study. Life Cycle Reliab Saf Eng2018; 7: 97–114.
5.
HowardI. A review of rolling element bearing vibration ‘detection, diagnosis and prognosis’. ADA291123, DEFENCE SCIENCE AND TECHNOLOGY ORGANIZATION CANBERRA (AUSTRALIA). 1994.
6.
SharmaA. Fault diagnosis of bearings using recurrences and artificial intelligence techniques. J Nondestruct Eval Diagn Progn Eng Syst2022; 5. DOI: 10.1115/1.4053773.
7.
SharmaABhardwajSKankarPK. Fault diagnosis of rolling element bearings using fractional linear prediction and AI techniques. Life Cycle Reliab Saf Eng2019; 8: 11–19.
8.
SharmaAAmarnathMKankarPK. Novel ensemble techniques for classification of rolling element bearing faults. J Braz Soc Mech Sci Eng2017; 39: 709–724.
9.
YangCQiaoZZhuR, et al.An intelligent fault diagnosis method enhanced by noise injection for machinery. IEEE Trans Instrum Meas2023; 72: 1–11.
10.
SharmaAAmarnathMKankarPK. Feature extraction and fault severity classification in ball bearings. J Vib Control2016; 22: 176–192.
11.
SharmaAAmarnathAKankarPK. Use of feature ranking techniques for defect severity estimation of rolling element bearings. Int J Acoust Vib2018; 23. DOI: 10.20855/ijav.2018.23.11104.
12.
RobertsTMTalebzadehM. Fatigue life prediction based on crack propagation and acoustic emission count rates. J Constr Steel Res2003; 59: 679–694.
13.
BruzeliusKMbaD. An initial investigation on the potential applicability of acoustic emission to rail track fault detection. NDT E Int2004; 37: 507–516.
14.
Johan SinghPMukhopadhyayCJayakumarT, et al.Understanding fatigue crack propagation in AISI 316 (N) weld using Elber’s crack closure concept: experimental results from GCMOD and acoustic emission techniques. Int J Fatigue2007; 29: 2170–2179.
15.
HaseAMishinaHWadaM. Correlation between features of acoustic emission signals and mechanical wear mechanisms. Wear2012; 292-293: 144–150.
16.
HaseA. Early detection and identification of fatigue damage in thrust ball bearings by an acoustic emission technique. Lubricants2020; 8: 37.
17.
CornelDGutiérrez GuzmánFJacobsG, et al.Condition monitoring of roller bearings using acoustic emission. Wind Energy Sci2021; 6: 367–376.
18.
Al-GhamdAMMbaD. A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mech Syst Signal Process2006; 20: 1537–1571.
19.
MiettinenJAnderssonP. Acoustic emission of rolling bearings lubricated with contaminated grease. Tribol Int2000; 33: 777–787.
20.
HouGZhangL. In-situ detection of lubrication status of bearings with acoustic emission monitoring. Wear2025; 571: 205839.
21.
KönigFSousCOuald ChaibA, et al.Machine learning based anomaly detection and classification of acoustic emission events for wear monitoring in sliding bearing systems. Tribol Int2021; 155: 106811.
22.
KönigFMarheinekeJJacobsG, et al.Data-driven wear monitoring for sliding bearings using acoustic emission signals and long short-term memory neural networks. Wear2021; 476: 203616.
23.
KönigFJacobsGStratmannA, et al.Fault detection for sliding bearings using acoustic emission signals and machine learning methods. IOP Conf Ser: Mater Sci Eng2021; 1097: 012013.
24.
PoddarSTandonN. Detection of particle contamination in journal bearing using acoustic emission and vibration monitoring techniques. Tribol Int2019; 134: 154–164.
25.
JangB-WKimC-G. Acoustic emission source localization in composite stiffened plate using triangulation method with signal magnitudes and arrival times. Adv Compos Mater2021; 30: 149–163.
26.
ZhangYLuWChuF. Planet gear fault localization for wind turbine gearbox using acoustic emission signals. Renew Energy2017; 109: 449–460.
27.
Gómez MuñozCGarcía MárquezF. A new fault location approach for acoustic emission techniques in wind turbines. Energies2016; 9: 40.
28.
McCannDFordeM. Review of NDT methods in the assessment of concrete and masonry structures. NDT E Int2001; 34: 71–84.
QuyTBKimJ-M. Crack detection and localization in a fluid pipeline based on acoustic emission signals. Mech Syst Signal Process2021; 150: 107254.
31.
ScrubyCBWadleyH. An assessment of acoustic emission for nuclear pressure vessel monitoring. Prog Nucl Energy1983; 11: 275–297.
32.
OnoK. Review on structural health evaluation with acoustic emission. Appl Sci2018; 8: 958.
33.
CiveraMSuraceC. Non-destructive techniques for the condition and structural health monitoring of wind turbines: a literature review of the last 20 years. Sensors (Basel)2022; 22: 1627.
34.
BechhoeferEQuYZhuJ, et al.Signal processing techniques to improve an acoustic emissions sensor. Annu Conf PHM Soc2013; 5. DOI: 10.36001/phmconf.2013.v5i1.2174.
35.
AntoniJXinGHamzaouiN. Fast computation of the spectral correlation. Mech Syst Signal Process2017; 92: 248–277.
36.
ElashaFGreavesMMbaD, et al.A comparative study of the effectiveness of vibration and acoustic emission in diagnosing a defective bearing in a planetry gearbox. Appl Acoust2017; 115: 181–195.
37.
van HeckeBQuYHeD. Bearing fault diagnosis based on a new acoustic emission sensor technique. Proc Inst Mech Eng Pt O J Risk Reliab2015; 229: 105–118.
38.
YoonJHeD. Planetary gearbox fault diagnostic method using acoustic emission sensors. IET Sci Meas Technol2015; 9: 936–944.
39.
van HeckeBYoonJHeD. Low speed bearing fault diagnosis using acoustic emission sensors. Appl Acoust2016; 105: 35–44.
40.
HemmerMWaagTI. A comparison of acoustic emission and vibration measurements for condition monitoring of an offshore drilling machine. Annu Conf Progn Health Manag Soc2017; 9: 278–285.
41.
LiuXWuXLiuC. A comparison of acoustic emission and vibration on bearing fault detection. In: Proceedings 2011 international conference on transportation, mechanical, and electrical engineering (TMEE), ChangChun, China, 16–18 December 2011, pp.922–926: IEEE.
42.
DyerDStewartRM. Detection of rolling element bearing damage by statistical vibration analysis. J Mech Des1978; 100: 229–235.
43.
ElforjaniM. Diagnosis and prognosis of slow speed bearing behavior under grease starvation condition. Struct Health Monit2018; 17: 532–548.
44.
GaoWZhangLDingS, et al.A comparative experimental study on fault diagnosis of rolling bearings based on vibration and acoustic emission. J Phys: Conf Ser2025; 3009: 12074.
45.
Usgame SandovalHMPedraza RamirezCAQuiroga MéndezJE. Acoustic emission-based early fault detection in tapered roller bearings. Ing Inv2013; 33: 5–10. doi:10.15446/ing.investig.v33n3.41032
