Evaluation of Ultrasonic Stress Wave Transmission in Cylindrical Roller Bearings for Acoustic Emission Condition Monitoring

Scheeren, Bart; Kaminski, M.L.; Pahlavan, Lotfollah

doi:10.3390/s22041500

Cited by 12 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the geometry of the set-up, and earlier work on the transmission of elastic stress waves in bearings [22], AE associated with development of significant degradation is regarded detectable on both raceways. Development of degradation is expected around 15,000-30,000 cycles, at 50,000 cycles, and at 80,000-85,000 cycles.…”

Section: Resultsmentioning

confidence: 99%

Condition Assessment of Low-Speed Slew Bearings in Offshore Applications Using Acoustic Emission Monitoring

Scheeren

Pahlavan

2022

Lecture Notes in Civil Engineering

Self Cite

View full text Add to dashboard Cite

This study presents an approach for the detection of evolving degradation in large-scale low-speed roller bearings by clustering of Acoustic Emission (AE) events, and its application to experimental degradation data. To acquire the latter, a purpose-built linear bearing, representative of a segment of a turret bearing, has been instrumented with multiple piezoelectric AE transducers in the frequency range between 40-580 kHz. Clustering based on crosscorrelation has identified a number of significant clusters that are linked to the observed damage. The results suggest that condition monitoring based on AE waveform similarity clustering is suitable for detection and identification of degradation in a large-scale roller bearing.

show abstract

Section: Resultsmentioning

confidence: 99%

Condition Assessment of Low-Speed Slew Bearings in Offshore Applications Using Acoustic Emission Monitoring

Scheeren

Pahlavan

2022

Lecture Notes in Civil Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another limitation of the present laboratory work is that the AE sensor was located in close proximity to the test specimen, which is not always feasible in industrial settings. Thus, for practical implementation of the proposed approach, the challenges associated with AE transmission through specific complex industrial structures need to be overcome [ 82 ].…”

Section: Final Remarks Conclusion and Future Scopesmentioning

confidence: 99%

Early Detection of Subsurface Fatigue Cracks in Rolling Element Bearings by the Knowledge-Based Analysis of Acoustic Emission

Hidle

Hestmo²,

Adsen³

et al. 2022

Sensors

View full text Add to dashboard Cite

Aiming at early detection of subsurface cracks induced by contact fatigue in rotating machinery, the knowledge-based data analysis algorithm is proposed for health condition monitoring through the analysis of acoustic emission (AE) time series. A robust fault detector is proposed, and its effectiveness was demonstrated for the long-term durability test of a roller made of case-hardened steel. The reliability of subsurface crack detection was proven using independent ultrasonic inspections carried out periodically during the test. Subsurface cracks as small as 0.5 mm were identified, and their steady growth was tracked by the proposed AE technique. Challenges and perspectives of the proposed methodology are unveiled and discussed.

show abstract

“…Moreover, if the bearing may be considered nearly static relative to the propagating elastic stress waves, transmission of those stress waves in between the rolling elements is expected to remain measurable by proper hardware and sensor layout despite substantial losses. 16,17 Sako and Yoshie 18 identified naturally developed flaking in a small-scale bearing at speeds between 1 and 10 rpm. However, the induced damage was the result of running for 98% of the lifetime at 400 rpm while overloading the bearing in the range of plastic deformation.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings

Scheeren

Kaminski

Pahlavan

2023

Structural Health Monitoring

Self Cite

View full text Add to dashboard Cite

This article presents an approach to identify naturally developed damage in low-speed bearings using waveform-similarity-based clustering of acoustic emissions (AEs) under fatigue loading. The approach is motivated by the notation that each recorded AE signal from a particular damage is defined by the convolution of the source signal, transfer function of the propagation path and transfer function of the utilised sensor, and may thusly be used to identify consistent AE sources, for example due to crack growth. A sequential clustering procedure is proposed, that is based on waveform cross-correlation. The supporting theoretical background of waveform similarity, rooted in an analytical formulation of waveform propagation and transmission in complex structures, is discussed. The presented methodology is evaluated through application to AE data obtained in a low-speed run-to-failure experiment utilising a densely instrumented purpose-built linear bearing segment. The implemented sensor system comprises arrays of three types of AE transducers, that is relatively low - (40–100 kHz), mid - (95–180 kHz) and high-frequency (180–580 kHz), that are situated on both the raceways and supporting substructures of either side of the bearing. Over the course of 225,000 cycles of extension and retraction, wear has been developed. A total of about ∼2,300,000 AE signals have been recorded. Analysis of the recorded data suggests the rate of degradation increases from around 70,000 cycles onwards. Highly consistent structures of clusters indicative of a localised defect in the raceway have been identified from around 170,000 cycles onwards. These clusters are characterised by hit-rates in the range of 1–2 hits per cycle and an average similarity of 93%, they comprise about half the AE activity for the periods they have been identified for. These results highlight that the proposed cross-correlation-based clustering of AE waveforms and identification of multi-channel formations in said clusters compose a suitable methodology for assessment of damage in low-speed roller bearings.

show abstract

Evaluation of Ultrasonic Stress Wave Transmission in Cylindrical Roller Bearings for Acoustic Emission Condition Monitoring

Cited by 12 publications

References 47 publications

Condition Assessment of Low-Speed Slew Bearings in Offshore Applications Using Acoustic Emission Monitoring

Condition Assessment of Low-Speed Slew Bearings in Offshore Applications Using Acoustic Emission Monitoring

Early Detection of Subsurface Fatigue Cracks in Rolling Element Bearings by the Knowledge-Based Analysis of Acoustic Emission

Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings

Contact Info

Product

Resources

About