Detection of journal bearing vapour cavitation using vibration and acoustic emission techniques with the aid of oil film photography

Poddar, Surojit; Tandon, N.

doi:10.1016/j.triboint.2016.06.039

Cited by 30 publications

(22 citation statements)

References 32 publications

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“…(Ohtsu et al 2016;Achenbach 1975;Inaba 2016;Trampe Broch 1984) AE has been used in cavitation detection and erosion intensity evaluation, but not in erosion evolution tracking, as far as the authors know of. AE detects the incipience of cavitation efficiently (Alfayez and Mba 2005;Neill et al 1997), enabling early detection of possibly harmful cavitation, for example in journal bearing lubrication films (Poddar and Tandon 2016).…”

Section: Introductionmentioning

confidence: 99%

Shedding frequency in cavitation erosion evolution tracking

Ylönen

Franc

Miettinen

et al. 2019

International Journal of Multiphase Flow

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Cavitation erosion is a concern for most hydro machine operation. An especially damaging type of cavitation is the cloud cavitation. This type has a growth-collapse cycle in which a group of vapor bubbles grows together in a low-pressure region, to collapse almost simultaneously when the pressure recovers. Measuring the frequency of these collapse events is possible by acoustic emission (AE), as demonstrated in this study, in which a cavitation tunnel is utilized to create cloud cavitation on a sample surface. These samples were fitted with AE sensors, and the initially high frequency AE signal was demodulated to detect the relatively low frequency cloud cavitation shedding. When the cavitation number is increased, AE detects the changes in this frequency correctly, confirmed by comparing the results to video analysis and to simulations by other authors. Additionally, the frequency increases when cavitation erosion progresses, thus it provides means to track the erosion stage. The presented method can be used in detecting the transition from cloud to sheet cavitation when the cavitation number is increased, and in tracking erosion evolution in the cavitation tunnel. The method could probably be extended to hydro machine monitoring, as this type of cloud cavitation is common in hydrofoils.

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Section: Introductionmentioning

confidence: 99%

Shedding frequency in cavitation erosion evolution tracking

Ylönen

Franc

Miettinen

et al. 2019

International Journal of Multiphase Flow

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“…Some sources of AE waves are wear/deformation, friction, corrosion, crack, phase transformation, fracture, cavitation, etc. [14][15][16] The AE waves are sensed through AE sensor that can measure surface displacement in the order of picometres. 17 In addition to the development of the prediction model, an app has been developed that integrates the diagnosis model and AE signal from the journal bearing to predict the category of fault.…”

Section: Introductionmentioning

confidence: 99%

Classification and detection of cavitation, particle contamination and oil starvation in journal bearing through machine learning approach using acoustic emission signals

Poddar

Tandon

2021

Proceedings of the Institution of Mechanical Engineers, Part J:

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The ability to classify condition-monitoring data and make a decision can be imparted to a computer through the machine learning approach. In this article, the acoustic emission signals emerging from journal bearings under normal operating conditions and faulty states, namely cavitation, particle contamination and oil starvation, have been classified to develop fault-prediction model using the machine learning approach. Furthermore, an application has been developed that takes acoustic emission data as input and diagnoses the category of faults besides triggering an alarm under faulty states.

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“…AE has been successfully used in monitoring cavitation, with different approaches than the one presented here. Neill et al (1997) and Poddar and Tandon (2016) were able to detect cavitation incipience, their methods being based on the AE signals root mean squared (RMS) values that increase in amplitude and increasingly fluctuate when cavitation occurs. Analyzing parameters such as RMS and AE event energy and their fluctuation is a common approach, with typically good results (Boorsma and Fitzsimmons 2009;Boorsma and Whitworth 2011;Schmidt et al 2014Schmidt et al , 2015Look et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Cavitation Pit Distributions by Acoustic Emission

et al. 2020

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Cavitation erosion in hydraulic machinery, such as in turbines and pumps, often leads to significant reduction of the service life of the affected components, with serious consequences for their maintenance costs and operation efficiency. In this study, the potential contribution of acoustic emission (AE) measurements to the assessment of cavitation damage is evaluated from experiments in a cavitation tunnel. Stainless steel samples were exposed to cavitation and damage was characterized from pitting tests carried out on mirror-polished samples. The pits were measured using an optical profilometer and cavitation damage was characterized by pit diameter distribution. In parallel, AE time signal was measured directly from behind the samples. A dedicated signal-processing technique was developed in order to identify each burst in the AE signal and determine its amplitude. The AE amplitude distribution compares well with PVDF and pressure sensor measurements from literature. It is concluded that AE signal analysis can be used to monitor the formation of pits without visual examination of the damaged surface. This provides a basis for possible future applications of nonintrusive cavitation erosion monitoring in hydraulic machines, provided the findings remain true in a more complex environment.

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Detection of journal bearing vapour cavitation using vibration and acoustic emission techniques with the aid of oil film photography

Cited by 30 publications

References 32 publications

Shedding frequency in cavitation erosion evolution tracking

Shedding frequency in cavitation erosion evolution tracking

Classification and detection of cavitation, particle contamination and oil starvation in journal bearing through machine learning approach using acoustic emission signals

Estimation of Cavitation Pit Distributions by Acoustic Emission

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