Experimentální studie monitorování poškození valivých ložisek pomocí metody akustické emise

An Experimental Study on Damage Monitoring of Rolling Bearings Using Acoustic Emission Method

článek ve sborníku ve WoS nebo Scopus

en

The aim of condition monitoring of rotating machinery is generally fault detection, determine its severity and prediction of remaining life of the component. Rolling-element bearings are a significant component of machines and its fault leads to critical malfunction. Various methods of condition monitoring are used to detect a damage of these components in the industry or during testing in laboratories. Conducting an endurance test series ensures an experimental data for bearing life estimation. An analysis of vibrations from tested bearings is the most widely used method for damage detection based on vibration acceleration measurement and its analysis in time or frequency domain. However, the bearing fault diagnostic using acoustic emission (AE) method is more sensitive to incipient defects than commonly used vibration-based methods. Over the past three decades, the AE based monitoring has developed as a potential tool for rolling bearing diagnostics. This paper focuses on detection and monitoring of initiation and propagation of natural defects of the grease lubricated thrust ball bearings using the AE technology. The monitoring was focused on upper ring that was replaced with a material specimen. The experiments were carried out under various loading conditions and the signal parameters such as count rate, cumulative count rate, energy and RMS were compared. In conclusion, the results from this study suggest that the AE monitoring method can be employed as a control tool for rolling contact fatigue testing of material specimens.

Tento článek se zaměřuje na detekci a monitorování vzniku a následného šíření přirozeně vzniklého poškození v tukem mazaném axiálním kuličkovém ložisku pomocí metody akustické emise. Hřídelový kroužek ložiska byl nahrazen vzorkem zkoumaného materiálu. Byly porovnávány parametry akustické emise, jako je kumulativní četnost překmitů, energie a RMS signálu ze zkoušek s různým zatížením. Závěrem můžeme říci, že výsledky naznačuje, že metoda akustické emise může být použita jako nástroj pro hodnocení kontaktní únavy materiálových vzorků.

The aim of condition monitoring of rotating machinery is generally fault detection, determine its severity and prediction of remaining life of the component. Rolling-element bearings are a significant component of machines and its fault leads to critical malfunction. Various methods of condition monitoring are used to detect a damage of these components in the industry or during testing in laboratories. Conducting an endurance test series ensures an experimental data for bearing life estimation. An analysis of vibrations from tested bearings is the most widely used method for damage detection based on vibration acceleration measurement and its analysis in time or frequency domain. However, the bearing fault diagnostic using acoustic emission (AE) method is more sensitive to incipient defects than commonly used vibration-based methods. Over the past three decades, the AE based monitoring has developed as a potential tool for rolling bearing diagnostics. This paper focuses on detection and monitoring of initiation and propagation of natural defects of the grease lubricated thrust ball bearings using the AE technology. The monitoring was focused on upper ring that was replaced with a material specimen. The experiments were carried out under various loading conditions and the signal parameters such as count rate, cumulative count rate, energy and RMS were compared. In conclusion, the results from this study suggest that the AE monitoring method can be employed as a control tool for rolling contact fatigue testing of material specimens.

Acoustic emission, rolling contact fatigue, condition monitoring, damage detection, pitting

2013

18.06.2013

The British Institute of NDT

Krakow

978-1-901892-37-6

The Tenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies

1–9

600