Parametric Fault Diagnosis of an Active Gas Bearing
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Recently research into active gas bearings has had an increase in popularity. There are several factors that can make the use of gas bearings favourable. Firstly gas bearings have extremely low friction due to the usage of gas as the lubricant which reduce the needed maintenance. Secondly gas bearings is a clean technology which makes it possible to use for food processing, air condition and applications with similar requirements. Active gas bearings are therefore useful for applications where downtime is expensive and dirty lubricants such as oil are inapplicable. In order to keep as low downtime as possible it is important to be able to determine when a fault occurs. Fault diagnosis of active gas bearings is able to minimize the necessary downtime by making certain the system is only taken offline when a fault has occurred. Usually industry demands the removal of any sensor redundancy in systems. This makes it impossible to isolate faults using passive fault diagnosis. Active fault diagnosis methods have been shown able to isolate faults when there is no sensor redundancy. This makes active fault diagnosis methods relevant for industrial systems. It is in this paper shown possible to apply active fault diagnosis to diagnose parametric faults on a controllable gas bearing. The fault diagnosis is based on a statistical detector which is able to quantify the quality of the diagnosis scheme.
KeywordsActive fault diagnosis active gas bearing closed loop fault diagnosis laboratory experiment parametric faults
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- I. F. Santos, “On the future of controllable fluid film bearings,” Mechanics & Industry, vol. 12, no. 4, pp. 275–281, 2011.Google Scholar
- S. Morosi and I. F. Santos, “Experimental investigations of active air bearings,” Proceedings of ASME Turbo Expo, vol. 7, pp. 901–910, 2012.Google Scholar
- F. G. Pierart and I. F. Santos, “Steady state characteristics of an adjustable hybrid gas bearing–computational fluid dynamics, modified reynolds equation and experimental validation,” Tribology International, vol. 229, no. 7, pp. 807–822, 2015.Google Scholar
- F. G. Pierart and I. F. Santos, “Adjustable hybrid gas bearing–influence of piezoelectrically adjusted injection on damping factors and natural frequencies of a flexible rotor operating under critical speeds,” Journal of Engineering Tribology, vol. 230, no. 10, pp. 1209–1220, 2016.Google Scholar
- J. P. Amezquita–Sanchez, M. Valtierra–Rodriguez, D. Camarena–Martinez, D. Granados–Lieberman, R. J. Romero–Troncoso, and A. Dominguez–Gonzalez, “Fractal dimension–based approach for detection of multiple combined faults on induction motors,” Journal of Vibration and Control, vol. 22, no. 17, pp. 3638–3648, 2016.CrossRefGoogle Scholar
- H. H. Niemann and N. Poulsen, “Active fault detection in MIMO systems,” Proc. of American Control Conference, pp. 1975.1980, 2014.Google Scholar
- H. H. Niemann and N. Poulsen, “Estimation of parametric fault in closed–loop systems,” Proc. of American Control Conference, pp. 201–206, 2015.Google Scholar
- L. R. S. Theisen, Advanced Control of Active Bearings–Modelling, Design And Experiments, Technical University of Denmark, Department of Electrical Engineering, 2016.Google Scholar
- S. M. Kay, Fundamentals of Statistical Signal Processing, Vol. II: Detection Theory, Prentice Hall, USA, 1998.Google Scholar
- H. H. Niemann, A YJBK Based Architecture for Fault Diagnosis and Fault–Tolerant Control, Linear System Theory, DTU publications, Denmark, 2015.Google Scholar