Abstract
Acoustic emission (AE) has been studied for monitoring the condition of mechanical seals by many researchers, however to the best knowledge of the authors, typical fault cases and their effects on tribological behaviour of mechanical seals have not yet been successfully investigated. In this paper, AE signatures from common faults of mechanical seals are studied in association with tribological behaviour of sealing gap to develop more reliable condition monitoring approaches. A purpose-built test rig was employed for recording AE signals from the mechanical seals under healthy and faulty conditions. The collected data was then processed using time domain and frequency domain analysis methods. The study has shown that AE signal parameters: root mean squared (RMS) along with AE spectrum, allows fault conditions including dry running, spring out and defective seal faces to be diagnosed under a wide range of operating conditions. However, when mechanical seals operate around their transition point, conventional signal processing methods may not allow a clear separation of the fault conditions from the healthy baseline. Therefore an auto-regressive (AR) model has been developed on recorded AE signals to classify different fault conditions of mechanical seals and satisfactory results have been perceived.
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Hossein TOWSYFYAN. He received his Ph.D. degree in mechanical engineering from the University of Huddersfield, United Kingdom, in 2017. Now he is a postdoctoral researcher at the University of Southampton, in the Institute of Sound and Vibration Research (ISVR). His research interest includes tribology, condition monitoring, advanced nondestructive testing methods, artificial intelligence and digital signal and image processing.
Fengshou GU. He is an expert in the fields of vibro-acoustics analysis and machinery diagnosis, with over 20 years of research experience. Now he is a principal research fellow at the the University of Huddersfield, United Kingdom. His research interest includes machine dynamics, advanced signal processing, tribology dynamic responses, condition monitoring, measurement systems and sensor development, artificial intelligence and related fields.
Andrew D BALL. He took the Shell sponsored lectureship in maintenance engineering at the University of Manchester in 1991 and was promoted to professor of maintenance engineering in 1999. He was the head of School of the Manchester School of Engineering from 2003 to 2004 and in 2005 he became dean of Graduate Education. In late-2007 he moved to the University of Huddersfield as professor of diagnostic engineering and pro-vice-chancellor for research and enterprise. His research expertise is in the detection and diagnosis of faults in mechanical, electrical and electro-hydraulic machines, in data analysis and signal processing, and in measurement systems and sensor development. He is the author of over 300 technical and professional publications, and he has spent a large amount of time lecturing and consulting to industry in all parts of the world. He has to date graduated almost 100 research degrees, in the fields of mechanical, electrical and diagnostic engineering. He has acted as external examiner at over 30 overseas institutions, he holds visiting and honorary positions at 4 overseas universities, he sits on 3 large corporate scientific advisory boards, and he is also a registered expert witness in 3 countries.
Bo LIANG. He received his BEng and MPhil from Harbin Engineering University (China) in 1982 and 1985, respectively. He got his PhD degree from University of Manchester (UK) in 2000. Dr. Bo Liang currently is a reader at University of Huddersfield (UK). His research interests are vibration analysis and control, vehicle dynamics, artificial intelligence, condition monitoring and fault diagnosis.
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Towsyfyan, H., Gu, F., Ball, A.D. et al. Tribological behaviour diagnostic and fault detection of mechanical seals based on acoustic emission measurements. Friction 7, 572–586 (2019). https://doi.org/10.1007/s40544-018-0244-4
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DOI: https://doi.org/10.1007/s40544-018-0244-4