Abstract
Feature-based classification techniques consist of data acquisition, preprocessing, feature representation, feature calculation, feature selection, and classifiers. They are useful for online, real-time condition monitoring and fault diagnosis / features, which are now available with the development of information technologies and various measurement techniques. In this paper, an intelligent feature-based fault diagnosis is suggested, developed, and compared with vibration signals and thermal images. Fault diagnosis is performed using thermal imaging along with support vector machine (SVM) classification to simulate machinery faults, resulting in an accuracy level comparable to vibration signals. The observed results show that fault diagnosis using thermal images for rotating machines can be applied to industrial areas as a novel intelligent fault diagnostic method with plausible accuracy. It can be also proposed as a unique non-contact method to analyze rotating systems in mass production lines within a short time.
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References
Y. Lei, Z. He and Y. Zi, Application of an intelligent classification method to mechanical fault diagnosis, Expert System with Application, 36 (2009) 9941.
J. H Williams, A. Davies and P. R. Drake, Condition-based Maintenance and Machine Diagnostics, Chapman & Hall, London (1994).
A. Barber, Handbook of noise and vibration control, 6th Ed., Elsevier Advanced Technology Publications, UK (1992).
B. S. Yang, D. S. Lim and J. L. An, Vibration diagnostic system of rotating machinery using artificial neural network and wavelet transform, Proc. 13 th International Congress on COMADEM, Houston, USA (2000) 12–20.
A. K. S. Jardine, D. Lin and D. Banjevic, A review on machinery diagnostics and prognostics implementing conditionbased maintenance, Mechanical Systems and Signal Processing, 20 (2006) 1483.
A. Widodo and B. S. Yang, Support vector machine in condition monitoring and fault diagnosis, Mechanical System and Signal Processing, 21 (2007) 2560.
B. Samanta, Gear fault detection using artificial neural networks and support vector machines with genetic algorithms, Mechanical Systems and Signal Processing, 18 (3) (2004) 625.
S. B. Glavatskih, O. Uusitalo and D. Spohn, Simultaneous monitoring of oil film thickness and temperature in fluid film bearings, Tribology International, 34 (12) (2001) 853.
S. B. Glavatskih, A method of temperature monitoring in fluid film bearing, Tribology International, 37 (2) (2004) 143.
P. De Choudhury and E. W. Barth, A comparison of film temperature and oil discharge temperature for a tilting-pad jounal bearing, J. lubrication Technology, 103 (1) (1981) 115.
A. Mazioud, L. Ibos, A. Khlaif and J. F. Durastant, Detection of rolling bearing degradation using infrared thermography, Proc. of 9 th International Conference on Quantitative Infrared Thermography, Krakow, Poland, July (2008).
A. M. Younus, A. Widodo and B. S. Yang, Evaluation of thermography image data for machine fault diagnosis, Nondestructive Testing and Evaluation, 25 (2009) 231.
A. M. Younus and B. S. Yang, Intelligent fault diagnosis of rotating machinery using infrared thermal image, Expert System with Applications, 39 (2011) 2082.
V. N. Vapnik, The nature of statistical learning theory, Springer, New York (1995).
C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, 20 (1995) 273.
C. W. Hsu and C. J. Lin, A comparison of methods for multi-class support vector machines, IEEE Trans. Neural Networks, 13 (2002) 415.
Colour Theory: Understanding and modelling colour, (http://www.jiscdigitalmedia.ac.uk/stillimages/advice/colour-theory-understanding-and-modelling-colour/).
CIEL*a*b* Color Scale, Hunterlab application Notes, 8 7 (2008).
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Recommended by Editor Yeon June Kang
Gang-Min Lim received his B.S. and M.E. degrees from the Department of Mechanical Engineering, Inha University, Incheon, Korea, in 1993 and 1995 respectively, and his Ph.D. from the interdisciplinary program of Acoustics and Vibration Engineering, Pukyong National University in 2013. Currently, he is CEO of ATG and a specialist in vibration and thermography analysis. His research interests are contact and noncontact type analytic technology as well as health monitoring of mechanical systems.
Dong-Myung Bae received his B.S., M.E. and Ph.D. degrees from the Department of Naval Architecture, Busan National University, Busan, Korea, in 1979, 1982, and 1985, respectively. He is currently a professor in the Department of Naval Architecture and Marine Systems Engineering, Pukyong National University, Busan, Korea. He was an associate professor in 1997. In 2003, he was manager of the Institute of Acoustics and Vibration, and from 2010 to 2012, was the dean of Academic Affairs, Pukyong National University. Moreover, he served as the Director of the Society of Naval Architecture of Korea from 2009 to 2010. His main research interests are computational structure, vibration, noise analysis, and dynamic response of ship structure.
Joo-Hyung Kim received his B.S. and M.E. degrees from the Department of Mechanical Engineering, Inha University, Incheon, Korea, in 1993 and 1995 respectively, and his Ph.D. from the Department of Microelectronics and Information Technology, KTH Royal Institute of Technology, Stockholm, Sweden, in 2005. From 1995 to 2002, he was a Senior Research Engineer in DAEWOO and SAMSUNG SDI Central Research Centers, Korea. From 2006 to 2008, he was a Senior Scientist with the Fraunhofer Institute, Germany, for novel material research in microelectronics. He was an assistant professor in the Department of Electronic Engineering, Chosun University. Currently, he is an associate professor in the Department of Mechanical Engineering in Inha University. His research interests are micro-electro-mechanical system (MEMS), infrared detector/thermal imaging technology, semiconductors, and advanced sensor devices.
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Lim, GM., Bae, DM. & Kim, JH. Fault diagnosis of rotating machine by thermography method on support vector machine. J Mech Sci Technol 28, 2947–2952 (2014). https://doi.org/10.1007/s12206-014-0701-6
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DOI: https://doi.org/10.1007/s12206-014-0701-6