Abstract
Gears are one of the most common mechanisms for transmitting power and motion.Studies on gear teeth contacts have been considered as one of the most complicated applications. Depending on the application, the speed and load conditions of teeth may cause several types of failures on teeth surface which leads to non stationary operating conditions. This paper is attempt to analyze the effectiveness of the new time-frequency distributions called the Zhao-Atlas-Marks (ZAM) distribution to enhance non stationary signal analysis for fault diagnosis in spur gears. Also the performance of ZAM with other methods like short term fourier transform (STFT) and discrete wavelet transform (DWT) is discussed in this paper.
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Recommended by Editor Yeon June Kang
Krishnakumari Aharamuthu received her B.E degree in Mechanical Engineering from Madurai Kamaraj University, India. M.E degree in computer aided design from Anna University, Chennai, India. She is currently a Ph.D candidate in department of mechanical engineering, Anna University, Chennai, India. Her research topics are focused on vibration analysis, intelligent fault diagnostics of rotating machinery.
Elayaperumal Ayyasamy received his B.E degree in Mechanical Engineering from Anna University, Chennai, India, M.Tech degree in Machine Dynamics from Indian Institute of Technology Chennai, India and Ph.D in Composite Smart Structures from Anna University, Chennai, India. He is currently an Associate Professor in Department of Mechanical Engineering, Anna University, Chennai, India. His research areas include vibration analysis and control, composite materials and nano composites.
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Aharamuthu, K., Ayyasamy, E.P. Application of discrete wavelet transform and Zhao-Atlas-Marks transforms in non stationary gear fault diagnosis. J Mech Sci Technol 27, 641–647 (2013). https://doi.org/10.1007/s12206-013-0114-y
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DOI: https://doi.org/10.1007/s12206-013-0114-y