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Bearings are amongst the frequently encountered components to be found in rotating machinery. Though inexpensive, their failure can interrupt the production in a plant causing unscheduled downtime and production losses. So the bearing prognosis plays a significant role in reducing plant down time and enhanced operation safety, by estimating the Remaining(More)
This paper presents a comparative study between soft computing techniques Artificial Neural networks (ANN) and Self-Organizing Maps (SOM) using continuous wavelet transform (CWT) for fault diagnosis of rolling element bearings. Six different base wavelets three real valued and three complex valued are considered. Out of these six wavelets, the base wavelet(More)
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