@article{(Open Science Index):https://publications.waset.org/pdf/9083,
	  title     = {Prediction of Coast Down Time for Mechanical Faults in Rotating Machinery Using Artificial Neural Networks},
	  author    = {G. R. Rameshkumar and  B. V. A Rao and  K. P. Ramachandran},
	  country	= {},
	  institution	= {},
	  abstract     = {Misalignment and unbalance are the major concerns
in rotating machinery. When the power supply to any rotating system
is cutoff, the system begins to lose the momentum gained during
sustained operation and finally comes to rest. The exact time period
from when the power is cutoff until the rotor comes to rest is called
Coast Down Time. The CDTs for different shaft cutoff speeds were
recorded at various misalignment and unbalance conditions. The
CDT reduction percentages were calculated for each fault and there
is a specific correlation between the CDT reduction percentage and
the severity of the fault. In this paper, radial basis network, a new
generation of artificial neural networks, has been successfully
incorporated for the prediction of CDT for misalignment and
unbalance conditions. Radial basis network has been found to be
successful in the prediction of CDT for mechanical faults in rotating
machinery.},
	    journal   = {International Journal of Mechanical and Mechatronics Engineering},
	  volume    = {5},
	  number    = {3},
	  year      = {2011},
	  pages     = {559 - 566},
	  ee        = {https://publications.waset.org/pdf/9083},
	  url   	= {https://publications.waset.org/vol/51},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 51, 2011},
	}