@inproceedings{924631c9b0cf4a6a8325ae0965dfabfa,
title = "Comparative study of reliability prediction of compressor system by ANN and TTF time series techniques",
abstract = "A comparative study of reliability prediction of Compressor system by means of Artificial Neural Network (ANN) and Time to failure (TTF) analysis is presented. The data for the study was taken from a specific example namely; CO2 compressor at the Utilities Department of a beverage plant, preferred to be addressed as X plant for the sake of confidentiality. The trained neural network predicted the reliability of the compressor very well, given the very low values of mean square error (1.02564 ×10-3 ) and high value of the regression (0.998742). The result was compared to what obtained in reliability prediction by time-to-failure time series. The later equally gave a considerably high value for the coefficient of determination of the linear regression line between the predicted values and the actual values (0.9824). Yet artificial neural network result gave a better value. The result shows that use of artificial neural network is a good means of predicting reliability of deteriorating systems.",
keywords = "Artificial neural network, Deteriorating systems, Reliability prediction, Time to failure analysis",
author = "Ozor, {P. A.} and C. Mbohwa",
note = "Publisher Copyright: {\textcopyright} 2018 Newswood Limited. . All rights reserved.; 2018 World Congress on Engineering, WCE 2018 ; Conference date: 04-07-2018 Through 06-07-2018",
year = "2018",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
pages = "494--498",
editor = "Hukins, {David WL} and Len Gelman and Andrew Hunter and Ao, {S. I.} and Korsunsky, {A. M.}",
booktitle = "Proceedings of the World Congress on Engineering 2018, WCE 2018",
}