Estimation and Prediction the Probability of Failure with Application
Main Article Content
Abstract
One of the reasons that reduces the production of factories is the failure of these machines that are used for production. One of the reasons for these failures is due to failures of the components of the machines, which is due to all the stress that these components face while working. That is why trying to make the life of these machines longer we need to know the (Probability of Failure) and (Cycle of Failure) for the components of these machines.
The Researcher, to achieve and obtain the main goal of this study, after very hard work started to collect data on five components (time of failure and repair) of these components in a steel company, the company that is in Erbil City Kurdistan Region of Iraq. The Researcher reached a conclusion finding the probability of failure and cycle to failure for each component in these machines, also estimating the value of cycle to failure for nine specific values of the probability of failure. We also designed a model for predicting the probability of failure for each machine including all the components together through using Factor Analysis and Multiple Regression Analysis.
STAT Graphics, SPSS and Microsoft Excel have been used for all the calculations, graphics, and analysis.
Downloads
Article Details
References
Ali, I. M. & Muhammad, Z. A., (2019), A statistical study on the most important factors that lead to the increase of car accidents in Erbil governorate using the analysis and cluster analysis. J Zanco Journal of Humanity Sciences, 23, 14-29.
Bertche, B., (2008), Reliability in Automotive and Mechanical Engineering. © Springer-Verlag Berlin Heidelberg 2008.
Bernstein, J. B., (2014), Reliability Prediction from Burn-In Data Fit to Reliability Models. British Library Cataloguing-in-Publication Data.
Chatterjee, S. & Simonoff, J.S., (2013), Handbook of Regression Analysis. Published by John Wiley & Sons, Inc., Hoboken, New Jersey published simultaneously in Canada.
Callister, JR. WD & Rethwisch, D.G, (2018), Materials Science and Engineering. An introduction. John Wiley & Sons, Inc., 111 River Street, Hoboken,NJ 07030-5774.
Dowling, NE., (2013), Mechanical Behavior of Materials.4th edition. Published by Pearson Education. The United States of America.
Dhillon, B. S., (1999), Design Reliability (Fundamentals and Application) CRC Press LLC.
Hadar, A. & Mahadevan, S., (2000), Probability, Reliability, and Statistical Methods in Engineering Design. John Wiley & Sons, Inc. USA.
Ibrahim, Nazir Abbas & Mahdi, Degla Ibrahim, (2011), Econometrics and Its Applications, Al-Jazeera Printing and Publishing Office, Iraq-Baghdad.
Joshi, Y. P, Dhaug, N and Pandey, B. B., (2004), Probability Analysis of Fatigue of Paving Concrete. Institution of Engineering (India) Journal, IE (I), Vol, 85, PP163-168.
Kumar, U., Crocker, J., Knezevic, J. & El-Haram, M., (2000), Reliability,Maintenance and Logistic Support: a life cycle approach. kluwer Academic.
Lee, S. W & Lee, H., (2008), Reliability prediction system based on the failure rate model for electronic components. Journal of mechanical science and technology, 22 (2008) 957-964.
Meeker, W., Q., & Escobar, L. A., (1998), Statistical Methods for Reliability data. John Wiley & Sons, Canada. Library of congress cataloging-in-publication data.
Ohring, M., (1998), Reliability and Failure of Electronic Materials and Devices. United Kingdom Edition published by academic press limited an imprint of Elsevier.
Qua, H. Q; Tan, C, S; Wong, K; Ho, J; Wang, X; Yap, E; Ooi, I & Wong, Y., (2015), Applied Engineering Failure Analysis; Theory and Practice. Taylor & Francis Group, LLC.
Ryan, T. P., (2007), Modern Engineering Statistics. John Wiley & Sons, Inc., Hoboken, New Jersey, Canada.
Todinov, M.T., (2005), Reliability and Risk Models: Setting Reliability Requirements. John Wiley & Sons Ltd, ISBN 0-470-09488-5 (HB).
Yang, G., (2007), Life cycle reliability engineering. John Wiley & Sons, Inc.