Optimizations of Manufacturing Capabilities Through Systems Reliability Analysis and Redundancy Compliance with Operations Design and Safety Considerations
Science Journal of Circuits, Systems and Signal Processing
Volume 8, Issue 1, June 2019, Pages: 11-18
Received: Jun. 17, 2019; Accepted: Jul. 12, 2019; Published: Aug. 6, 2019
Views 52      Downloads 9
Casmir Onyeneke, Department of Mathematics and Computer Science, Hezekiah University, Umudi, Nigeria
Jovita Onyeaghala, Department Mechanical Engineering, Nnamdi Azikiwe University, Awka, Nigeria
Justin Okere, Department of Industrial Chemistry, Hezekiah University, Umudi, Nigeria
Victor Oguanobi, Department of Computer Science, Hezekiah University, Umudi, Nigeria
Article Tools
Follow on us
System and machine reliability is an important consideration that must be made when attempting the optimization of manufacturing capability; it has to be factored into the system design, layout and construction. Consideration has to be given to how reliability factors which influence the required optimization of the system, and the necessary level of its redundancy to comply with manufacturing process and safety considerations. These considerations must be made when commissioning and operating the system, with specific attention to associated maintenance requirements. These considerations and effects that redundancy engineering can have upon them are reviewed in this work indicating the latest ideas on their implementation and improvement. System availability is a consideration which is of paramount importance in the design of industrial structures. As the system becomes more complicated the cost of improving reliability also increases. Redundancy is the main avenue of increasing system availability. One of the main objectives for carrying out this research is to establish a system which optimize manufacturing capabilities through systems reliability analysis and redundancy compliance with operations design and safety considerations in a steel rolling mill. Repairable failures have been considered in most power system’s reliability analysis and that a modeling concept for unavailability due to ageing must be developed. A Normal or Weibull distribution is suggested as the means to estimate the failure probability density function due to the ageing process and a combined model is proposed including calculations for repairable and ageing failures. An example using seven generating units is used to verify the correctness of the constructed model. The results indicate that ageing failures have significant impact on the unavailability of components particularly in the case of older systems.
Optimization, Reliability, System Analysis, Regression Model, Cobble Formation, Cycles, Fineness, Rolling, Variation
To cite this article
Casmir Onyeneke, Jovita Onyeaghala, Justin Okere, Victor Oguanobi, Optimizations of Manufacturing Capabilities Through Systems Reliability Analysis and Redundancy Compliance with Operations Design and Safety Considerations, Science Journal of Circuits, Systems and Signal Processing. Vol. 8, No. 1, 2019, pp. 11-18. doi: 10.11648/j.cssp.20190801.12
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Lin Lingong. Xiangjie; LinQiang; Zhang Guangna (2014). Effectivity of Total Productive Maintenance (TPM) in Large size Organizations - a case study in Shandong. Applied Mechanics & Materials; 2014, Vol. 701/702.
Jiang, R.; Murthy, D. N. P. (2011). A study of Weibull shape parameter: Properties and significance. Reliability Engineering & System Safety. 96 (12): 1619–26. doi: 10.1016/j.ress.2011.09.003.
Myers, H. R. Montgomery, D. C and Anderson C. (2009) Response Surface Methodology, 3rd Edition, New York. John Wiley and Sons.
Kumar, Vijay; Patel, R. B.; Singh, Manpreet; Vaid, Rohit (2011). Reliability Analysis in Wireless Sensor Networks. International Journal of Engineering & Technology (0975-4024); Apr 2011, Vol. 3 Issue 2.
Casmir Onyeneke, Samson Olorunju, Udu Eta, Cyril Nwaonu (2018). Weibull Transformation Approach to Formulation of Reliability Model for Analysis of Filth Formation Using Zenith Grinding Machine. American Journal of Aerospace Engineering. Vol. 5, No. 1, 2018, pp. 30-38. doi: 10.11648/j.ajae.20180501.15.
Collett, David (2015). Modelling survival data in medical research (3rd ed.). Boca Raton: Chapman and Hall / CRC. ISBN 1439856788.
Eisinga, R.; Te Grotenhuis, M.; Pelzer, B. (2012). The reliability of a two-item scale: Pearson, Cronbach or Spearman-Brown?. International Journal of Public Health. 58 (4): 637–642. doi: 10.1007/s00038-012-0416-3.
Davidshofer, Kevin R. Murphy, Charles O. (2005). Psychological testing: principles and applications (6th ed.). Upper Saddle River, N. J.: Pearson/Prentice Hall. ISBN 0-13-189172-3.
Gulliksen, Harold (1987). Theory of mental tests. Hillsdale, N. J.: L. Erlbaum Associates. ISBN 978-0-8058-0024-1.
Ritter, N. (2010). Understanding a widely misunderstood statistic: Cronbach's alpha. Paper presented at Southwestern Educational Research Association (SERA) Conference 2010, New Orleans, LA (ED526237).
Chen, M. J., Chen, K. N. & Lin, C. W. (2004). Sequential Quadratic Programming for Development of a new probiotic diary with glucono-lactone. Journal of Food Science, 69 (22), 344-350.
Sharif, M. Nawaz; Islam, M. Nazrul (1980). "The Weibull distribution as a general model for forecasting technological change". Technological Forecasting and Social Change. 18 (3): 247–56. doi: 10.1016/0040-1625(80)90026-8.
Onyeneke Casmir Chidiebere, Effanga Okon Effanga.(2016). Application of Reduced Second Order Response Surface Model of Convex Optimization in Paper Producing Industries. International Journal of Theoretical and Applied Mathematics. Vol. 2, No. 1, 2016, pp. 13-23. doi: 10.11648/j.ijtam.20160201.13.
Sarlea, Nicoleta; Iudean, D.; Munteanu jr., R. (2013). Reliability Indicators Analysis for Sliding System of Industrial Knitting Machines. Acta Electrotehnica; 2013, Vol. 54 Issue 3/4, p204.
Coolen, F. P. A.; Coolen-Schrijner, P.; Rahrouh, M. (2006). Bayesian Reliability Demonstration with Multiple Independent tasks. IMA Journal of Management Mathematics; Apr 2006, Vol. 17 Issue 2.
Rahmat, M. K.; Jovanovic, S. (2016). Reliability Modelling of Uninterruptible Power Supply Systems Using Reliability Block Diagram Method. International Review of Electrical Engineering; Oct 2006, Vol. 1 Issue 4.
Jain, M.; Preeti (2013). Performance Analysis of a Repairable Robot Safety System with Standby, Imperfect Coverage and Reboot Delay. International Journal of Engineering (1025-2495); Sep 2013, Vol. 26 Issue 9.
Ding Chen; Zhigeng Fang; Lifeng Wu; Xingzi Zhu (2013). Study on the Prediction of Complex Equipment El MTBF DGMW (p/q)(1,1) Model Based on the Small Sample. Journal of Grey System; 2013, Vol. 25 Issue 3.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186