Maintenance Planning with Prognostics for Systems Located In Various Places

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 23, 2012
F. Camci M. Sevkli M. Karakas I. K. Jennions

Abstract

Predictive maintenance has been attracting researchers and industry in recent years, since maintenance and repair of assets is one of the most contributing factors of operating & support cost. Predictive maintenance proposes to maintain the assets only when necessary aiming to reduce the unnecessary repair and maintenance by monitoring the health of the assets. The expected time of the failure is estimated by analyzing the monitored signals and remaining useful life of the asset before failure is used to plan, get prepared and perform the maintenance. When one team is responsible for maintenance of systems that are located in various places, the travel time between these systems should also be incorporated in the maintenance planning. Off shore wind farms and railway switches are two examples of these systems. This paper presents formulation of the problem that incorporates travel times between systems and prognostics information obtained from each system.

How to Cite

Camci, F. ., Sevkli, M. ., Karakas , M. ., & K. Jennions, I. . (2012). Maintenance Planning with Prognostics for Systems Located In Various Places. Annual Conference of the PHM Society, 4(1). https://doi.org/10.36001/phmconf.2012.v4i1.2137
Abstract 289 | PDF Downloads 158

##plugins.themes.bootstrap3.article.details##

Keywords

prognostics, Maintenance planning, Maintenance Scheduling

References
Barbera F., Schneider H., & Kelle P., (1996) A condition based maintenance model with exponential failures and fixed inspection interval, The Journal of the Operational Research Society, 47(8), 1037–1045
Berenguer C., Grall A., Diulle L., & Roussignol M., (2003) Maintenance policy for a continuously monitored deteriorating system, Probability in the Engineering and Informational Sciences, 17(2), 235- 250
Camci F. (2009a), Comparison of Genetic and Binary PSO Algorithms on System Maintenance Scheduling Using Prognostics Information, Engineering Optimization, 41(2), 119-136
Camci F., (2009) System Maintenance Scheduling With Prognostics Information Using Genetic Algorithm, IEEE Transaction on Reliability, 58(3)
Camci F., Chinnam R. B., (2010) Process Monitoring, Diagnostics and Prognostics in Machining Processes, LAP Lambert Academic Publishing, 978-3838335667
Camci F., Chinnam R. B., (2010a), Health-State Estimation and Prognostics in Machining Processes", IEEE Transactions on Automation Science and Engineering, 7(3), 581-597
Heng A., Tan A. C.C., Mathew J., Montgomery N., Banjevic D., (2009) Intelligent Condition–based Prediction of Machinery Reliability, Mechanical Systems and Signal Processing, 23, 1600-1614
Jardine A.K.S., Lin D., and Banjevic D., (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, 20(7), 1483-1510
Lu H., Kolarik W. J., Lu S. S., (2001) Real-Time Performance Reliability Prediction, IEEE Transactions on Reliability, 50(4), 353-357
Marseguerra M., Zio E., & Podofillini L., (2002) Condition- based maintenance optimization by means of genetic algorithm and monte carlo simulation, Reliability Engineering and System Safety, 77(2), 151–166
Sloan T. W., & Shanthikumar J. G., (2002) Using in-line component condition and yield information for maintenance scheduling and dispatching in semiconductor wafer fabs, IIE Transactions, 34(2), 191–209
Sritavastava A. N., Das S., (2009) Detection and Prognostics on Low-Dimensional Systems, IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 39(1), 44-54
Xu Z., Ji Y., Zhou D., (2008) Real-time Reliability Prediction for a Dynamic System Based on the Hidden Degradation Process Identification, IEEE Transactions on Reliability, 57(2), 230-242
Xu Z., Ji Y., Zhou D., (2009) A New Real-Time Reliability Prediction Method for Dynamic Systems Based on Online Fault Prediction, IEEE Transactions on Reliability, 58(3), 523-537
Yam R. C. M., Tse P.W., Li L. & Tu P. (2001), Intelligent predictive decision support system for condition-based maintenance, International Journal of Advanced Manufacturing Technology, 17(5), 383–391
Section
Technical Research Papers