Estimation of the remaining useful life of equipment with gradual deterioration with condition-based maintenance policy with the presence of two failure accelerators

Document Type : Original Article

Authors

1 Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 Faculty of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran

Abstract
Equipment is usually damaged by a random pattern based on a gradual deterioration process. In these cases, the level of deterioration gradually increases, and when its value exceeds the predefined decline threshold, it is considered disabled. In addition, environmental disturbance factors such as temperature, humidity, pressure, etc. may experience uncontrollable changes and alter or accelerate failure patterns. Since estimating the remaining equipment life is very important in the effectiveness of forecast maintenance planning and this estimate should be based on identifying accelerated failure patterns, the present study is the first to estimate the remaining equipment life in the presence of effects. The accelerator is focused on two correlated disturbance factors and in it to monitor the environmental factors affecting the life of the equipment from control charts and how to meet the average residual life of the equipment in different conditions under control, out of control due to the first disturbance factor, Being out of control due to the second factor and being out of control due to both factors are presented. A numerical example is also provided to illustrate the details of the calculations.

Keywords


[1]Wang H. (2002). A survey of maintenance policies of deteriorating systems, European Journal of Operational Research,139:469–489.
[2] Jardine, A.K.S., Lin, D., Banjevic, D., 2006. A review on machinery diagnostics andprognostics implementing condition-based maintenance. Mechanical Systems
[3] Elwany, A.H., Gebraeel, N.Z., 2008. Sensor-driven prognostic models for equipment replacement and spare parts inventory. IIE Transactions 40, 629–639.
[4] Wang, W., Hussian, B., 2009. Plant residual time modelling based on observed variables in oil samples. Journal of the Operational Research Society 60, 789–769
[5] Kim, K.O., Kuo, W., 2009. Optimal burn-in for maximizing reliability of repairable non-series systems. European Journal of Operational Research 193, 140–151.
[6] Elsayed, A.(2012). Reliability Engineering. John Wiley & Sons
[7] Xiao-sheng, s., Wenbin, w., Chang-Hua, H., & Dong-Hua, Z. (2011). Remaining useful life estimation- A review on the statistical data driven approaches, European Journal of Operational Research, 213, 1-14.
[8] E Deloux., B Castanier., and C Be´renguer. (2008). Maintenance policy for a deteriorating system evolving in a stressful environment, 222,  613-622
[9] Dodson, B. (2006). Accelerated testing-A Practitioner’s Guide to Accelerated and Reliability Testing. United States of America: SAE international.
[10] Pecht MG. (208). Prognostics and health management of electronics, Hoboken: John Wiley & Sons Inc, p. 1–9.
[11] Si XS., Wang W., Hu CH, Zhou DH.(2011). Remaining useful life estimation–a review on the statistical data-driven approaches. Eur Journal of the Operational Research, 213(1), 1–14.
 [12] Hu, C., Zhou, Z., Zhang, J., & Si, X. (2015). A survey on life prediction of equipment. Chinese Journal of Aeronautics, 28(1) ,25-33
[13] Cassady, CR., Bowden, RO., Liew, L., & Pohl, EA.(2000). Combining preventive maintenance and statistical process control: a preliminary investigation. IIE Trans, 32, 471–8.
[14] Ben-Daya, M., & Rahim, MA. (2000). Effect of maintenance on the economic design of x-control chart. European Journal of Operational Research, 120, 131–43.
[15] Linderman, K., McKone-Sweet, KE., & Anderson, JC. (2005). An integrated systems approach to process control and maintenance. European Journal of Operational Research, 164, 324–40.
[16] Deloux, E., B.Castanier, & C.Berenguer. (2009). Predictive maintenance policy for a gradually deteriorating system subject to stress. Reliability Engineering and System Safety, 94, 418-431.
[17] Mehr afrooz, Z., & Noorossana, R. (2011). An integrated model based on statistical process control and maintenance. Computers & Iindustrial engieering, 61, 1245-1255.