Residual Lifetime Prediction for Multi-State System Using Control Charts to Monitor Affecting Noise Factor on Deterioration Process

Gol-Ahmadi, Neda; Raissi, Sadigh

doi:10.22105/jarie.2018.111526.1027

Document Type : Research Paper

Authors

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

https://doi.org/10.22105/jarie.2018.111526.1027

Abstract

In this research, multi-state complex systems are analyzed in order to measure reliability and predict residual of systems’ lifetime under the effect of an out of control noise factor. Hence, the analytic method helps us to estimate multi-state system reliability, and then means residual lifetime that is calculated under normal conditions. Finally, the calculation is updated for out of control noisy condition using the accelerated method. To reveal the applied results, the proposed policy is implemented in a case study in a molding machine on SNJ Co. at Isfahan.

Keywords

Main Subjects

Risk management

References

[1] Das, S., & Nanda, A. K. (2013). Some stochastic orders of dynamic additive mean residual life model. Journal of statistical planning and inference, 143(2), 400-407.

[2] Deloux, E., Castanier, B., Yeung, T., & Bérenguer, C. (2006, November). A Predictive Maintenance Policy Combining Statistical Process Control and Condition-Based Approaches. 31 ESReDA seminar on ageing (pp. 210-220).

[3] Dieulle, L., Bérenguer, C., Grall, A., & Roussignol, M. (2003). Sequential condition-based maintenance scheduling for a deteriorating system. European journal of operational research, 150(2), 451-461.

[4] Eryilmaz, S. (2017). Computing optimal replacement time and mean residual life in reliability shock models. Computers & industrial engineering, 103, 40-45.

[5] Huang, J., & Zuo, M. J. (2004). Dominant multi-state systems. IEEE transactions on reliability, 53(3), 362-368.

[6] Huang, W., Zhou, J., & Ning, J. (2016). A Condition Based Maintenance for System Subject to Competing Failure due to Degradation and Shock. International journal of applied mathematics, 46(2).

[7] Kurniati, N., Yeh, R. H., & Lin, J. J. (2015). Quality inspection and maintenance: the framework of interaction. Procedia manufacturing, 4, 244-251.

[8] Li, W., & Pham, H. (2005). Reliability modeling of multi-state degraded systems with multi-competing failures and random shocks. IEEE transactions on reliability, 54(2), 297-303.

[9] Yingkui, G., & Jing, L. (2012). Multi-state system reliability: a new and systematic review. Procedia engineering, 29, 531-536.

[10] Zhao, M., Jiang, H., & Liu, X. (2013). A note on estimation of the mean residual life function with left-truncated and right-censored data. Statistics & probability letters, 83(10), 2332-2336.

[11] Madsen, H. O. (1985, May). Random fatigue crack growth and inspection. Proceeding of 4th international conference on structural safety and reliability (ICOSSAR’85). Kobe, Japan.

[12] Wu, S., & Wang, W. (2011). Optimal inspection policy for three-state systems monitored by control charts. Applied mathematics and computation, 217(23), 9810-9819.

Journal of Applied Research on Industrial Engineering

Residual Lifetime Prediction for Multi-State System Using Control Charts to Monitor Affecting Noise Factor on Deterioration Process

References

References

Volume 5, Issue 1 - Serial Number 1
June 2018
Pages 27-38

Residual Lifetime Prediction for Multi-State System Using Control Charts to Monitor Affecting Noise Factor on Deterioration Process

References

References

Volume 5, Issue 1 - Serial Number 1June 2018Pages 27-38

Volume 5, Issue 1 - Serial Number 1
June 2018
Pages 27-38