A bivariate replacement policy for system in an increasing failure rate model with double repair cost limits

2017 ◽  
Vol 11 (3/4) ◽  
pp. 220
Author(s):  
Min Tsai Lai
Author(s):  
MIN-TSAI LAI

In this paper, a periodical replacement model combining the concept of cumulative repair cost limit for a two-unit system with failure rate interaction is presented. In this model, whenever unit 1 fails, it causes a certain amount of damage to unit 2 by increasing the failure rate of unit 2 of a certain degree. Unit 2 failure whenever occurs causes unit 1 into failure at the same time and then the total failure of the system occurs. Without failure rate interaction between units, the failure rates of two units also increase with age. When unit 1 fails, the necessary repair cost is estimated and is added to the accumulated repair cost. If the accumulated repair cost is less than a pre-determined limit L, unit 1 is corrected by minimal repair. Otherwise, the system is preventively replaced by a new one. Under periodical replacement policy and cumulative repair cost limit, the long-run expected cost per unit time is derived by introducing relative costs as a criterion of optimality. The optimal period T* which minimizes that cost is discussed. A numerical example is given to illustrate the method.


1995 ◽  
Vol 35 (1) ◽  
pp. 109-111
Author(s):  
S.A. Siddiqui ◽  
Deoki Nandan ◽  
Sanjay Gupta ◽  
Manish Subharwal

2003 ◽  
Vol 17 (1) ◽  
pp. 153-153
Author(s):  
James Lynch

The above-mentioned article by James Lynch was published in Probability in the Engineering and Informational Sciences (1999), 13: 33–36.It has recently been brought to the author's attention that the results in that paper were preceded and superseded by the results in Thomas H. Savits' paper, “A multivariate IFR class,” which appeared in the Journal of Applied Probability (1985), 22: 197–204. This acknowledgment is to correct this contretemps.


2013 ◽  
Vol 9 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Edward K. Cheng

AbstractWhether the nature of the risks associated with climbing high-altitude (8000 m) peaks is in some sense “controllable” is a longstanding debate in the mountaineering community. Well-known mountaineers David Roberts and Ed Viesturs explore this issue in their recent memoirs. Roberts views the primary risks as “objective” or uncontrollable, whereas Viesturs maintains that experience and attention to safety can make a significant difference. This study sheds light on the Roberts-Viesturs debate using a comprehensive dataset of climbing on Nepalese Himalayan peaks. To test whether the data is consistent with a constant failure rate model (Roberts) or a decreasing failure rate model (Viesturs), it draws on Total Time on Test (TTT) plots from the reliability engineering literature and applies graphical inference techniques to them.


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