Age replacement with Markovian opportunity process

Author(s):  
Junjun Zheng ◽  
Hiroyuki Okamura ◽  
Tadashi Dohi
Keyword(s):  
2021 ◽  
Vol 58 (2) ◽  
pp. 289-313
Author(s):  
Ruhul Ali Khan ◽  
Dhrubasish Bhattacharyya ◽  
Murari Mitra

AbstractThe performance and effectiveness of an age replacement policy can be assessed by its mean time to failure (MTTF) function. We develop shock model theory in different scenarios for classes of life distributions based on the MTTF function where the probabilities $\bar{P}_k$ of surviving the first k shocks are assumed to have discrete DMTTF, IMTTF and IDMTTF properties. The cumulative damage model of A-Hameed and Proschan [1] is studied in this context and analogous results are established. Weak convergence and moment convergence issues within the IDMTTF class of life distributions are explored. The preservation of the IDMTTF property under some basic reliability operations is also investigated. Finally we show that the intersection of IDMRL and IDMTTF classes contains the BFR family and establish results outlining the positions of various non-monotonic ageing classes in the hierarchy.


2001 ◽  
Vol 38 (02) ◽  
pp. 386-406 ◽  
Author(s):  
Bernd Heidergott

We consider a multicomponent maintenance system controlled by an age replacement policy: when one of the components fails, it is immediately replaced; all components older than a threshold age θ are preventively replaced. Costs are associated with each maintenance action, such as replacement after failure or preventive replacement. We derive a weak derivative estimator for the derivative of the cost performance with respect to θ. The technique is quite general and can be applied to many other threshold optimization problems in maintenance. The estimator is easy to implement and considerably increases the efficiency of a Robbins-Monro type of stochastic approximation algorithm. The paper is self-contained in the sense that it includes a proof of the correctness of the weak derivative estimation algorithm.


2018 ◽  
Vol 154 ◽  
pp. 01056
Author(s):  
Fifi Herni Mustofa ◽  
Ria Ferdian Utomo ◽  
Kusmaningrum Soemadi

PT Lucas Djaja is a company engaged in the pharmaceutical industry which produce sterile drugs and non-sterile. Filling machine has a high failure rate and expensive corrective maintenance cost. PT Lucas Djaja has a policy to perform engine maintenance by way of corrective maintenance. The study focused on the critical components, namely bearing R2, bearing 625 and bearing 626. When the replacement of the failure done by the company is currently using the formula mean time to failure with the result of bearing R2 at point 165 days, bearing 625 at a point 205 days, and bearing 626 at a point 182 days. Solutions generated by using age replacement method with minimization of total maintenance cost given on the bearing R2 at a point 60 days, bearing 625 at the point of 80 days and bearing 626 at a point 40 days.


Author(s):  
Andriani Andriani ◽  
Ikhsan Romli

In an industry, the maintenance department plays a very important role in ensuring the smooth production process. The method of machine maintenance with preventive maintenance is a strategy that can be used to repair existing machines. This is related to proper and regular maintenance can improve engine performance and reduce the level of engine damage which will increase the continuity of production activities. In the die casting division of PT Astra Honda Motor in the observation on the die casting machine 07 there were 45 times damage to the ladle component and 11 times the damage to the auto spray component. These two components are critical components of the 07 die casting machine. After testing the compatibility index and the good compatibility of the damage time data and repair time data to obtain distribution data distribution patterns, obtain the tablespoon component MTTF assessment results of 107,833 hours and auto spray components amounting to 314,226 hours. Whereas the MTTR value of the spoon component is 0.385 hours and the auto spray component is 0.766 hours. The next step is to look for critical component replacement time intervals with the age replacement model, to further review whether it is related to increased reliability, decrease in total downtime, and cost savings before preventive maintenance is carried out and after preventive maintenance is carried out.


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