Component Reliability, Replacement, and Cost Analysis with Incomplete Failure Data

Author(s):  
Nicholas A. J. Hastings
2018 ◽  
Vol 56 (12) ◽  
pp. 2067-2071 ◽  
Author(s):  
Alekh Verma ◽  
Aastha Narula ◽  
Akshi Katyal ◽  
Shakti Kumar Yadav ◽  
Priyanka Anand ◽  
...  

Abstract Background Life cycle prediction measures, that provide information on the probability of failure of equipments, have been applied in electronic and mechanical engineering and for predicting the strength of dental implants. However, the same has not been utilized as yet in medical equipment such as hematology analyzers. Methods Failure data of five automated hematology analyzers (3-part differential) was collected over 14 consecutive months and a Weibull probability plot was made. The scale and shape parameters of this plot were used to predict failure probability distribution. This was then combined with various costs involved in remedial maintenance to get a cost analysis. Results The analyzers in their “useful life” period were found to suffer fewer actual and predicted failures compared to those in the “wear out” phase. Cost analysis showed a considerably higher per month cost of remedial maintenance of analyzers compared to the price of a comprehensive maintenance contract. Conclusions Our study demonstrates, for the first time, that Weibull distribution can be applied well to hematology analyzers for modeling of failure data and the resultant information is helpful in the cost analysis of maintenance to allow for prudent and informed decision making with regards to the mode of maintenance of analyzers.


2019 ◽  
Vol 21 (1) ◽  
pp. 15
Author(s):  
Entin Hartini ◽  
Hery Adrial ◽  
Santosa Pujiarta

Reliability and maintenance play an important role in ensuring successful operation of a system. Reliability analysis is often used to determine the probability whether or not a system is functioning. However, limited available data and information are causing uncertainties and inaccuracies on component parameters. The purpose of this study is to conduct component/system reliability analysis using Monte Carlo simulation-based method. This method enables us to estimate the reliability of components/systems including parameter uncertainty and imprecision. It is also useful to predict and evaluate maintenance decisions related to reliability. Monte Carlo method employs random number generation based on the probability of the distribution of processed data, of which then validated with real available data to ensure the simulation condition is relatively similar to real-life condition. The data used in this research is failure data on RSG-GAS components/systems for core configuration number of 81 to 95, accumulated from year 2013 to 2018. The results show that reliability values of components JE01/AP01-02 on TTF 233.619 is 0.579 while for components KBE01/AP-01-02 in TTF 185.38 is 0.368.The component reliability value is 60%, which implies that maintenance may be performed after 225 days and 100 days for componentsJE01/AP01-02 and KBE01/AP01-02, respectively.Keywords: Reliability, Monte Carlo, Component damage, RSG-GAS


2006 ◽  
Vol 5 (1) ◽  
pp. 134-134
Author(s):  
L SCELSI ◽  
L TAVAZZI ◽  
A MAGGIONI ◽  
D LUCCI ◽  
G CACCIATORE ◽  
...  

1989 ◽  
Vol 7 (1) ◽  
pp. 27-41 ◽  
Author(s):  
Norman Keith Womer
Keyword(s):  

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