Fuzzy RAM Analysis of the Screening Unit in a Paper Industry by Utilizing Uncertain Data. (14th November 2012)

• Record Type: Journal Article
• Authors: Garg, Harish; Rani, Monica; Sharma, S. P.
• Other Names: Dohi Tadashi Academic Editor.
• Is Part Of: International journal of quality, statistics, and reliability. Volume 2012(2012)
• Publication Date: 2012-11-14
• Subjects: Quality control -- Periodicals; Quality control; Electronic journals; Journals - full-text; Periodicals; 620.0045
• DOI: 10.1155/2012/203842
• Languages: English
• ISSNs: 1687-7144

Abstract

Abstract : Reliability, availability, and maintainability (RAM) analysis has helped to identify the critical and sensitive subsystems in the production systems that have a major effect on system performance. But the collected or available data, reflecting the system failure and repair patterns, are vague, uncertain, and imprecise due to various practical constraints. Under these circumstances it is difficult, if not possible, to analyze the system performance up to desired degree of accuracy. For this, Artificial Bee Colony based Lambda-Tau (ABCBLT) technique has been used for computing the RAM parameters by utilizing uncertain data up to a desired degree of accuracy. Results obtained are compared with the existing Fuzzy Lambda-Tau results and we conclude that proposed results have a less range of uncertainties. Also ranking the subcomponents for improving the performance of the system has been done using RAM-Index. The approach has been illustrated through analyzing the performance of the screening unit of a paper industry.