Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195617 
Year of Publication: 
2018
Citation: 
[Journal:] Journal of Industrial Engineering International [ISSN:] 2251-712X [Volume:] 14 [Issue:] 3 [Publisher:] Springer [Place:] Heidelberg [Year:] 2018 [Pages:] 443-453
Publisher: 
Springer, Heidelberg
Abstract: 
In this manuscript, the reliability of a robotic system has been analyzed using the available data (containing vagueness, uncertainty, etc). Quantification of involved uncertainties is done through data fuzzification using triangular fuzzy numbers with known spreads as suggested by system experts. With fuzzified data, if the existing fuzzy lambda-tau (FLT) technique is employed, then the computed reliability parameters have wide range of predictions. Therefore, decision-maker cannot suggest any specific and influential managerial strategy to prevent unexpected failures and consequently to improve complex system performance. To overcome this problem, the present study utilizes a hybridized technique. With this technique, fuzzy set theory is utilized to quantify uncertainties, fault tree is utilized for the system modeling, lambda-tau method is utilized to formulate mathematical expressions for failure/repair rates of the system, and genetic algorithm is utilized to solve established nonlinear programming problem. Different reliability parameters of a robotic system are computed and the results are compared with the existing technique. The components of the robotic system follow exponential distribution, i.e., constant. Sensitivity analysis is also performed and impact on system mean time between failures (MTBF) is addressed by varying other reliability parameters. Based on analysis some influential suggestions are given to improve the system performance.
Subjects: 
Reliability analysis
Robotic system
Nonlinear programming
Fuzzy lambda-tau technique
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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