Condition Based Reliability, Availability, Maintainability, and Safety (CB-RAMS) model: Improving RAMS predictions by combining condition-monitoring (CM) data with RAMS calculations

Dan M. Shalev, Joseph Tiran, David Katoshevski, and Jacob Bortman
Submission Type: 
Full Paper
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phmec_16_015.pdf1.27 MBJune 23, 2016 - 9:07am

The aim of this paper is to present a method for the enhancement of Reliability, Availability, Maintainability, and Safety (RAMS) assessments of complex systems by integrating condition monitoring (CM) local data. During the last decade CM methods have been extensively improved. The integration of CM and reliability is essential to enhance reliable prognostics and safety.
Implementing the method as a practical tool, updating RAMS probabilistic assessment related to operating and deteriorating complex systems is suggested, and explained by solving examples that emphasize the method’s advantages.
The method is based on conducting correlations between deterioration stages and Remaining Useful Life. Reliability is continuously updated according to pre-calculated Weibull parameters and concurrent CM findings. The updated assessments represent the real system deterioration along deterioration stages. The method is intended to support systems during design, operation, and maintenance by enabling improved decisions.
The conclusions are that RAMS prediction improvements are achieved by taking into account CM local historic and concurrent data. We named the method Condition Based-RAMS (CB-RAMS). Analyzing systems by CB-RAMS together with Monte-Carlo Simulation becomes a practical, efficient tool to utilize along a system’s life.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
015
Submission Keywords: 
Reliability Availability Maintainability and Safety (RAMS); Condition Monitoring (CM); Weibull analysis; Deterioration; Monte Carlo simulation (MCS); Life Cycle Cost (LCC)
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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