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

  • Shalev D
  • Tiran J
  • Katoshevski D
  • et al.
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Abstract

The goal of this paper is to present a practical method for the enhancement of systems Reliability, Availability, Maintainability, and Safety (RAMS) assessments. During the last decades Condition Monitoring (CM) methods have been improved and extensively implement. A method for integration the CM data with RAMS calculations is suggests. Implementing the method as a practical tool and updating RAMS prognostics assessment according to deterioration condition, is demonstrated by examples that emphasize the method’s contribution and advantages. The method is basedon conducting correlations between deterioration stages and Remaining Useful Life. Reliability is continuously updated according to pre-calculated Weibull parameters based on historic deterioration stages accumulated data and concurrent CM findings. The updated assessment represents the real system condition along its deterioration stages. The method enables improved decision making operation and maintenance action thus lower the Life Cycle Cost (LCC). The method named "Condition Based-RAMS" (CB-RAMS). Analyzing systems by CB-RAMS in conjunction with Monte-Carlo Simulation software tool, and CM data, is a practical and efficient method to eliminate surprising dangerous and costly events. The paper introduces the method and improvements that are achievable by implementing it. The contribution of the paper is the applicable detailed procedure to use any sort of CM data to enhance his RAMS predictions. The significance of this paper is that presented approach will enhance the importance of implementing CM on systems to lower LCC and increase safety.

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APA

Shalev, D. M., Tiran, J., Katoshevski, D., & Bortman, J. (2016). Condition Based Reliability, Availability, Maintainability, and Safety (CB-RAMS) model: Improving RAMS predictions by combining condition-monitoring (CM) data with RAMS calculations. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1590

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