TY - JOUR
T1 - A risk-based availability estimation using Markov method
AU - Ahmed, Qadeer
AU - Khan, Faisal I.
AU - Raza, Syed A.
PY - 2014/2
Y1 - 2014/2
N2 - Purpose: Asset intensive process industries are under immense pressure to achieve promised return on investments and production targets. This can be accomplished by ensuring the highest level of availability, reliability and utilization of the critical equipment in processing facilities. In order to achieve designed availability, asset characterization and maintainability play a vital role. The most appropriate and effective way to characterize the assets in a processing facility is based on risk and consequence of failure. The paper aims to discuss these issues. Design/methodology/approach: In this research, a risk-based stochastic modeling approach using a Markov decision process is investigated to assess a processing unit's availability, which is referred as the risk-based availability Markov model (RBAMM). RBAMM will not only provide a realistic and effective way to identify critical assets in a plant but also a method to estimate availability for efficient planning purposes and resource optimization. Findings: A unique risk matrix and methodology is proposed to determine the critical equipment with direct impact on the availability, reliability and safety of the process. A functional block diagram is then developed using critical equipment to perform efficient modeling. A Markov process is utilized to establish state diagrams and create steady-state equations to calculate the availability of the process. RBAMM is applied to natural gas absorption process to validate the proposed methodology. In the conclusion, other benefits and limitations of the proposed methodology are discussed. Originality/value: A new risk-based methodology integrated with Markov model application of the methodology is demonstrated using a real-life application.
AB - Purpose: Asset intensive process industries are under immense pressure to achieve promised return on investments and production targets. This can be accomplished by ensuring the highest level of availability, reliability and utilization of the critical equipment in processing facilities. In order to achieve designed availability, asset characterization and maintainability play a vital role. The most appropriate and effective way to characterize the assets in a processing facility is based on risk and consequence of failure. The paper aims to discuss these issues. Design/methodology/approach: In this research, a risk-based stochastic modeling approach using a Markov decision process is investigated to assess a processing unit's availability, which is referred as the risk-based availability Markov model (RBAMM). RBAMM will not only provide a realistic and effective way to identify critical assets in a plant but also a method to estimate availability for efficient planning purposes and resource optimization. Findings: A unique risk matrix and methodology is proposed to determine the critical equipment with direct impact on the availability, reliability and safety of the process. A functional block diagram is then developed using critical equipment to perform efficient modeling. A Markov process is utilized to establish state diagrams and create steady-state equations to calculate the availability of the process. RBAMM is applied to natural gas absorption process to validate the proposed methodology. In the conclusion, other benefits and limitations of the proposed methodology are discussed. Originality/value: A new risk-based methodology integrated with Markov model application of the methodology is demonstrated using a real-life application.
KW - Availability
KW - Maintainability
KW - Markov decision process
KW - Risk assessment
KW - Risk assessment matrix
KW - Risk-based availability Markov model
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U2 - 10.1108/IJQRM-04-2012-0056
DO - 10.1108/IJQRM-04-2012-0056
M3 - Article
AN - SCOPUS:84893479488
SN - 0265-671X
VL - 31
SP - 106
EP - 128
JO - International Journal of Quality and Reliability Management
JF - International Journal of Quality and Reliability Management
IS - 2
ER -