Turbogroup Spare Part Optimization by Availability Centered Maintenance Methodology: An Application to LTSA Contract

2003 ◽  
Author(s):  
Giuseppe Fabio Cheschini ◽  
Fausto Carlevaro ◽  
Giuseppe Racioppi ◽  
Andrea Masi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Block Diagram ◽  
Spare Part ◽  
Spare Parts ◽  
Minimum Level ◽  
Part List ◽  
Simulation Techniques ◽  
System Components ◽  
Penalty Clauses

Most Oil&Gas companies that currently outsource both maintenance and related engineering activities, by establishing Long Terms Service Agreements (LTSA) with engine manufacturers, base their contracts on several clauses. A minimum level of overall Plant Availability is usually to be guaranteed by the maintenance services supplier. Both parts agree upon this availability threshold and Bonus/Penalty clauses are based on this value. The Availability of a system is a non-linear function of: • Reliability, in terms of components life, system functional configuration and scheduled maintenance frequencies; • Maintainability, in terms of time to repair/restore, duration of scheduled maintenance tasks and special maintenance tools on site; • Logistics. Concerning Logistics, the problem is to define both the set and levels of spare parts in a warehouse for one or more installations (pooling) and the location of the warehouse itself. In this paper a solution for this problem is presented, based on RBD (Reliability Block Diagram) Monte Carlo simulation techniques and an associated What-If analysis. One of the deliverables of this optimisation process is the ranking of all the system components in terms of their influence on the availability of the whole system. This spare part list optimisation is one of the major deliverables of the discipline called ACM “Availability Centered Maintenance”, currently developed in Nuovo Pignone.

scholarly journals Practical Risk Assessment of Ground Vibrations Resulting from Blasting, Using Gene Expression Programming and Monte Carlo Simulation Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 472 ◽  
Author(s):  
Amir Mahdiyar ◽  
Danial Jahed Armaghani ◽  
Mohammadreza Koopialipoor ◽  
Ahmadreza Hedayat ◽  
Arham Abdullah ◽  
...  
Keyword(s):  
Gene Expression ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Gene Expression Programming ◽  
Sensitivity Analyses ◽  
Accurate Estimation ◽  
Blasting Operation ◽  
Simulation Techniques ◽  
Input Variables ◽  
The Impact

Peak particle velocity (PPV) is a critical parameter for the evaluation of the impact of blasting operations on nearby structures and buildings. Accurate estimation of the amount of PPV resulting from a blasting operation and its comparison with the allowable ranges is an integral part of blasting design. In this study, four quarry sites in Malaysia were considered, and the PPV was simulated using gene expression programming (GEP) and Monte Carlo simulation techniques. Data from 149 blasting operations were gathered, and as a result of this study, a PPV predictive model was developed using GEP to be used in the simulation. In order to ensure that all of the combinations of input variables were considered, 10,000 iterations were performed, considering the correlations among the input variables. The simulation results demonstrate that the minimum and maximum PPV amounts were 1.13 mm/s and 34.58 mm/s, respectively. Two types of sensitivity analyses were performed to determine the sensitivity of the PPV results based on the effective variables. In addition, this study proposes a method specific to the four case studies, and presents an approach which could be readily applied to similar applications with different conditions.

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