Simulation and the monte carlo method

An overview of the recent advances in delay-time-based maintenance modelling

Availability, reliability and economic sustainability of naval propulsion plants are key elements to cope with because maintenance costs represent a large slice of total operational expenses. Depending on the adopted strategy, impact of maintenance on overall expenses can remarkably vary; for example, letting an asset running up until breakdown can lead to unaffordable costs. As a matter of fact, a desideratum is to progress maintenance technology of ship propulsion systems from breakdown or preventive maintenance up to more effective condition-based maintenance approaches. The central idea in condition-based maintenance is to monitor the propulsion equipment by exploiting heterogeneous sensors, enabling diagnosis and, most of all, prognosis of the propulsion system’s components and of their potential future failures. The success of condition-based maintenance clearly hinges on the capability of developing effective predictive models; for this purpose, effective use of machine learning methods is proposed...

Machine learning approaches for improving condition-based maintenance of naval propulsion plants

Sequential Monte Carlo による強化学習メタパラメータ調整

https://pure.tue.nl/ws/files/3815115/24194911834760.pdf

A survey of maintenance and service logistics management

This article presents a methodology for analysing the cost of operating marine systems under varying conditions. Data obtained from a previously developed Monte Carlo analysis are applied to assess the operational costs for various maintenance and inspection policies. The concept of total insured value is also applied to determine the cost attributed to risk. The aim is to show that Monte Carlo analysis can be adapted to provide information on various factors affecting operational costs to be used for decision-making to optimise the efficiency of marine systems. A method of modelling the effects of lead times due to un-stocked items has also been included to increase the scope of the analysis.

/pdf/a-model-assessing-cost-of-operating-marine-systems-using-4bybqk3vtn.pdf

A model assessing cost of operating marine systems using data obtained from Monte Carlo analysis

This paper presents a methodology using Monte Carlo methods to analyse factors affecting the operational efficiency of marine systems. The aim is to show how Monte Carlo methods can take into account a wide range of factors to facilitate the decision making process for maritime operations. Monte Carlo methods have been used to model the varying effects of different failure modes and the items which contribute to failure for a given system. The methods used have been applied to a case study to examine system failure probability, downtime and maintainable item contributions for a marine cooling system.

Application of the Monte Carlo method to marine system maintenance studies considering failure modes and spare part stock control

When continued operation and function of a ship's systems are required, unforeseen system failure or breakdown can often have disastrous and costly consequences. Ship owners and operators require ships to be operating at full capacity as often as possible in order to remain profitable. This places the ship's engineering department under pressure to plan maintenance schedules which optimise reliability and minimise downtime. The industry as a whole has been slow to respond to the need for better maintenance planning, often relying on manufacturer's recommendations for the setting of service intervals and the replacement of parts. Monte Carlo Methods have proved to be a powerful tool in the nuclear sector and for around three decades remained exclusive to that industry. In recent years researchers have realized the vast potential and flexibility contained in the methodology and its possible application in other fields. One of the problems which is inherent to Monte Carlo Methods is the handling of rare events. Often to remain statistically significant, variance reduction techniques need to be implemented. One of the principal methods is the use of forced simulation. In the marine industry, due to the high levels of salinity, this problem of rare events becomes less significant. It is also the case that often the process mediums used are of a much lower quality than in other industries. This all contributes to the failure probabilities being much higher, negating the need for any forced simulation. The majority of current reliability and maintenance practice is based on time to first failure, or time between failures. Delay-time modelling is a concept which has been developed to be relevant in the operating culture of today's industry. Delay-time analysis provides engineers with a tool which can help to minimise downtime of a machine or plant item, based on an inspection period. Classical delay-time analysis is mathematically arduous and takes time, however the benefits of implementing the technique are well proven and recognised. Monte Carlo Methods lend themselves well to delay-time techniques and could offer an automated analysis tool which requires very little user input. The availability of

Monte Carlo simulation in the marine environment

Modelling of maintenance and inspection policies for marine systems using Monte Carlo simulation and Delay-Time Analysis

This paper presents a methodology for the application of delay-time analysis via Monte Carlo Simulation. The aim is to demonstrate the worth of delay-time analysis and how its application can provide engineers with more information when making maintenance decisions. The methodology has been applied to two case studies.

https://hal-supelec.archives-ouvertes.fr/hal-00642284/document

Application of delay-time analysis via Monte Carlo simulation

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