Search results

This paper aims to propose an approach for the optimization of imperfect preventive maintenance and corrective actions performed on a single machine. After maintenance, the machine returns to an age between “as good as new” and “as bad as old”.

The approach is based on fuzzy logic and simulation‐based optimization. Fuzzy logic is preferred over crisp logic because it is relatively easy to implement in this situation considering that the human factor is hardly interpreted by analytical methods because of its unpredictable nature. Simulation‐based optimization is used to have a more reactive and accurate tool for practitioners.

Taking into account the impact of the imperfections due to human factors, the period for preventive maintenance, which minimizes the expected cost rate per unit of time or maximizes the availability of the system, is evaluated by the simulation‐based optimization.

Different and more realistic maintenance levels must be considered and the traceability of a specific system could be used to determine the most appropriate failure law. For this study, cost reduction was considered as the priority, but the model can be adjusted according to the user's preferences.

This paper considers a single repairable machine as a system that undergoes periodic preventive and corrective maintenance actions. Considering maintenance imperfections, rule‐based fuzzy logic can be integrated into the maintenance program to determine the times for the periodic preventive maintenance actions.

Considering human factors in maintenance programs is indispensable to assure more accurate and realistic results. However, due to the difficulty engendered by their modeling, most theoretical maintenance models do not consider these factors. Therefore, the proposed fuzzy model in the paper can be an important tool to include them.

The purpose of this paper is to address reported weaknesses with existing equipment reliability improvement methods through their integration into the Six‐Sigma DMAIC methodology.

The evaluation was done by assessing the weaknesses of traditional methods such as reliability centered maintenance (RCM), evaluating what Six‐Sigma could potentially offer to close the gaps, and testing potential improvements through an example application.

It is concluded that Six‐Sigma addresses many RCM flaws and weaknesses. It is also concluded that Six‐Sigma, if integrated with other reliability techniques, can produce results that are far more objective and dependable.

Six‐Sigma, however, still bears its own cons and limitations. It requires good data which are sometimes unavailable. Six‐Sigma is also lengthier and consumes more resources per single problem since it focuses at one problem at a time.

The introduction of Six‐Sigma into equipment reliability/maintenance applications is quite original since this methodology has traditionally been limited to manufacturing and only recently to administrative processes. The outcome is of significant value as it opens up a new perspective into the development of reliability improvement measures for plant equipment.

The purpose of this paper is to evaluate and improve the quality and the reliability of pre‐treatment quality controls of an efficient technique of radiotherapy called IMRT (intensity‐modulated radiation therapy). The aim is then to determine if the controls can be safely reduced while keeping an optimal level of quality.

The statistical process control method (SPC) was applied to quality assurance in IMRT. In order to characterize prostate and head‐and‐neck treatment process variability, individual value control charts and moving‐range control charts were established.

Control charts showed that prostate and head‐and‐neck treatment processes are only subject to random causes of variability, which means they are statistically controlled. It was proved that both processes are statistically stable and capable.

The paper shows that SPC is an efficient method to objectively determine if quality controls can be reduced.