PurposeThe purpose of this paper is to address reported weaknesses with existing equipment reliability improvement methods through their integration into the Six‐Sigma DMAIC methodology.Design/methodology/approachThe 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.FindingsIt 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.Research limitations/implicationsSix‐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.Originality/valueThe 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.
PurposeThe purpose of this paper is to develop a preventive maintenance (PM) model for auxiliary components whose failures may not necessarily correspond to system failure but rather to faster system degradation.Design/methodology/approachThe concept of load sharing was utilized to build a suitable Markov model for the problem. Regression analysis was used to estimate the various transition rates of the model. A real field application was used to illustrate the model.FindingsModels addressing the design of an optimal PM strategy for such a problem are rare in the literature. The load‐sharing concept was borrowed and found very useful to model this problem. Regression analysis based on real field data was also found to be useful to estimate the model transition rates.Originality/valueThis paper addresses a problem that is not given enough attention in the currently available literature. Available models assume that a PM activity will restore the equipment to an as new, or at least to a better, condition. There exist situations, however, where a PM activity does not amend any damage but instead slows down further deterioration.
1 Sometimes, the failure of an auxiliary component may not have an adverse effect on the system if it occurs alone. Only when another component fails simultaneously with it, a significant interaction effect on the system is encountered.Abstract -Auxiliary components in some systems exist only to serve some other primary components. Preventive Maintenance (PM) activities are, therefore, done to these auxiliary components for the sake of extending the life of the primary component.Frequent PM care to auxiliary components increase the cost of PM while infrequent PM leads to increasing downtime of the primary component and, subsequently, production losses. The search for the optimal level of PM care requires a non-linear programming (NLP) solution that can, sometimes, be very complex. This paper suggests that a very useful pattern exists in the NLP model of this optimization problem which can significantly reduce the complexity of model formulation and the arrival to the NLP solution.
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