AhtractIn the past, attempts to approximate this balance have often been based on trial and error. In this paper, an analytical tool is described, which helps to identify the best maintenance policies. Throughout, the terminology proposed in the above reference will be applied.The purpose of maintenance is to extend equipment lllfetime, or at least the mean time to the next failure. While too little maintenance may have very costly consequences, maintenance, too, incurs expenditures and it may not be economical to perform it too frequently. Therefore, the two BACKGROUND costs must be balanced.In the past, attempts to approximate this balance have often been based on trial and error. In this paper, a probabilistic model is proposed for the purpose, and a program based on this model is described. The model provides a quantitative connection between reliability and maintenance, a link missing in the heuristic approaches. The component ageing process is modelled, and the mean and distribution of the remaining life to failure are predicted for any stage of ageing. n e method is applied to a practical example.
Control room operators are faced with frequent security-economy decision-making situations necessitated by stressed system operating conditions, and there is increased need for securityeconomy decision-support tools. Although probabilistic methods are promising in this regard, they have been mainly used in planning environments. This task force paper explores their use for operational decision-making, comparing them to the more traditional deterministic approach. Two examples are used to facilitate this comparison via overload and low voltage security assessment to identify secure regions of operation for a small 5-bus system and for the IEEE Reliability Test System. The results of this comparison show that the probabilistic approach offers several inherent advantages.
Abstract-In this paper, the most frequently used maintenance strategies are reviewed. Distinction is made between strategies where maintenance consists of replacement by a new (or "good as new") component and where it is represented by a less costly activity resulting in a limited improvement of the component's condition. Methods are also divided into categories where maintenance is performed at fixed intervals and where it is carried out as needed. A further distinction is made between heuristic methods and those based on mathematical models; the models themselves can be deterministic or probabilistic.From a review of present maintenance policies in electric utilities it is concluded that maintenance at fixed intervals is the most frequently used approach, often augmented by additional corrections. Newer "as needed"-type methods, such as reliability-centered maintenance (RCM), are increasingly considered for application in North America, but methods based on mathematical models are hardly ever used or even considered. Yet only mathematical approaches where component deterioration and condition improvement by maintenance are quantitatively linked can determine the effect of maintenance on reliability. Although more complex, probabilistic models have advantages over deterministic ones: they are capable of describing actual processes more realistically, and also facilitate optimization for maximal reliability or minimal costs.
Abstract-In this paper, the most frequently used maintenance strategies are reviewed. Distinction is made between strategies where maintenance consists of replacement by a new (or "good as new") component and where it is represented by a less costly activity resulting in a limited improvement of the component's condition. Methods are also divided into categories where maintenance is performed at fixed intervals and where it is carried out as needed. A further distinction is made between heuristic methods and those based on mathematical models; the models themselves can be deterministic or probabilistic.From a review of present maintenance policies in electric utilities it is concluded that maintenance at fixed intervals is the most frequently used approach, often augmented by additional corrections. Newer "as needed"-type methods, such as reliability-centered maintenance (RCM), are increasingly considered for application in North America, but methods based on mathematical models are hardly ever used or even considered. Yet only mathematical approaches where component deterioration and condition improvement by maintenance are quantitatively linked can determine the effect of maintenance on reliability. Although more complex, probabilistic models have advantages over deterministic ones: they are capable of describing actual processes more realistically, and also facilitate optimization for maximal reliability or minimal costs.
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