<p>For complex technical systems in the electricity and pulp and paper industries, maintenance management addresses how to exploit physical assets in the most profitably way. This is a difficult task that requires taking into consideration parameters of totally different natures – e.g. reliability data, operating costs, condition of technical systems, the environment and rules and regulation.</p><p>An incorrect estimate of a residual lifetime can result in a premature renewal with accompanying high capital costs. If, however, renewal is delayed, a breakdown may occur which can cause major damage to technical equipment and a loss of income due to outages. Because of the complexity of many technical systems, it can be hard to select adequate data to use when making decisions about renewal strategies. To cope with this, one approach is to use less detailed models that are operated by skilled analysts.</p><p>This work demonstrates the advantage of such an approach by proposing two methods applied in a joint methodology that has its origins in RCM. The methodology consists of Dynamic Lifetime Model (DLA) and the Condition Based Index (CBI). The DLA method copes with the financial risk associated with the point in time for when a renewal is carried out and the CBI method uses critical parameters to estimate the condition of a technical system. The two methods together create a quantitative connection between reliability, maintenance and financial risk. A case study based validation of the methodology was carried out at SCA Ortvikens paper mill on a refiner system and Forsmark nuclear power plant. Lessons learned from the case study showed that the methodology could be used to identify which components could cause costly breakdown. By using the methodology a manager gets a decision support tool for estimating short-term and long-term consequences of decisions regarding maintenance management in order to maximize utility of the system concerned</p>

For complex technical systems in the electricity and pulp and paper industries, maintenance management addresses how to exploit physical assets in the most profitably way. This is a difficult task that requires taking into consideration parameters of totally different natures – e.g. reliability data, operating costs, condition of technical systems, the environment and rules and regulation. An incorrect estimate of a residual lifetime can result in a premature renewal with accompanying high capital costs. If, however, renewal is delayed, a breakdown may occur which can cause major damage to technical equipment and a loss of income due to outages. Because of the complexity of many technical systems, it can be hard to select adequate data to use when making decisions about renewal strategies. To cope with this, one approach is to use less detailed models that are operated by skilled analysts. This work demonstrates the advantage of such an approach by proposing two methods applied in a joint methodology that has its origins in RCM. The methodology consists of Dynamic Lifetime Model (DLA) and the Condition Based Index (CBI). The DLA method copes with the financial risk associated with the point in time for when a renewal is carried out and the CBI method uses critical parameters to estimate the condition of a technical system. The two methods together create a quantitative connection between reliability, maintenance and financial risk. A case study based validation of the methodology was carried out at SCA Ortvikens paper mill on a refiner system and Forsmark nuclear power plant. Lessons learned from the case study showed that the methodology could be used to identify which components could cause costly breakdown. By using the methodology a manager gets a decision support tool for estimating short-term and long-term consequences of decisions regarding maintenance management in order to maximize utility of the system concerned / QC 20111216