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Abstract
For safe and reliable machine operation, maintenance, repair and overhaul (MRO) activities are required. Spare parts demand forecasting and inventory planning, which is an important part of MRO activities, must be accurate to avoid costs because of surplus spare parts or machine downtimes. The restriction of reduced accessibility to wind turbines during the winter months also has to be taken into account when planning maintenance activities and spare part inventories for wind farms. The presented model provides the most economic stock quantity under given environmental conditions. It is based on the proportional hazards model, which is extended to calculate the remaining useful component life time and derive required spare parts inventory levels. The presented model is validated, using condition monitoring data and environmental data of an onshore wind farm. Comparison of the spare part inventory prediction to wind farm’s failure data proves the model’s accuracy. Parameter analyses show that the model can be applied for spare parts inventory planning under consideration of environmental conditions.
Highlights
For safe and reliable machine operation, maintenance, repair and overhaul (MRO) activities are required
The calculated probability combined with cost parameter of surplus material and downtime because missing spare parts allow for cost-optimal inventory planning
The probability of surplus material is multiplied with the inventory cost per day. Minimizing this function leads to the cost-optimal inventory level, because further cost parameter named in Eq 1 cannot be influenced by means of increasing or lowering spare parts inventory level
Summary
Maintenance, repair and overhaul (MRO) activities are necessary to ensure safe and reliable machine operation. For many MRO processes, spare parts are essential, because their non-availability causes machine downtime. It is prolonged in case of long lead times that entail high operation costs. Stock holding is necessary to achieve high service levels, allowing for short machine downtime. In contrast to high service levels and spare parts availability, inventory costs need to be considered to attain economic machine operation. For implementing weather restrictions, utilizing available information and achieving minimum operation and maintenance cost, mechanically stressed wind energy components have been investigated within the research project ‘‘EloWind’’—service logistics for wind energy turbines. A cost-optimal inventory level of spare parts is derived, taking into account inventory costs of spare parts and downtime costs of wind energy turbines. The proposed method is validated with data of an onshore wind turbine farm
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