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This paper presents a case study on condition monitoring of power generators at offshore wind turbines. Two fault detection models are proposed for detecting sudden changes in the sensed value of metallic debris at the generator. The first model uses an exponentially weighted moving average, while the second monitors first-order derivatives using a fixed threshold. This is expected to improve the maintenance activities by avoiding late or early part replacement. The economic impact of the proposed approach is also provided with a realistic depiction of the cost structure associated with the corresponding maintenance plan. While the specifics of the case study are supported by real-life data, considering the prevalence of the use of generators not only in offshore wind turbines but also in other production environments, we believe the case study covered in this paper can be used as a blueprint for similar studies in other applications. • Large scale condition monitoring demonstrates economic benefits. • Case study from real wind turbine population with economic impact assessment. • Sensor for monitoring fault mode results in lead time for maintenance planning. • Sensitivity analysis assesses results despite downtime and power price uncertainty. [ABSTRACT FROM AUTHOR]