The offshore wind power generation industry is critically dependent upon high capital-cost turbines. Such assets are required to operate over long periods of time, often in harsh conditions and high stresses, with minimal maintenance. The ever increasing size, complexity and remote location of wind turbines results in maintenance contributing a significant proportion of the cost-per-unit generated. The industry needs sensor and digital technologies to provide a route to faster and better maintenance decision-making – boosting safety, productivity and efficiency, and helping to maintain profitability. At a time of lower prices, revenues and capital spending, sensor technologies combined with data analytics stand out as a leading contributor for reducing costs. Data driven analytics can be employed to detect when equipment is going to fail, or can be used to run that same equipment close to design capacity, to maximise asset use. We propose an intelligent sensor technology that complements our existing vibration measurement expertise with lubricant analysis: a potent predictive data analytics tool which will bring advanced asset management to the industry.