Wind Turbines – Reliability Centered Maintenance (RCM)

By Bahwan CyberTek, April 10, 2017


Reliability Centered Maintenance (RCM) is a familiar concept to most professionals of the O&M realm. But for newbies, it could be quite daunting. This blog seeks to give a brief description about RCM that might help in understanding this essential process: RCM is not a maintenance methodology in itself, but a framework for choosing the appropriate maintenance strategy (reactive, preventive and predictive) that is to be applied to each component of the system. As per, RCM strives to balance the cost and effort needed to maintain each component with the environmental, health and safety (EH&S) concerns, and economic consequences of that component’s failure in mind.

Predictive maintenance is all about saving the system from potential downtime. For example: the gearbox, generator, or the main bearing of the turbine drive train are the subsystems that account for more than half of the downtime, in Wind Turbine systems, worldwide. Now, in real-time though, it is not just the replacement of the failed part that could cause the downtime; it could be affected by the availability and lead time, or the need for special equipment. Ironically, Predictive analytics systems do not take such factors into consideration, except for identifying the failing part – The rate at which a component fails is determined by the equipment installed, maintenance performed, and external conditions to which the part is exposed. And large turbines are subject to a lot of external factors, such as Wind Speed and Velocity Pressure, Wind directionality, etc., Such scenarios beg the need for a clear focus area for condition monitoring efforts; And that is exactly where RCM steps-in. It tells us where we need to focus our monitoring efforts, and what maintenance methodology should be used.

Conclusively, RCM is a framework, that works in tandem with your predictive analytics system, to ensure minimal system downtime, ultimately impacting bottom-line business and operational efficiency.

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