Lead Session Chair:
Stephan Barth, Managing Director, ForWind - Center for Wind Energy Research, Germany
Kun Marhadi (1) F P
(1) Brüel & Kjær Vibro, Nærum, Denmark
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Presenter's biographyBiographies are supplied directly by presenters at OFFSHORE 2015 and are published here unedited
Dr. Kun Marhadi is a diagnostic engineer in the Remote Monitoring Group at Brüel & Kjær Vibro. He joined Brüel & Kjær Vibro in 2012. He had previously worked as a postdoctoral fellow in the Department of Mathematics at the Technical University of Denmark (DTU). He received his PhD in computational science in 2010 from San Diego State University and Claremont Graduate University. He has M.S. and B.S. in aerospace engineering from Texas A&M University. Dr. Marhadi's expertise is in structural vibration and analyses, probabilistic methods, and design optimization.
Condition monitoring of offshore wind turbines with multilevel severity assessment of potential faults to help plan maintenance
Having condition monitoring system (CMS) in offshore wind turbines is vital for planning maintenance in case a machine component fault occurs. The turbines may not always be accessible all year round. Having a fault detected as early as possible at its development stage is crucial so that inspection and maintenance can be planned ahead of time, and maintenance is performed when weather condition allows it. In that way the turbine can have maximum uptime and the cost of maintenance can be reduced. Experience shows that lead-time plays a very important role in maintenance planning, but traditionally is difficult to estimate.
The present study shows how online CMS installed in offshore wind turbines can detect potential faults in the gearbox and generator at the very early stage despite stochastic nature of the wind turbine operating conditions. The CMS is based on vibration, and several vibration measurements (e.g. ISO RMS, Crest Factor, Gear Mesh Frequency, etc.) are trended over time. When one or more measurements cross predetermined limits, they will trigger an alarm. The alarm severity is determined automatically using simple algorithm and predetermined conditions, and thus urgency for attending the fault and the impending maintenance needed can be initially assessed.
Main body of abstract
In this work, there are four severity levels of the alarms used as prognosis tools (lead-time estimation) of a potential fault. The urgency of maintenance can be determined at each severity level. The lowest alarm severity is simply a notification that a potential problem is detected with at least 6 months before a real action is required. As a fault progresses, higher severities can be assessed with reduced lead-time for maintenance. The third and second level severities can have estimated lead-time ranging from 4 months to 2 weeks. The final one will recommend not running the turbine because keeping it in operation may lead to consequential damages. Having an online CMS on a turbine allows this continuous monitoring of a fault development, and having vibration specialists in a remote location that perform diagnostic of the potential fault can help pinpoint the actual fault as well as verifying the lead-time estimation. Case studies from offshore wind turbines are presented to illustrate the benefits of having this multilevel severity assessment in planning maintenance, such as in generator bearing and gear related problems at the third stage of a gearbox.
The case studies show the benefits of having multilevel severity assessment in maintenance planning. The development of a fault can be tracked and closely monitored by having online CMS until maintenance can be done when offshore condition permits. Multiple visits to the turbines can be minimized, and thus maintenance cost is reduced.
The study aims to show how to use information regarding severity of a potential fault detected in an offshore wind turbine in planning maintenance by showing several examples from the industry. A fault detected at early stage does not need to be inspected when sea condition is rough. Knowing the fault develops into a later stage and having the knowledge of how much time until maintenance is required can help plan maintenance.