2,344 research outputs found
Early fault detection with multi-target neural networks
Wind power is seeing a strong growth around the world. At the same time,
shrinking profit margins in the energy markets let wind farm managers explore
options for cost reductions in the turbine operation and maintenance.
Sensor-based condition monitoring facilitates remote diagnostics of turbine
subsystems, enabling faster responses when unforeseen maintenance is required.
Condition monitoring with data from the turbines' supervisory control and data
acquisition (SCADA) systems was proposed and SCADA-based fault detection and
diagnosis approaches introduced based on single-task normal operation models of
turbine state variables. As the number of SCADA channels has grown strongly,
thousands of independent single-target models are in place today for monitoring
a single turbine. Multi-target learning was recently proposed to limit the
number of models. This study applied multi-target neural networks to the task
of early fault detection in drive-train components. The accuracy and delay of
detecting gear bearing faults were compared to state-of-the-art single-target
approaches. We found that multi-target multi-layer perceptrons (MLPs) detected
faults at least as early and in many cases earlier than single-target MLPs. The
multi-target MLPs could detect faults up to several days earlier than the
single-target models. This can deliver a significant advantage in the planning
and performance of maintenance work. At the same time, the multi-target MLPs
achieved the same level of prediction stability
Multi-target normal behaviour models for wind farm condition monitoring
The trend towards larger wind turbines and remote locations of wind farms
fuels the demand for automated condition monitoring strategies that can reduce
the operating cost and avoid unplanned downtime. Normal behaviour modelling has
been introduced to detect anomalous deviations from normal operation based on
the turbine's SCADA data. A growing number of machine learning models of the
normal behaviour of turbine subsystems are being developed by wind farm
managers to this end. However, these models need to be kept track of, be
maintained and require frequent updates. This research explores multi-target
models as a new approach to capturing a wind turbine's normal behaviour. We
present an overview of multi-target regression methods, motivate their
application and benefits in wind turbine condition monitoring, and assess their
performance in a wind farm case study. We find that multi-target models are
advantageous in comparison to single-target modelling in that they can reduce
the cost and effort of practical condition monitoring without compromising on
the accuracy. We also outline some areas of future research.Comment: 7 pages, 5 figure
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