Early fault detection with multi-target neural networks
- URL: http://arxiv.org/abs/2106.08957v1
- Date: Sat, 12 Jun 2021 11:35:33 GMT
- Title: Early fault detection with multi-target neural networks
- Authors: Angela Meyer
- Abstract summary: Multi-target neural networks were applied to the task of early fault detection in drive-train components.
We found that multi-target multi-layer perceptrons (MLPs) detected faults at least as early and in many cases earlier than single-targets.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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