A multi-head self-attention autoencoder network for fault detection of wind turbine gearboxes under random loads*
Xiaoxia Yu,
Zhigang Zhang,
Baoping Tang
et al.
Abstract:Wind turbine gearboxes work under random load for extended periods of time, and the fault detection indicator constructed by the existing deep learning models fluctuate constantly due to the load, which is easy to cause frequent false alarms. Therefore, a multihead self-attention autoencoder network is proposed and combined with a dynamic alarm threshold to detect faults in a wind turbine gearbox subjected to random loads. The multiheaded attention mechanism layer enhances the feature-extraction capability of … Show more
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