Sensor techniques and emerging CNN models have greatly facilitated the development of
collaborative fault diagnosis. Existing CNN models apply different fusion schemes to …

A wind turbine (WT) is an electromechanical system that often operates under a wide range
of production conditions. These electrical systems are nowadays expanding rapidly, and …

Intelligent fault diagnosis plays an important role in maintaining the safe and reliable
operation of rotating machinery. However, the data collected in real engineering scenarios …

The fault diagnosis method based on model and signal processing has some problems,
such as difficulty in modeling and difficulty in extracting signal features; As the depth of the …

Currently, numerous machine learning (ML) techniques are being applied in the field of
renewable energy (RE). These techniques may not perform well if they do not have enough …

Recently, unsupervised domain adaptation fault diagnosis (FD) techniques, which learn
transferable features by reducing distribution inconsistency of source and target domians …

Strong background noise, multiple interferences, and complex transmission paths
contaminate weak early fault signatures of vibration signals from rotating machineries …

The gearbox represents a critical component of the wind turbine and its proper functioning is
fundamental to ensure high operational reliability. Efficient extraction of fault information …

While deep learning has found widespread utility in gearbox fault diagnosis, its direct
application to wind turbine gearboxes encounters significant hurdles. Disparities in data …

Compressed sensing can extract fault features from down-sampled signals well below the
Nyquist sampling frequency, alleviating the significant pressure on data storage and …