Gene

Wavelet packet decomposition

Transformation (genetics)

Algorithm

Residual

Linguistics

Engineering

Philosophy

Fault detection and isolation

Bandwidth (computing)

Marine engineering

Actuator

Telecommunications

Frequency band

Biochemistry

Computer science

Fault (geology)

Seismology

Drivetrain

Geology

Wavelet transform

Mechanical engineering

Thermodynamics

Physics

Torque

Pattern recognition (psychology)

Feature (linguistics)

Automotive engineering

Time–frequency analysis

Artificial intelligence

Turbine

Wind power

Chemistry

Artificial neural network

Radar

Feature extraction

Wavelet

Electrical engineering

Convolution (computer science)

Measurement

It is significant to boost the performance of fault diagnosis of wind turbine gearboxes. In this paper, a hybrid attention improved residual network (HA-ResNet) based method is proposed to diagnose the fault of wind turbines gearbox by highlighting the essential frequency bands of wavelet coefficients and the fault features of convolution channels. First, the paper performed wavelet packet transformation (WPT) on the raw signal and improved the ResNet by the band attention to highlight features of wavelet coefficients. Second, a fault diagnosis framework based on channel attention is designed to effectively improve the nonlinear feature extraction ability of deep convolutional networks. The proposed method is verified by a simulation dataset of the drivetrain diagnostic simulator (DDS) and the measured data from a wind farm. The results illustrate the superior performance of the HA-ResNet based fault diagnosis method for time–frequency feature extraction of vibration signals, frequency band information enhancement, and recognition accuracy improvement.

A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox