Rolling bearing fault diagnosis method based on fast fourier transform coding and lightweight convolutional neural network

A rolling bearing fault diagnosis method based on FFT coding and L-CNN, comprising:

• obtaining original bearing fault vibration data, extracting intrinsic mode components of different frequency bands in the original bearing fault vibration data, calculating a permutation entropy value corresponding to each of the intrinsic mode components, and performing wavelet threshold denoising according to the permutation entropy value to obtain a denoised reconstructed time domain signal;
• performing fast Fourier transform to the denoised reconstructed time domain signal to obtain a frequency domain signal and a phase angle corresponding to the time domain signal, reconstructing the frequency domain signal according to a preset rule, retaining frequency domain data of features of the phase angle, and drawing FFT-x heat maps of different fault type data according to an amplitude range;
• wherein
• the drawing FFT-x heat maps of different fault type data comprises:
• decomposing the denoised reconstructed time domain signal into several single harmonic components by fast Fourier transform, and obtaining a relationship between amplitude, phase, power and frequency domain of each harmonic of the signal;
• and
• when the phase angle is in a first quadrant and a second quadrant, a frequency domain value is positive, and when the phase angle is in a third quadrant and a fourth quadrant, a frequency domain value is negative;
• and according to this characteristic, reconstructing the frequency domain signal, and according to an amplitude characteristic of a reconstructed frequency domain signal, setting a boundary to (−150, 150) for heat map coding;
• and
• constructing an improved lightweight convolutional neural network model L-CNN, and inputting coded data in the FFT-x heat maps into the L-CNN model for processing and diagnosis, and obtaining fault diagnosis results;
• the inputting coded data in the FFT-x heat maps into the L-CNN model for processing and diagnosis, comprising:
• capturing different levels of features of the coded data in the FFT-x from different scales by using convolution kernels with different sizes, and weighting captured features for importance of each channel by structures of global average pooling, one-dimensional convolution, channel multiplication and spatial replication through an ECA attention mechanism;
• and
• processing a channel dimension and a spatial dimension of a color feature of an input FFT-x coded map by depth separable convolution to obtain a feature map;
• exchanging a channel sequence of the feature map between different depths and different groups by using a ChannelSplit module and a Channelshuffle module, and changing a channel arrangement mode;
• further extracting data features based on the residual and depth separable convolution;
• and finally, outputting the fault diagnosis results through average pooling and two fully connected layers.