快速傅里叶变换
编码(社会科学)
计算机科学
方位(导航)
断层(地质)
图像(数学)
人工智能
计算机视觉
电信
算法
地质学
数学
地震学
统计
作者
Kun Chen,Mei Liu,Yu Meng
标识
DOI:10.1088/1361-6501/ad3295
摘要
Abstract To address the problems of low diagnostic accuracy and slow diagnostic speed of the convolutional neural network fault diagnosis method in rolling bearing diagnosis, a new rolling bearing fault diagnosis method based on Fast Fourier Transform (FFT) image coding and Lightweight-Convolutional Neural Network (LCNN) is proposed. The method is mainly divided into three stages: firstly, the original signal is reconstructed by noise reduction using a joint noise reduction method of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Permutation Entropy(PE), and Wavelet Threshold Denoise(WTD); then, the frequency spectra and phase spectra feature fusion data of the noise-reduced and reconstructed bearing vibration signals are obtained by FFT, the feature fusion data are encoded into a heat map, and the image coding data-set is fed into an improved L-CNN for fault diagnosis. Experiments were carried out using the Guangdong University of Petrochemical Technology bearing fault data-set and the Case Western Reserve University bearing fault data-set with diagnostic accuracies of 98.75% and 99%, respectively. The results demonstrate that the method can effectively classify bearing fault vibration signals with the advantages of a fast diagnosis, high accuracy, and good generalization ability.
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