卷积神经网络
计算机科学
人工智能
深度学习
断层(地质)
小波包分解
噪音(视频)
模式识别(心理学)
支持向量机
小波
算法
小波变换
图像(数学)
地质学
地震学
作者
Shangjun Ma,Wei Cai,Wenkai Liu,Zhaowei Shang,Geng Liu
出处
期刊:Sensors
[MDPI AG]
日期:2019-05-24
卷期号:19 (10): 2381-2381
被引量:45
摘要
To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) and deep convolutional neural networks (good classification performance, data-driven design and high transfer-learning ability). First, a vibration signal is subjected to pyramid wavelet packet decomposition, and each sub-band coefficient is used as the input for each channel of a deep convolutional network (DCN). Then, based on the lightweight modeling requirements and techniques, a new DCN structure is designed for the fault diagnosis. The proposed algorithm is compared with the support vector machine algorithm and the published DL algorithms based on a bearing dataset produced by Case Western Reserve University. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of accuracy, memory space, computational complexity, noise resistance, and transfer performance, producing good results.
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