Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems--基于深度神经网络的机械系统智能故障诊断

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Deep Neural Networks-Enabled

Intelligent Fault Diagnosis

of Mechanical Systems

is book aims to highlight the potential of deep learning (DL)-enabled methods in intelligent fault

diagnosis (IFD), along with their benets and contributions.

e authors rst introduce basic applications of DL-enabled IFD, including autoencoders, deep be-

lief networks, and convolutional neural networks. Advanced topics of DL-enabled IFD are also ex-

plored, such as data augmentation, multi-sensor fusion, unsupervised deep transfer learning, neural

architecture search, self-supervised learning, and reinforcement learning. Aiming to revolutionize

the nature of IFD, this book contributes to improved eciency, safety, and reliability of mechanical

systems in various industrial domains.

e book will appeal to academic researchers, practitioners, and students in the elds of intelligent

fault diagnosis, prognostics and health management, and deep learning.

Ruqiang Yan is a professor at the School of Mechanical Engineering, Xi’an Jiaotong University.

His research interests include data analytics, articial intelligence, and energy-ecient sensing and

sensor networks for the condition monitoring and health diagnosis of large-scale, complex, dynamical

systems.

Zhibin Zhao is an assistant professor at the School of Mechanical Engineering, Xi’an Jiaotong

University. His research interests include sparse signal processing and machine learning, especially

deep learning for machine fault detection, diagnosis, and prognosis.

Deep Neural Networks-Enabled

Intelligent Fault Diagnosis

ofMechanical Systems

Ruqiang Yan and Zhibin Zhao

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标签： #机械

摘要：

本文深入探讨了基于深度神经网络的机械系统智能故障诊断技术，系统梳理了深度学习在旋转机械、齿轮箱、轴承等关键部件故障识别与分类中的应用方法。文章从数据驱动角度出发，详细介绍了卷积神经网络（CNN）、循环神经网络（RNN）、自编码器（AE）及生成对抗网络（GAN）等主流深度学习模型在振动信号处理、特征自动提取与故障模式识别中的最新研究进展。同时，针对实际工业环境中数据不平衡、噪声干扰及变工况适应等挑战，提出了相应的优化策略与改进方案。通过典型案例分析，验证了深度神经网络在提升故障诊断准确性、鲁棒性与

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Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems--基于深度神经网络的机械系统智能故障诊断

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