Artificial Intelligence-Driven Engineering Vibration Identification, Prediction and Control
Advances in Engineering Vibration
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Beschreibung
This book lies at the intersection of engineering vibration and artificial intelligence, bridging core disciplines like mechanical engineering, civil engineering, and control science. It introduces a range of cutting-edge methods including machine learning, deep learning and reinforcement learning. It closely integrates these with the core links of engineering vibration, namely identification, prediction and control, puts forward a series of new approaches, and solves many practical engineering problems. These innovations address long-standing industry challenges of data scarcity, strong nonlinearity, and poor generalization of traditional methods. The book presents complex theories through intuitive visualizations and step-by-step technical workflows, balancing academic depth with practical operability. For readers, it offers actionable solutions for vibration identification, prediction, and control, while establishing a systematic knowledge system integrating data-driven and physics-informed modeling. It is ideal for researchers, engineers, and graduate students in vibration control, intelligent manufacturing, aerospace, civil engineering, and related fields seeking to advance their work with AI-powered technologies.
This book lies at the intersection of engineering vibration and artificial intelligence, bridging core disciplines like mechanical engineering, civil engineering, and control science. It introduces a range of cutting-edge methods including machine learning, deep learning and reinforcement learning. It closely integrates these with the core links of engineering vibration, namely identification, prediction and control, puts forward a series of new approaches, and solves many practical engineering problems. These innovations address long-standing industry challenges of data scarcity, strong nonlinearity, and poor generalization of traditional methods. The book presents complex theories through intuitive visualizations and step-by-step technical workflows, balancing academic depth with practical operability. For readers, it offers actionable solutions for vibration identification, prediction, and control, while establishing a systematic knowledge system integrating data-driven and physics-informed modeling. It is ideal for researchers, engineers, and graduate students in vibration control, intelligent manufacturing, aerospace, civil engineering, and related fields seeking to advance their work with AI-powered technologies.
Integrates AI with engineering vibration, covering recognition, prediction, and control in one systematic framework Presents practical models validated by real-world data for direct engineering application Addresses small-sample challenges, enabling high-precision prediction with limited experimental data
Autor*in
Wei Huang
Themen in »Artificial Intelligence-Driven Engineering Vibration Identification, Prediction and Control«
Engineering vibration recognition Engineering vibration prediction Magnetorheological damper (MRD) Deep learning model Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) Semi-active vibration control Nuclear explosion vibration Transfer learning application Nonlinear vibration system Time series prediction Parallel LSTM-Transformer Vibration signal processing Multi-source vibration identification MRD output force prediction