Artificial Intelligence-Driven Engineering Vibration Identification, Prediction and Control: Advances in Engineering Vibration

Huang, Wei; Xu, Jian

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 cutting-edge methods including CNN-LSTM, transfer learning, parallel LSTM-Transformer, and MRD semi-active control optimized by deep learning, delivering breakthroughs in multi-source vibration recognition, nonlinear system prediction, and nuclear explosion vibration control. 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.

Details

Published by: Springer

Publication Date: 2026-10-11

Format: Hardcover

ISBN-13: 9789819228294

DOI:

Dimensions: 235cm x155cm

Pages: