£107.27

Springer Structural Health Monitoring - Signal Processing Book

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Description

This Springer publication provides a comprehensive look at the latest advances in signal processing for structural health monitoring (SHM). It serves as a specialized resource for understanding how modern techniques can be applied to monitor the health status of vital machinery and structures. Readers will explore advanced methods including wavelet theory, time-frequency analysis, and empirical mode decomposition. The text also covers compressive sensing, sparse representation, and stochastic resonance to provide a complete technical perspective. By focusing on practical applications, the book explains how these signal processing tools help maintain the safety and efficiency of critical assets like wind turbines, gas turbines, and machine tools. Designed as a key reference guide, this volume is built for researchers and graduate students seeking to master the intersection of smart sensors and measurement instrumentation. It offers the technical depth required to understand how to monitor the structures and machines encountered in daily life through sophisticated mathematical and engineering approaches.

Key Features

Covers advanced signal processing methods such as wavelet theory and time-frequency analysis for monitoring structural health.

Explores specialized techniques including empirical mode decomposition, compressive sensing, and sparse representation.

Provides technical insights into stochastic resonance to support modern structural health monitoring research.

Focuses on real-world applications for critical assets like wind turbines, gas turbines, and various machine tools.

Serves as a detailed reference guide for graduate students and professional researchers in the field.

Part of the Smart Sensors, Measurement and Instrumentation series for academic and professional study.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
25 July 2018
Listed Since
25 July 2018

Barcode

No barcode data available

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