Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring

Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring by Dong Wang; Bingchang Hou

Unlock a sharper, faster approach to fault detection with this authoritative guide to sparsity-based signal processing for machine condition monitoring. Written for engineers, researchers, and maintenance professionals, this book demystifies advanced mathematical tools and translates them into practical strategies for real-world reliability challenges.

Inside you’ll find clear explanations of sparsity measures and sparse representation, modern signal-processing techniques such as compressed sensing and time–frequency analysis, and robust approaches to denoising, feature extraction, and anomaly detection. The text connects theory to practice, showing how sparsity-driven methods improve sensitivity to early faults, reduce false alarms, and enhance predictive maintenance workflows across industries.

Whether you work in manufacturing, energy, transportation, or aerospace, the methods presented help you detect bearing faults, gear defects, rotor imbalances and other common failures more reliably. Global practitioners—from maintenance teams in factories to research groups in academic labs—will benefit from the book’s practical orientation and focus on deployable solutions that cut downtime and optimize asset life.

Clear diagrams, step-by-step explanations, and application-focused guidance make complex concepts accessible without sacrificing rigor. If you’re seeking to modernize your condition-monitoring toolkit, increase diagnostic confidence, and translate signal-processing theory into measurable reliability gains, this title belongs on your shelf.

Order your copy today to bring advanced sparsity techniques into your maintenance program and stay ahead in condition monitoring and predictive maintenance.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.