Filter-Based Fault Diagnosis and Remaining Useful Life Prediction 1st Edition

Grab the cutting edge of prognostics and health management with Filter-Based Fault Diagnosis and Remaining Useful Life Prediction, 1st Edition by Yong Zhang, Zidong Wang, and Ye Yuan. This authoritative resource unlocks practical, mathematically rigorous techniques that engineers and researchers rely on to detect faults early and forecast equipment life with confidence.

Dive into clear, applied explanations of filter-based approaches—including Kalman, extended and unscented filters, as well as particle filtering—and learn how these methods drive reliable fault diagnosis and Remaining Useful Life (RUL) prediction. Realistic case studies and algorithmic workflows bridge theory and practice, making complex concepts accessible for prognostics, condition monitoring, and predictive maintenance applications.

Ideal for control engineers, maintenance managers, data scientists, and graduate students, this volume demonstrates how to reduce downtime, optimize maintenance schedules, and extend asset life across industries such as aerospace, automotive, manufacturing, and energy. Whether you work in Europe, North America, or Asia, the techniques presented are applicable to global industrial networks and IoT-enabled systems.

You’ll come away with actionable skills: designing robust state estimators, implementing fault-detection filters, and producing reliable RUL estimates under uncertainty. The book supports implementation in real-world systems and helps teams translate analysis into measurable operational improvements.

Ready to elevate your prognostics toolkit? Add Filter-Based Fault Diagnosis and Remaining Useful Life Prediction (1st Edition) to your library and start applying proven filter-based methods to safeguard equipment, reduce costs, and make maintenance smarter and more predictive.

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