State Estimation and Fault Diagnosis under Imperfect Measurements 1st Edition

Capture the forefront of reliable control and monitoring with State Estimation and Fault Diagnosis under Imperfect Measurements, 1st Edition by Yang Liu, Zidong Wang, and Donghua Zhou. This essential reference unites theory and practice for engineers, researchers, and graduate students tackling state estimation and fault diagnosis in real-world systems affected by noisy, incomplete, or corrupted measurements.

Discover rigorously developed methods for robust estimation and detection that address sensor noise, bias, packet loss, quantization and missing data—common challenges in modern industrial, aerospace, automotive and power systems. The book presents advanced techniques including extended and adaptive filtering, H-infinity and robust observer design, and model-based fault detection strategies, helping readers translate mathematical insight into reliable system diagnostics and resilience.

Written with clarity and supported by practical examples, this volume guides readers from foundational concepts to cutting-edge solutions. You’ll gain the tools to design estimators and fault-detection schemes that maintain performance under uncertainty, improving system safety and operational uptime across global applications—from smart grids in Europe to manufacturing automation in Asia and transportation systems in North America.

Ideal for practicing control engineers, system architects, and postgraduate researchers, this book is a compact, authoritative resource that balances mathematical depth with real-world applicability. Equip your library with a guide that turns imperfect measurements into actionable intelligence.

Order your copy today to strengthen your design toolkit and ensure dependable state estimation and fault diagnosis in the face of measurement imperfections.

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