Data-Driven Fault Detection and Reasoning for Industrial Monitoring

Data-Driven Fault Detection and Reasoning for Industrial Monitoring Jing Wang Primary Author Jinglin Zhou Primary Author Xiaolu Chen Primary Author mixed material bibliography Springer Singapore Springer Nature 2022 en English 264 Halaman This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book. Statistika Mesin Manajemen Industri 658.5 9789811680441 Perpustakaan UMA Online Public Access Catalog (OPAC) 658.5 Jin d EB26000279 Perpustakaan Pusat UMA 658.5 Jin d Data-Driven Fault Detection and Reasoning for Industrial Monitoring 978-981-16-8044-1.pdf.jpg.jpg 23735 2026-09-15 10:30:54 2026-09-30 10:24:03 machine generated