TY  - GEN
AU  - Wang,Jing
AU  - Zhou,Jinglin
AU  - Chen,Xiaolu
TI  - Data-Driven Fault Detection and Reasoning for Industrial Monitoring
SN  - 978-981-16-8044-1
PY  - 2022///
PB  - Springer Nature
KW  - Robotics
KW  - bicssc
KW  - Artificial intelligence
KW  - Multivariate causality analysis
KW  - Process monitoring
KW  - Manifold learning
KW  - Fault diagnosis
KW  - Data modeling
KW  - Fault classification
KW  - Fault reasoning
KW  - Causal network
KW  - Probabilistic graphical model
KW  - Data-driven methods
KW  - Industrial monitoring
KW  - Open Access
N1  - Open Access
N2  - 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
UR  - https://library.oapen.org/bitstream/20.500.12657/52452/1/978-981-16-8044-1.pdf
UR  - https://directory.doabooks.org/handle/20.500.12854/77320
ER  -