| spelling |
oapen-20.500.12657-524522022-01-15T02:49:43Z Data-Driven Fault Detection and Reasoning for Industrial Monitoring Wang, Jing Zhou, Jinglin Chen, Xiaolu Multivariate causality analysis Process monitoring Manifold learning Fault diagnosis Data modeling Fault classification Fault reasoning Causal network Probabilistic graphical model Data-driven methods Industrial monitoring Open Access bic Book Industry Communication::T Technology, engineering, agriculture::TJ Electronics & communications engineering::TJF Electronics engineering::TJFM Automatic control engineering::TJFM1 Robotics bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence 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. 2022-01-14T13:41:53Z 2022-01-14T13:41:53Z 2022 book ONIX_20220114_9789811680441_39 9789811680441 https://library.oapen.org/handle/20.500.12657/52452 eng Intelligent Control and Learning Systems application/pdf n/a 978-981-16-8044-1.pdf https://link.springer.com/978-981-16-8044-1 Springer Nature Springer Singapore 10.1007/978-981-16-8044-1 10.1007/978-981-16-8044-1 6c6992af-b843-4f46-859c-f6e9998e40d5 9789811680441 Springer Singapore 3 264 open access
|
| description |
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.
|
