Data-Driven Fault Detection and Reasoning for Industrial Monitoring

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 proce...

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Автор:	Wang, Jing (auth)
Інші автори:	Zhou, Jinglin (auth), Chen, Xiaolu (auth)
Формат:	Електронний ресурс Частина з книги
Мова:	Англійська
Опубліковано:	Springer Nature 2022
Серія:	Intelligent Control and Learning Systems 3
Предмети:	Robotics Artificial intelligence 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
Онлайн доступ:	OAPEN Library: download the publication OAPEN Library: description of the publication
Теги:	Додати тег Немає тегів, Будьте першим, хто поставить тег для цього запису!

Data-Driven Fault Detection and Reasoning for Industrial Monitoring за авторством Wang, Jing, Zhou, Jinglin, Chen, Xiaolu

Опубліковано 2022

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Електронний ресурс eКнига

Data-Driven Fault Detection and Reasoning for Industrial Monitoring за авторством Wang, Jing

Опубліковано 2022

DOAB: download the publication
DOAB: description of the publication

Електронний ресурс Частина з книги

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

Data-Driven Fault Detection and Reasoning for Industrial Monitoring за авторством Wang, Jing, Zhou, Jinglin, Chen, Xiaolu

Data-Driven Fault Detection and Reasoning for Industrial Monitoring за авторством Wang, Jing

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