Data-Driven Fault Detection and Reasoning for Industrial Monitoring

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

Show other versions (1)

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...

Full description

Saved in:
Bibliographic Details
Main Author: Wang, Jing (auth)
Other Authors: Zhou, Jinglin (auth), Chen, Xiaolu (auth)
Format: Electronic Book Chapter
Language:English
Published: Springer Nature 2022
Series:Intelligent Control and Learning Systems 3
Subjects:
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
Online Access:OAPEN Library: download the publication
OAPEN Library: description of the publication
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

OAPEN Library: download the publication
OAPEN Library: description of the publication

3rd Floor Main Library

Holdings details from 3rd Floor Main Library
Call Number: A1234.567
Copy 1 Available

Similar Items