LEADER 00000cam 2200637Ii 4500
001 on1292353116
003 OCoLC
005 20221108213017.0
006 m o d
007 cr cnu---unuuu
008 220115s2022 si a ob 000 0 eng d
019 1290840388|a1291146279|a1291170260|a1294362343|a1296666095
020 9789811680441|q(electronic book)
020 9811680442|q(electronic book)
020 |z9789811680434
020 |z9811680434
024 7 10.1007/978-981-16-8044-1|2doi
035 (OCoLC)1292353116|z(OCoLC)1290840388|z(OCoLC)1291146279
|z(OCoLC)1291170260|z(OCoLC)1294362343|z(OCoLC)1296666095
037 |bSpringer
040 EBLCP|beng|erda|epn|cEBLCP|dYDX|dGW5XE|dOCLCO|dDCT|dOCLCF
|dDKU|dOCLCO
049 STJJ
050 4 T57.5|b.W36 2022
072 7 TEC037000|2bisacsh
072 7 TBM|2bicssc
072 7 TBM|2thema
082 04 658.5|223
100 1 Wang, Jing,|eauthor.
245 10 Data-driven fault detection and reasoning for industrial
monitoring /|cJing Wang, Jinglin Zhou, Xiaolu Chen.
264 1 Singapore :|bSpringer,|c[2022]
264 4 |c©2022
300 1 online resource (277 pages) :|billustrations (chiefly
color).
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
347 text file|bPDF|2rda
490 1 Intelligent control and learning systems ;|vvolume 3
504 Includes bibliographical references.
505 0 Introduction -- Basic Statistical Fault Detection Problems
-- Principal Component Analysis -- Canonical Variate
Analysis -- Partial Least Squares Regression -- Fisher
Discriminant Analysis -- Canonical Variate Analysis --
Fault Classification based on Local Linear Embedding --
Fault Classification based on Fisher Discriminant Analysis
-- Quality-Related Global-Local Partial Least Square
Projection Monitoring -- Locality-Preserving Partial Least
-Squares Statistical Quality Monitoring -- Locally Linear
Embedding Orthogonal Projection to Latent Structure
(LLEPLS) -- Bayesian Causal Network for Discrete Systems -
- Probability Causal Network for Continuous Systems --
Dual Robustness Projection to Latent Structure Method
based on the L_1 Norm.
506 0 Open access|5GW5XE
520 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.
588 Description based upon print version of record.
590 Springer Nature|bSpringer Nature Open Access eBooks
650 0 Industrial engineering|xData processing.
650 0 Fault location (Engineering)|xData processing.
650 7 Fault location (Engineering)|xData processing.|2fast
|0(OCoLC)fst00921984
650 7 Industrial engineering|xData processing.|2fast
|0(OCoLC)fst00970996
655 0 Electronic books.
700 1 Zhou, Jinglin,|eauthor.
700 1 Chen, Xiaolu,|eauthor.
776 08 |iPrint version:|aWang, Jing|tData-Driven Fault Detection
and Reasoning for Industrial Monitoring|dSingapore :
Springer Singapore Pte. Limited,c2022|z9789811680434
830 0 Intelligent control and learning systems ;|vvolume 3.
914 on1292353116
947 MARCIVE Processed 2023/02/10
994 92|bSTJ
