|
|
|
|
| LEADER |
05533nam a2200577 4500 |
| 001 |
ocn823726462 |
| 003 |
OCoLC |
| 005 |
20170124070245.9 |
| 006 |
m o d |
| 007 |
cr |n||||||||| |
| 008 |
130109s2012 njua ob 001 0 eng d |
| 040 |
|
|
|a EBLCP
|b eng
|e pn
|c EBLCP
|d OCLCQ
|d DG1
|d TXA
|d OCLCF
|d OCLCQ
|d DEBSZ
|d OCLCQ
|d DG1
|d GrThAP
|
| 020 |
|
|
|a 9781118481950
|q (electronic bk.)
|
| 020 |
|
|
|a 111848195X
|q (electronic bk.)
|
| 020 |
|
|
|z 9781118481967
|
| 020 |
|
|
|z 1118481968
|
| 029 |
1 |
|
|a DEBBG
|b BV041829107
|
| 029 |
1 |
|
|a DEBSZ
|b 431281890
|
| 029 |
1 |
|
|a DEBSZ
|b 449336425
|
| 029 |
1 |
|
|a GBVCP
|b 790200066
|
| 029 |
1 |
|
|a NZ1
|b 15341141
|
| 035 |
|
|
|a (OCoLC)823726462
|
| 050 |
|
4 |
|a TP155.75
|b .F527 2013
|
| 082 |
0 |
4 |
|a 660.2815
|a 660/.2815
|a 670
|
| 049 |
|
|
|a MAIN
|
| 100 |
1 |
|
|a Fickelscherer, Richard J.
|
| 245 |
1 |
0 |
|a Optimal automated process fault analysis /
|c Richard J. Fickelscherer, Daniel L. Chester.
|
| 264 |
|
1 |
|a Hoboken, New Jersey :
|b John Wiley and Sons, Inc.,
|c [2013]
|
| 264 |
|
4 |
|c ©2013
|
| 300 |
|
|
|a 1 online resource (xix, 204 pages) :
|b illustrations
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 505 |
0 |
|
|a Optimal Automated Process Fault Analysis; Contents; Foreword; Preface; Acknowledgments; 1 Motivations for Automating Process Fault Analysis; 1.1 Introduction; 1.2 CPI Trends to Date; 1.3 The Changing Role of Process Operators in Plant Operations; 1.4 Methods Currently Used to Perform Process Fault Management; 1.5 Limitations of Human Operators in Performing Process Fault Management; 1.6 The Role of Automated Process Fault Analysis; 1.7 Anticipated Future CPI Trends; 1.8 Process Fault Analysis Concept Terminology; References; 2 Method of Minimal Evidence: Model-Based Reasoning; 2.1 Overview.
|
| 505 |
8 |
|
|a 2.2 Introduction2.3 Method of Minimal Evidence Overview; 2.3.1 Process Model and Modeling Assumption Variable Classifications; 2.3.2 Example of a MOME Primary Model; 2.3.3 Example of MOME Secondary Models; 2.3.4 Primary Model Residuals' Normal Distributions; 2.3.5 Minimum Assumption Variable Deviations; 2.3.6 Primary Model Derivation Issues; 2.3.7 Method for Improving the Diagnostic Sensitivity of the Resulting Fault Analyzer; 2.3.8 Intermediate Assumption Deviations, Process Noise, and Process Transients; 2.4 Verifying the Validity and Accuracy of the Various Primary Models; 2.5 Summary.
|
| 505 |
8 |
|
|a References3 Method of Minimal Evidence: Diagnostic Strategy Details; 3.1 Overview; 3.2 Introduction; 3.3 MOME Diagnostic Strategy; 3.3.1 Example of MOME SV & PFA Diagnostic Rules' Logic; 3.3.2 Example of Key Performance Indicator Validation; 3.3.3 Example of MOME SV & PFA Diagnostic Rules with Measurement Redundancy; 3.3.4 Example of MOME SV & PFA Diagnostic Rules for Interactive Multiple-Faults; 3.4 General Procedure for Developing and Verifying Competent Model-Based Process Fault Analyzers; 3.5 MOME SV & PFA Diagnostic Rules' Logic Compiler Motivations; 3.6 MOME Diagnostic Strategy Summary.
|
| 505 |
8 |
|
|a References4 Method of Minimal Evidence: Fuzzy Logic Algorithm; 4.1 Overview; 4.2 Introduction; 4.3 Fuzzy Logic Overview; 4.4 MOME Fuzzy Logic Algorithm; 4.4.1 Single-Fault Fuzzy Logic Diagnostic Rule; 4.4.2 Multiple-Fault Fuzzy Logic Diagnostic Rule; 4.5 Certainty Factor Calculation Review; 4.6 MOME Fuzzy Logic Algorithm Summary; References; 5 Method of Minimal Evidence: Criteria for Shrewdly Distributing Fault Analyzers and Strategic Process Sensor Placement; 5.1 Overview; 5.2 Criteria for Shrewdly Distributing Process Fault Analyzers; 5.2.1 Introduction.
|
| 505 |
8 |
|
|a 5.2.2 Practical Limitations on Target Process System Size5.2.3 Distributed Fault Analyzers; 5.3 Criteria for Strategic Process Sensor Placement; References; 6 Virtual SPC Analysis and Its Routine Use in FALCONEERTM IV; 6.1 Overview; 6.2 Introduction; 6.3 EWMA Calculations and Specific Virtual SPC Analysis Configurations; 6.3.1 Controlled Variables; 6.3.2 Uncontrolled Variables and Performance Equation Variables; 6.4 Virtual SPC Alarm Trigger Summary; 6.5 Virtual SPC Analysis Conclusions; References; 7 Process State Transition Logic and Its Routine Use in FALCONEERTM IV.
|
| 500 |
|
|
|a 7.1 Temporal Reasoning Philosophy.
|
| 520 |
|
|
|a The book first describes motivations for automating process fault analysis in detail and subsequently discusses MOME and its associated Fuzzy logic algorithm. Other chapters cover various topics related to process fault Analysis including the need for augmenting process fault analysis with trend analysis of the various process sensor measurements and Key Performance Indicators (KPIs). Also included is a brief review of a number of the other various possible diagnostic strategies used to automate process fault analysis as well as their limitations.
|
| 504 |
|
|
|a Includes bibliographical references and index.
|
| 588 |
0 |
|
|a Print version record.
|
| 650 |
|
0 |
|a Chemical process control
|x Data processing.
|
| 650 |
|
0 |
|a Fault location (Engineering)
|x Data processing.
|
| 650 |
|
7 |
|a Chemical process control
|x Data processing.
|2 fast
|0 (OCoLC)fst00853148
|
| 650 |
|
7 |
|a Fault location (Engineering)
|x Data processing.
|2 fast
|0 (OCoLC)fst00921984
|
| 655 |
|
4 |
|a Electronic books.
|
| 700 |
1 |
|
|a Chester, Daniel L.
|
| 776 |
0 |
8 |
|i Print version:
|a Fickelscherer, Richard J.
|t Optimal Automated Process Fault Analysis.
|d New York : Wiley, ©2012
|z 9781118372319
|
| 856 |
4 |
0 |
|u https://doi.org/10.1002/9781118481950
|z Full Text via HEAL-Link
|
| 994 |
|
|
|a 92
|b DG1
|
