LEADER 10637nam 22004453 450 001 9910840961503321 005 20240202080215.0 010 $a1-119-82592-X 010 $a1-119-82590-3 035 $a(CKB)30098036800041 035 $a(MiAaPQ)EBC31093696 035 $a(Au-PeEL)EBL31093696 035 $a(EXLCZ)9930098036800041 100 $a20240202d2024 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence in Process Fault Diagnosis $eMethods for Plant Surveillance 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2024. 210 4$d©2024. 215 $a1 online resource (436 pages) 311 08$a9781119825890 327 $aCOVER -- TITLE PAGE -- COPYRIGHT PAGE -- DEDICATION PAGE -- CONTENTS -- LIST OF CONTRIBUTORS -- FOREWORD -- PREFACE -- ACKNOWLEDGMENTS -- CHAPTER 1 MOTIVATIONS FOR AUTOMATING PROCESS FAULT ANALYSIS -- OVERVIEW -- CHAPTER HIGHLIGHTS -- 1.1 INTRODUCTION -- 1.2 THE CHANGING ROLE OF THE PROCESS OPERATORS IN PLANT OPERATIONS -- 1.3 TRADITIONAL METHODS FOR PERFORMING PROCESS FAULT MANAGEMENT -- 1.4 LIMITATIONS OF HUMAN OPERATORS IN PERFORMING PROCESS FAULT MANAGEMENT -- 1.5 THE ROLE OF AUTOMATED PROCESS FAULT ANALYSIS -- REFERENCES -- CHAPTER 2 VARIOUS PROCESS FAULT DIAGNOSTIC METHODOLOGIES -- OVERVIEW -- CHAPTER HIGHLIGHTS -- 2.1 INTRODUCTION -- 2.2 VARIOUS ALTERNATIVE DIAGNOSTIC STRATEGIES OVERVIEW -- 2.2.1 Fault Tree Analysis -- 2.2.2 Alarm Analysis -- 2.2.3 Decision Tables -- 2.2.4 Sign-Directed Graphs -- 2.2.5 History-Based Statistical Methods -- 2.2.6 Diagnostic Strategies Based upon Qualitative Models -- 2.2.7 Diagnostic Strategies Based upon Quantitative Models -- 2.2.8 Artificial Neural Network Strategies -- 2.2.9 Artificial Immune System Strategies -- 2.2.10 Knowledge-Based System Strategies -- 2.2.11 Role of the Process Operators in Automated Fault Detection and Diagnosis -- 2.3 DIAGNOSTIC METHODOLOGY CHOICE CONCLUSIONS -- REFERENCES -- CHAPTER 2.A FAILURE MODES AND EFFECTS ANALYSIS -- 2.A.1 Introduction -- 2.A.2 FMEA Procedure -- 2.A.3 Conclusion -- CHAPTER 3 ALARM MANAGEMENT AND FAULT DETECTION -- CHAPTER HIGHLIGHTS -- ABBREVIATIONS USED -- 3.1 INTRODUCTION -- 3.2 APPLICABLE DEFINITIONS AND GUIDELINES -- 3.3 THE ALARM MANAGEMENT LIFE CYCLE -- 3.3.1 Introduction -- 3.3.2 Life Cycle Model -- 3.3.3 Alarm Philosophy -- 3.3.4 Alarm Identification -- 3.3.5 Alarm Rationalization -- 3.3.6 Alarm Design -- 3.3.7 Implementation -- 3.3.8 Operation -- 3.3.9 Maintenance -- 3.3.10 Monitoring and Assessment -- 3.3.11 Management of Change -- 3.3.12 Audit. 327 $a3.4 GENERATION OF DIAGNOSTIC INFORMATION -- 3.4.1 Introduction -- 3.4.2 As Part of the Basic Process Control System -- 3.4.3 As a Separate Application -- 3.5 PRESENTATION OF THE DIAGNOSTIC INFORMATION -- 3.5.1 Introduction -- 3.5.2 As Part of the Alarm Text -- 3.5.3 As a Link to the Diagnostic Application -- 3.5.4 As an Indication in the HMI -- 3.6 INFORMATION RATES -- 3.6.1 Introduction -- 3.6.2 Nuisance Alarms -- REFERENCES -- CHAPTER 4 OPERATOR PERFORMANCE: SIMULATION AND AUTOMATION -- CHAPTER HIGHLIGHTS -- 4.1 BACKGROUND -- 4.2 AUTOMATION -- 4.2.1 Smart Alarming -- 4.2.2 Safe Park Applications -- 4.3 SIMULATION -- 4.4 RESEARCH -- 4.4.1 Method -- 4.4.2 Testing and Results -- 4.4.3 Operator Performance -- 4.4.4 Implications -- 4.5 AI INTEGRATION -- 4.5.1 Pattern Recognition -- 4.5.2 Training -- 4.6 CASE STUDY: TURBO EXPANDERS OVER-SPEED -- 4.7 HUMAN-CENTERED AI -- 4.7.1 Case Study: Boeing 737 MAX -- 4.7.2 AI Mental Models -- REFERENCES -- CHAPTER 5 AI AND ALARM ANALYTICS FOR FAILURE ANALYSIS AND PREVENTION -- OVERVIEW -- 5.1 INTRODUCTION -- 5.2 POST-ALARM ASSESSMENT AND ANALYSIS -- 5.2.1 Alarm Configuration Database -- 5.3 REAL-TIME ALARM ACTIVITY DATABASE AND OPERATOR ACTION JOURNAL -- 5.4 PRE-ALARM ASSESSMENT AND ANALYSIS -- 5.5 UTILIZING ALARM ASSESSMENT INFORMATION -- 5.6 EXAMINING THE ALARM SYSTEM TO RESOLVE FAILURES ON A WIDER SCALE -- 5.6.1 Sequence of Events (SOE) Module -- 5.6.2 Use of First Principles to Determine Likely Root Causes -- 5.6.3 Use of Simple Data Analytics to Identify Redundant/Repetitive Alarms -- 5.6.4 Use of Data Analytics to Identify Problem Areas with Upsets Related to Transitions, out of Service, and out of Suppression States -- 5.6.5 Use of Data Analytics to Identify Problem Areas with Chronic Alarm Shelving -- 5.7 EMERGING METHODS OF ALARM ANALYSIS -- 5.7.1 Use of Advanced Modeling Methods to Determine Remediation. 327 $a5.7.2 Use of Automated Machine Learning to Determine Causes and Assess Interventions -- 5.8 DEEP REINFORCEMENT LEARNING FOR ALARMING AND FAILURE ASSESSMENT -- 5.9 SOME TYPICAL AI AND MACHINE LEARNING EXAMPLES FOR FURTHER STUDY -- 5.9.1 Boolean Logic Tables -- 5.9.2 Statistical Regression and Variance -- 5.9.3 Artificial Neural Networks (ANNs) -- 5.9.4 Expert Systems -- 5.9.5 Sensitivity Analysis -- 5.9.6 Fuzzy Logic -- 5.9.7 Bayesian Networks -- 5.9.8 Genetic Algorithms -- 5.9.9 SmartSignal, PRiSM (AVEVA), and PPCL -- 5.9.10 Control System Effectiveness Study -- 5.10 WRAP-UP -- CHAPTER 5.A PROCESS STATE TRANSITION LOGIC EMPLOYED BY THE ORIGINAL FMC FALCONEER KBS -- 5.A.1 INTRODUCTION -- 5.A.2 POSSIBLE PROCESS OPERATING STATES -- 5.A.3 SIGNIFICANCE OF PROCESS STATE IDENTIFICATION AND TRANSITION DETECTION -- 5.A.4 METHODOLOGY FOR DETERMINING PROCESS STATE IDENTIFICATION -- 5.A.4.1 Present Value States of all Key Sensor Data -- 5.A.4.2 Predicted Next Value States of all Key Sensor Data -- 5.A.5 PROCESS STATE IDENTIFICATION AND TRANSITION LOGIC PSEUDO-CODE -- 5.A.5.1 Attributes of the Current Data Vector -- 5.A.5.2 Method that is Applied to Each Updated Data Vector -- 5.A.6 SUMMARY -- APPENDIX 5.A REFERENCES -- CHAPTER 5.B PROCESS STATE TRANSITION LOGIC AND ITS ROUTINE USE IN FALCONEER? IV -- 5.B.1 TEMPORAL REASONING PHILOSOPHY -- 5.B.2 INTRODUCTION -- 5.B.3 STATE IDENTIFICATION ANALYSIS CURRENTLY USED IN FALCONEER? IV -- 5.B.4 STATE IDENTIFICATION ANALYSIS SUMMARY -- APPENDIX 5.B REFERENCES -- CHAPTER 6 PROCESS FAULT DETECTION BASED ON TIME-EXPLICIT KIVIAT DIAGRAM -- OVERVIEW -- CHAPTER HIGHLIGHTS -- 6.1 INTRODUCTION -- 6.2 TIME-EXPLICIT KIVIAT DIAGRAM -- 6.3 FAULT DETECTION BASED ON THE TIME-EXPLICIT KIVIAT DIAGRAM -- 6.4 CONTINUOUS PROCESSES -- 6.5 BATCH PROCESSES -- 6.6 PERIODIC PROCESSES -- 6.7 CASE STUDIES -- 6.8 CONTINUOUS PROCESSES. 327 $a6.9 BATCH PROCESSES -- 6.10 PERIODIC PROCESSES -- 6.11 CONCLUSIONS -- ACKNOWLEDGMENT -- REFERENCES -- ACKNOWLEDGMENTS -- APPENDIX 6.A REFERENCES -- CHAPTER 6.A VIRTUAL STATISTICAL PROCESS CONTROL ANALYSIS -- 6.A.1 OVERVIEW -- 6.A.2 INTRODUCTION -- 6.A.3 EWMA CALCULATIONS AND SPECIFIC VIRTUAL SPC ANALYSIS CONFIGURATIONS -- 6.A.3.1 Controlled Variables -- 6.A.3.2 Uncontrolled Variables and Performance Equation Variables -- 6.A.4 VIRTUAL SPC ALARM TRIGGER SUMMARY -- 6.A.5 VIRTUAL SPC ANALYSIS CONCLUSIONS -- ACKNOWLEDGMENTS -- APPENDIX 6.A REFERENCES -- CHAPTER 7 SMART MANUFACTURING AND REAL-TIME CHEMICAL PROCESS HEALTH MONITORING AND DIAGNOSTIC LOCALIZATION -- CHAPTER HIGHLIGHTS -- 7.1 INTRODUCTION TO PROCESS OPERATIONAL HEALTH MODELING -- 7.2 DIAGNOSTIC LOCALIZATION - KEY CONCEPTS -- 7.2.1 Qualitative Modeling and Symptomaticand Topographic Search -- 7.2.2 Functional Representation as a Qualitative Modeling Construct -- 7.2.3 Causal Link Assessment for Combined Topographical and Symptomatic Assessment -- 7.3 TIME -- 7.3.1 Discretization and Single Time-Step Analysis -- 7.3.2 Dynamics in an Individual Functional Representation -- 7.3.3 Time Window and Feature Extraction -- 7.4 THE WORKFLOW OF DIAGNOSTIC LOCALIZATION -- 7.5 DL-CLA USE CASE IMPLEMENTATION: NOVA CHEMICAL ETHYLENE SPLITTER -- 7.5.1 CPP Generation -- 7.5.2 CPP Interpretation -- 7.5.3 Diagnostic Localization -- 7.6 ANALYZING POTENTIAL MALFUNCTIONS OVER TIME -- 7.7 ANALYSIS OF VARIOUS OPERATIONAL SCENARIOS -- 7.7.1 Event Manifestation, Sensor Reliability/Sensor Malfunctions -- 7.7.2 Hypothetical and Function-Only Devices -- 7.7.3 Unaccounted Malfunctions, Graceful Degradation, Multiple Malfunctions, Etc. -- 7.7.4 Sensor Availability and Reliability -- 7.7.5 Process Complexity -- 7.8 DL-CLA INTEGRATION WITH SMART MANUFACTURING (SM) -- 7.9 AN FR MODEL LIBRARY -- 7.9.1 Reusing FR Device Models. 327 $a7.9.2 Complex FR Models -- 7.9.3 Analysis and Results -- 7.10 CONCLUSIONS -- REFERENCES -- CHAPTER 8 OPTIMAL QUANTITATIVE MODEL-BASED PROCESS FAULT DIAGNOSIS -- OVERVIEW -- CHAPTER HIGHLIGHTS -- 8.1 INTRODUCTION -- 8.2 PROCESS FAULT ANALYSIS CONCEPT TERMINOLOGY -- 8.3 MOME QUANTITATIVE MODELS OVERVIEW -- 8.4 MOME QUANTITATIVE MODEL DIAGNOSTIC STRATEGY -- 8.5 MOME SV& -- PFA DIAGNOSTIC RULES' LOGIC COMPILER MOTIVATIONS -- 8.6 MOME FUZZY LOGIC ALGORITHM OVERVIEW -- 8.6.1 MOME Fuzzy Logic Algorithm Details -- 8.6.2 Single Fault Fuzzy Logic SV& -- PFA Diagnostic Rule -- 8.6.3 Multiple Fault Fuzzy Logic SV& -- PFA Diagnostic Rule -- 8.7 SUMMARY OF THE MOME DIAGNOSTIC STRATEGY -- 8.8 ACTUAL PROCESS SYSTEM KBS APPLICATION PERFORMANCE RESULTS -- 8.9 CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- CHAPTER 8.A FALCONEERTM IV FUZZY LOGIC ALGORITHM PSEUDO-CODE -- 8.A.1 Introduction -- 8.A.2 Single and Non-Interactive Multiple Faults -- 8.A.3 Pairs of Interactive Multiple Faults -- 8.A.4 Summary -- CHAPTER 8.B MOME CONCLUSIONS -- 8.B.1 Overview -- 8.B.2 Summary of the Mome Diagnostic Strategy -- 8.B.3 Falconeer? IV KBS Application Project Procedure -- 8.B.4 Optimal Automated Process Fault Analysis Conclusions -- CHAPTER 9 FAULT DETECTION USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING -- CHAPTER HIGHLIGHTS -- ABBREVIATIONS USED -- 9.1 INTRODUCTION -- 9.2 ARTIFICIAL INTELLIGENCE -- 9.3 MACHINE LEARNING -- 9.4 ENGINEERED FEATURES -- 9.4.1 Fast Fourier Transformation and Signal Processing -- 9.4.2 Principal Component Analysis -- 9.5 MACHINE LEARNING ALGORITHMS -- 9.5.1 Decision Trees and Ensemble Trees -- 9.5.2 Artificial Neural Networks -- 9.5.3 Bayesian Networks -- 9.5.4 High-Density Clustering -- 9.5.5 Large Language Models and the Future AI-Driven Factories -- REFERENCES -- CHAPTER 10 KNOWLEDGE-BASED SYSTEMS -- CHAPTER HIGHLIGHTS. 327 $aABBREVIATIONS USED. 676 $a670.427 700 $aFickelscherer$b Richard J$01727860 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910840961503321 996 $aArtificial Intelligence in Process Fault Diagnosis$94135699 997 $aUNINA LEADER 02976nam 22004935 450 001 9910255030003321 005 20240628121258.0 010 $a9781137313034 010 $a113731303X 024 7 $a10.1057/978-1-137-31303-4 035 $a(CKB)3710000001363102 035 $a(DE-He213)978-1-137-31303-4 035 $a(MiAaPQ)EBC4856765 035 $a(Perlego)3505549 035 $a(EXLCZ)993710000001363102 100 $a20170509d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultivariate Modelling of Non-Stationary Economic Time Series /$fby John Hunter, Simon P. Burke, Alessandra Canepa 205 $a2nd ed. 2017. 210 1$aLondon :$cPalgrave Macmillan UK :$cImprint: Palgrave Macmillan,$d2017. 215 $a1 online resource (XIII, 502 p.) 225 1 $aPalgrave Texts in Econometrics,$x2662-6608 311 08$a9780230243309 311 08$a0230243304 320 $aIncludes bibliographical references and index. 327 $aChapter 1. Introduction: Time Series, Common Trends and Equilibrium -- Chapter 2. Multivariate Time Series -- Chapter 3. Cointegration -- Chapter 4. Testing for Cointegration: Under Standard and Non-Standard Conditions -- Chapter 5. Structure and Evaluation -- Chapter 6. Testing in VECMs with Small Sample -- Chapter 7. Heteroscedasticity and Multivariate Volatility -- Chapter 8. Models with Alternative Orders of Integration -- Chapter 9. The Structural Analysis of Time Series. 330 $aThis book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models. The authors provide a detailed and extensive study of impulse responses and forecasting in the stationary and non-stationary context, considering small sample correction, volatility and the impact of different orders of integration. Models with expectations are considered along with alternate methods such as Singular Spectrum Analysis (SSA), the Kalman Filter and Structural Time Series, all in relation to cointegration. Using single equations methods to develop topics, and as examples of the notion of cointegration, Burke, Hunter, and Canepa provide direction and guidance to the now vast literature facing students and graduate economists. 410 0$aPalgrave Texts in Econometrics,$x2662-6608 606 $aEconometrics 606 $aEconometrics 615 0$aEconometrics. 615 14$aEconometrics. 676 $a330.015195 700 $aHunter$b John$4aut$4http://id.loc.gov/vocabulary/relators/aut$0424730 702 $aBurke$b Simon P$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aCanepa$b Alessandra$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910255030003321 996 $aMultivariate Modelling of Non-Stationary Economic Time Series$91942939 997 $aUNINA