{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":22,"n_cols":5,"rows_kept":22,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[11,2,11,3],[13,2,13,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910830640803321"]
[null,null,"Autore",null,"Fickelscherer Richard J"]
[null,null,"Titolo",null,"Artificial Intelligence in Process Fault Diagnosis : Methods for Plant Surveillance"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"1-119-82592-X"]
[{"$nulls":4},"1-119-82590-3"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (436 pages)"]
[null,null,"Disciplina",null,"670.427"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"COVER -- 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 "]
[{"$nulls":4},"DEFINITIONS AND GUIDELINES -- 3.3 THE ALARM MANAGEMENT LIFE CYCLE -- 3.3.1 Introduction -- 3.3.2 Life Cycle Model -- 3.3.3 Alarm Philosophy -- 3.3.4 Alarm Identification -- 3.3.5 Alarm Rationalization -- 3.3.6 Alarm Design -- 3.3.7 Implementation -- 3.3.8 Operation -- 3.3.9 Maintenance -- 3.3.10 Monitoring and Assessment -- 3.3.11 Management of Change -- 3.3.12 Audit."]
[{"$nulls":4},"3.4 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."]
[{"$nulls":4},"5.7.2 Use of Automated Machine Learning to Determine Causes and Assess Interventions -- 5.8 DEEP REINFORCEMENT LEARNING FOR ALARMING AND FAILURE ASSESSMENT -- 5.9 SOME TYPICAL AI AND MACHINE LEARNING EXAMPLES FOR FURTHER STUDY -- 5.9.1 Boolean Logic Tables -- 5.9.2 Statistical Regression and Variance -- 5.9.3 Artificial Neural Networks (ANNs) -- 5.9.4 Expert Systems -- 5.9.5 Sensitivity Analysis -- 5.9.6 Fuzzy Logic -- 5.9.7 Bayesian Networks -- 5.9.8 Genetic Algorithms -- 5.9.9 SmartSignal, PRiSM (AVEVA), and PPCL -- 5.9.10 Control System Effectiveness Study -- 5.10 WRAP-UP -- CHAPTER 5.A PROCESS STATE TRANSITION LOGIC EMPLOYED BY THE ORIGINAL FMC FALCONEER KBS -- 5.A.1 INTRODUCTION -- 5.A.2 POSSIBLE PROCESS OPERATING STATES -- 5.A.3 SIGNIFICANCE OF PROCESS STATE IDENTIFICATION AND TRANSITION DETECTION -- 5.A.4 METHODOLOGY FOR DETERMINING PROCESS STATE IDENTIFICATION -- 5.A.4.1 Present Value States of all Key Sensor Data -- 5.A.4.2 Predicted Next Value States of all Key Sensor Data -- 5.A.5 PROCESS STATE IDENTIFICATION AND TRANSITION LOGIC PSEUDO-CODE -- 5.A.5.1 Attributes of the Current Data Vector -- 5.A.5.2 Method that is Applied to Each Updated Data Vector -- 5.A.6 SUMMARY -- APPENDIX 5.A REFERENCES -- CHAPTER 5.B PROCESS STATE TRANSITION LOGIC AND ITS ROUTINE USE IN FALCONEER™ IV -- "]
[{"$nulls":4},"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."]
[{"$nulls":4},"6.9 BATCH PROCESSES -- 6.10 PERIODIC PROCESSES -- 6.11 CONCLUSIONS -- ACKNOWLEDGMENT -- REFERENCES -- ACKNOWLEDGMENTS -- APPENDIX 6.A REFERENCES -- CHAPTER 6.A VIRTUAL STATISTICAL PROCESS CONTROL ANALYSIS -- 6.A.1 OVERVIEW -- 6.A.2 INTRODUCTION -- 6.A.3 EWMA CALCULATIONS AND SPECIFIC VIRTUAL SPC ANALYSIS CONFIGURATIONS -- 6.A.3.1 Controlled Variables -- 6.A.3.2 Uncontrolled Variables and Performance Equation Variables -- 6.A.4 VIRTUAL SPC ALARM TRIGGER SUMMARY -- 6.A.5 VIRTUAL SPC ANALYSIS CONCLUSIONS -- ACKNOWLEDGMENTS -- APPENDIX 6.A REFERENCES -- CHAPTER 7 SMART MANUFACTURING AND REAL-TIME CHEMICAL PROCESS HEALTH MONITORING AND DIAGNOSTIC LOCALIZATION -- CHAPTER HIGHLIGHTS -- 7.1 INTRODUCTION TO PROCESS OPERATIONAL HEALTH MODELING -- 7.2 DIAGNOSTIC LOCALIZATION - KEY CONCEPTS -- 7.2.1 Qualitative Modeling and Symptomaticand Topographic Search -- 7.2.2 Functional Representation as a Qualitative Modeling Construct -- 7.2.3 Causal Link Assessment for Combined Topographical and Symptomatic Assessment -- 7.3 TIME -- 7.3.1 Discretization and Single Time-Step Analysis -- 7.3.2 Dynamics in an Individual Functional Representation -- 7.3.3 Time Window and Feature Extraction -- 7.4 THE WORKFLOW OF DIAGNOSTIC LOCALIZATION -- 7.5 DL-CLA USE CASE IMPLEMENTATION: NOVA CHEMICAL ETHYLENE SPLITTER -- 7.5.1 CPP Generation -- 7.5.2 CPP Interpretation -- 7.5.3 Diagnostic Localization -- 7.6 ANALYZING POTENTIAL MALFUNCTIONS OVER TIME -- 7.7 ANALYSIS OF VARIOUS OPERATIONAL SCENARIOS -- 7.7.1 Event Manifestation, Sensor Reliability/Sensor Malfunctions -- 7.7.2 Hypothetical and Function-Only Devices -- 7.7.3 Unaccounted Malfunctions, Graceful Degradation, Multiple Malfunctions, Etc. -- 7.7.4 Sensor Availability and Reliability -- 7.7.5 Process Complexity -- 7.8 DL-CLA INTEGRATION WITH SMART MANUFACTURING (SM) -- 7.9 AN FR MODEL LIBRARY -- 7.9.1 Reusing FR Device Models."]
[{"$nulls":4},"7.9.2 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&amp -- 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&amp -- PFA Diagnostic Rule -- 8.6.3 Multiple Fault Fuzzy Logic SV&amp -- 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 "]
[{"$nulls":4},"MOME CONCLUSIONS -- 8.B.1 Overview -- 8.B.2 Summary of the Mome Diagnostic Strategy -- 8.B.3 Falconeer™ IV KBS Application Project Procedure -- 8.B.4 Optimal Automated Process Fault Analysis Conclusions -- CHAPTER 9 FAULT DETECTION USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING -- CHAPTER HIGHLIGHTS -- ABBREVIATIONS USED -- 9.1 INTRODUCTION -- 9.2 ARTIFICIAL INTELLIGENCE -- 9.3 MACHINE LEARNING -- 9.4 ENGINEERED FEATURES -- 9.4.1 Fast Fourier Transformation and Signal Processing -- 9.4.2 Principal Component Analysis -- 9.5 MACHINE LEARNING ALGORITHMS -- 9.5.1 Decision Trees and Ensemble Trees -- 9.5.2 Artificial Neural Networks -- 9.5.3 Bayesian Networks -- 9.5.4 High-Density Clustering -- 9.5.5 Large Language Models and the Future AI-Driven Factories -- REFERENCES -- CHAPTER 10 KNOWLEDGE-BASED SYSTEMS -- CHAPTER HIGHLIGHTS."]
[{"$nulls":4},"ABBREVIATIONS USED."]