Digital Maintenance Management: Guiding Digital Transformation in Maintenance
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing AG
2022
|
| Schriftenreihe: | Springer Series in Reliability Engineering Series
|
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (183 Seiten) |
| ISBN: | 9783030976606 |
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| 245 | 1 | 0 | |a Digital Maintenance Management |b Guiding Digital Transformation in Maintenance |
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| 505 | 8 | |a Intro -- Foreword -- Acknowledgements -- Overview -- Relevant Topics -- Structure of the Book -- Contents -- Part I Fundamental Concepts to Understand Digital Transformation Benefits -- 1 Benefits of Digital Transformation for Maintenance Management Systems. Market Trends -- 1.1 Introduction -- 1.2 Key Managerial Areas That Can Be Digitally Supported -- 1.3 Emerging Asset Management Systems APM & -- AIP -- 1.4 The New Am Systems/Platforms Architecture -- References -- 2 A Review of New Digital Technologies Impacting Maintenance Management -- 2.1 Introduction -- 2.2 The 'Internet of Things' (IOT) Technologies -- 2.3 Big Data -- 2.4 Predictive Analytics -- 2.5 Digital Twin Simulations -- 2.6 Augmented Reality -- 2.7 Business Intelligence and Data Visualization Tools -- 2.8 Complementary Technologies -- References -- Part II The Pillars for Digital Maintenance Management Excellence -- 3 Driving the Introduction of Digital Technologies to Enhance the Maintenance Management Process and Framework -- 3.1 Introduction -- 3.2 Starting from the Mm Process and Framework -- 3.3 New Opportunities with Data Availability and Analitycs -- 3.4 Visualizing the New Digital Maintenance Management Framework -- 3.5 The Need for a Business Asset Data Model -- References -- 4 The Definition of the Asset Data Model -- 4.1 Introduction -- 4.2 Methodology Proof of Concept: Data Model for a Criticality Analysis App -- References -- Part III Developments in Advanced Maintenance Management Apps -- 5 Advanced Asset Performance Management (APM) and Asset Investment Planning (AIP) Systems -- 5.1 The APM Toolbox Approach to Asset Maintenance -- 5.1.1 GE Digital's Predix Asset Performance Management -- 5.1.2 SAP APM Solution -- 5.1.3 Siemens APM Products -- 5.1.4 IBM APM Portfolio -- 5.2 The Asset Investment Planning (AIP) Solutions -- 5.2.1 Copperleaf Decision Analytics | |
| 505 | 8 | |a 5.2.2 The Cosmo Tech Hybrid Approach -- References -- Part IV Technical Challenges in the Use of Digital Technologies for Maintenance -- 6 The Non-ergodicity of Assets -- 6.1 Introduction to the Problem -- 6.2 A Case Study for Train Bearings -- 6.2.1 Axel Bearing Position Model (ML Model 1) -- 6.2.2 Train Bearing Position Model (ML Model 2) -- 6.2.3 Fleet Bearing Position Model (ML Model 3) -- 6.2.4 Testing and Validating the Models with Different Data -- 6.2.5 Conclusions of the Case Study -- References -- 7 The Curse of Dimensionality -- 7.1 Introduction to the Problem -- 7.2 Feature Selection Methods -- 7.3 The Use of Classifiers to Identify Failure Modes -- 7.4 Case Study: Cryogenic Reciprocating Compressors -- 7.4.1 Introduction to the Industrial Process -- 7.4.2 The Design of a CBM Strategy for the Compressors -- 7.4.3 The Selection of Features -- 7.4.4 The Results of the Analysis -- 7.4.5 Discussion of Results -- 7.5 Conclusions -- References -- 8 The Dynamic Measurement of Failure Risk -- 8.1 Introduction to the Problem -- 8.2 Dynamic Risk Level Assessment in Literature -- 8.3 Risk Management (RM), Assessment (RA) and Analysis (RAn) -- 8.4 Introduction to Dynamic Risk Assessment (DRA) -- 8.5 A Failure Mode Dra Method -- 8.6 Final Remarks -- References -- 9 The Dynamic Scheduling of Maintenance -- 9.1 Introduction -- 9.2 CBM Applied to Multi-component Systems -- 9.3 Fleet Maintenance Scheduling -- 9.4 Case Study. Scheduling Train Components' CBM -- 9.4.1 Introduction to the Problem -- 9.4.2 Building the Continuous Time Simulation Model -- 9.4.3 Implementation of a Practical Model in Vensim -- 9.4.4 Conclusions of the Case Study -- 9.5 Conclusions -- References -- Part V Emerging Data-Driven Processes for Asset Performance Management (APM) -- 10 Techniques for Anomalies Detection -- 10.1 Introduction | |
| 505 | 8 | |a 10.2 Anomalies Detection in Train Bearings -- 10.3 Baseline Analytics Selection -- 10.4 Anomalies Detection Model Implementation -- 10.5 Discussion of Results -- References -- 11 Asset Condition and Operations Efficiency -- 11.1 Introduction -- 11.2 Case Study. Efficiency in Renewable Energy Generation -- 11.2.1 Reviewing the Use of Predictive Techniques -- 11.2.2 Case Study Development -- 11.2.3 Case Study Conclusions -- 11.3 Case Study. LNG Storage Tank Pump Operation Analysis -- 11.3.1 Using Data Mining Together with Predictive Analytics -- 11.3.2 An Introduction to the DM AR Technique -- 11.3.3 The Emerging APM and Reliability Asessment Process -- 11.3.4 Presenting the Process Results -- 11.3.5 Case Study Conclusions -- References -- Part VI Emerging Data-Driven Processes for Asset Investment Planning (AIP) -- 12 Asset Health Indexing and Life Cycle Costing -- 12.1 Introduction -- 12.2 AHI Modelling Methodology -- 12.3 Life Cycle Costing Modelling Methodology -- 12.4 Case Study. Lng Regassification Plant -- 12.4.1 Asset Expense Profile Based on Its Health Index -- 12.4.2 Industrial Case Results and Findings -- References | |
| 650 | 4 | |a Plant maintenance-Data processing | |
| 650 | 4 | |a Plant maintenance-Management | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Crespo Márquez, Adolfo |t Digital Maintenance Management |d Cham : Springer International Publishing AG,c2022 |z 978-3-030-97659-0 |
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Datensatz im Suchindex
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| author | Crespo Márquez, Adolfo |
| author_GND | (DE-588)1234106639 |
| author_facet | Crespo Márquez, Adolfo |
| author_role | aut |
| author_sort | Crespo Márquez, Adolfo |
| author_variant | m a c ma mac |
| building | Verbundindex |
| bvnumber | BV048921005 |
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| contents | Intro -- Foreword -- Acknowledgements -- Overview -- Relevant Topics -- Structure of the Book -- Contents -- Part I Fundamental Concepts to Understand Digital Transformation Benefits -- 1 Benefits of Digital Transformation for Maintenance Management Systems. Market Trends -- 1.1 Introduction -- 1.2 Key Managerial Areas That Can Be Digitally Supported -- 1.3 Emerging Asset Management Systems APM & -- AIP -- 1.4 The New Am Systems/Platforms Architecture -- References -- 2 A Review of New Digital Technologies Impacting Maintenance Management -- 2.1 Introduction -- 2.2 The 'Internet of Things' (IOT) Technologies -- 2.3 Big Data -- 2.4 Predictive Analytics -- 2.5 Digital Twin Simulations -- 2.6 Augmented Reality -- 2.7 Business Intelligence and Data Visualization Tools -- 2.8 Complementary Technologies -- References -- Part II The Pillars for Digital Maintenance Management Excellence -- 3 Driving the Introduction of Digital Technologies to Enhance the Maintenance Management Process and Framework -- 3.1 Introduction -- 3.2 Starting from the Mm Process and Framework -- 3.3 New Opportunities with Data Availability and Analitycs -- 3.4 Visualizing the New Digital Maintenance Management Framework -- 3.5 The Need for a Business Asset Data Model -- References -- 4 The Definition of the Asset Data Model -- 4.1 Introduction -- 4.2 Methodology Proof of Concept: Data Model for a Criticality Analysis App -- References -- Part III Developments in Advanced Maintenance Management Apps -- 5 Advanced Asset Performance Management (APM) and Asset Investment Planning (AIP) Systems -- 5.1 The APM Toolbox Approach to Asset Maintenance -- 5.1.1 GE Digital's Predix Asset Performance Management -- 5.1.2 SAP APM Solution -- 5.1.3 Siemens APM Products -- 5.1.4 IBM APM Portfolio -- 5.2 The Asset Investment Planning (AIP) Solutions -- 5.2.1 Copperleaf Decision Analytics 5.2.2 The Cosmo Tech Hybrid Approach -- References -- Part IV Technical Challenges in the Use of Digital Technologies for Maintenance -- 6 The Non-ergodicity of Assets -- 6.1 Introduction to the Problem -- 6.2 A Case Study for Train Bearings -- 6.2.1 Axel Bearing Position Model (ML Model 1) -- 6.2.2 Train Bearing Position Model (ML Model 2) -- 6.2.3 Fleet Bearing Position Model (ML Model 3) -- 6.2.4 Testing and Validating the Models with Different Data -- 6.2.5 Conclusions of the Case Study -- References -- 7 The Curse of Dimensionality -- 7.1 Introduction to the Problem -- 7.2 Feature Selection Methods -- 7.3 The Use of Classifiers to Identify Failure Modes -- 7.4 Case Study: Cryogenic Reciprocating Compressors -- 7.4.1 Introduction to the Industrial Process -- 7.4.2 The Design of a CBM Strategy for the Compressors -- 7.4.3 The Selection of Features -- 7.4.4 The Results of the Analysis -- 7.4.5 Discussion of Results -- 7.5 Conclusions -- References -- 8 The Dynamic Measurement of Failure Risk -- 8.1 Introduction to the Problem -- 8.2 Dynamic Risk Level Assessment in Literature -- 8.3 Risk Management (RM), Assessment (RA) and Analysis (RAn) -- 8.4 Introduction to Dynamic Risk Assessment (DRA) -- 8.5 A Failure Mode Dra Method -- 8.6 Final Remarks -- References -- 9 The Dynamic Scheduling of Maintenance -- 9.1 Introduction -- 9.2 CBM Applied to Multi-component Systems -- 9.3 Fleet Maintenance Scheduling -- 9.4 Case Study. Scheduling Train Components' CBM -- 9.4.1 Introduction to the Problem -- 9.4.2 Building the Continuous Time Simulation Model -- 9.4.3 Implementation of a Practical Model in Vensim -- 9.4.4 Conclusions of the Case Study -- 9.5 Conclusions -- References -- Part V Emerging Data-Driven Processes for Asset Performance Management (APM) -- 10 Techniques for Anomalies Detection -- 10.1 Introduction 10.2 Anomalies Detection in Train Bearings -- 10.3 Baseline Analytics Selection -- 10.4 Anomalies Detection Model Implementation -- 10.5 Discussion of Results -- References -- 11 Asset Condition and Operations Efficiency -- 11.1 Introduction -- 11.2 Case Study. Efficiency in Renewable Energy Generation -- 11.2.1 Reviewing the Use of Predictive Techniques -- 11.2.2 Case Study Development -- 11.2.3 Case Study Conclusions -- 11.3 Case Study. LNG Storage Tank Pump Operation Analysis -- 11.3.1 Using Data Mining Together with Predictive Analytics -- 11.3.2 An Introduction to the DM AR Technique -- 11.3.3 The Emerging APM and Reliability Asessment Process -- 11.3.4 Presenting the Process Results -- 11.3.5 Case Study Conclusions -- References -- Part VI Emerging Data-Driven Processes for Asset Investment Planning (AIP) -- 12 Asset Health Indexing and Life Cycle Costing -- 12.1 Introduction -- 12.2 AHI Modelling Methodology -- 12.3 Life Cycle Costing Modelling Methodology -- 12.4 Case Study. Lng Regassification Plant -- 12.4.1 Asset Expense Profile Based on Its Health Index -- 12.4.2 Industrial Case Results and Findings -- References |
| ctrlnum | (ZDB-30-PQE)EBC6922462 (ZDB-30-PAD)EBC6922462 (ZDB-89-EBL)EBL6922462 (OCoLC)1303574602 (DE-599)BVBBV048921005 |
| dewey-full | 658.202 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.202 |
| dewey-search | 658.202 |
| dewey-sort | 3658.202 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Electronic eBook |
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| id | DE-604.BV048921005 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:55:16Z |
| indexdate | 2024-07-20T05:07:47Z |
| institution | BVB |
| isbn | 9783030976606 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034185096 |
| oclc_num | 1303574602 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (183 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer International Publishing AG |
| record_format | marc |
| series2 | Springer Series in Reliability Engineering Series |
| spelling | Crespo Márquez, Adolfo Verfasser (DE-588)1234106639 aut Digital Maintenance Management Guiding Digital Transformation in Maintenance Cham Springer International Publishing AG 2022 ©2022 1 Online-Ressource (183 Seiten) txt rdacontent c rdamedia cr rdacarrier Springer Series in Reliability Engineering Series Description based on publisher supplied metadata and other sources Intro -- Foreword -- Acknowledgements -- Overview -- Relevant Topics -- Structure of the Book -- Contents -- Part I Fundamental Concepts to Understand Digital Transformation Benefits -- 1 Benefits of Digital Transformation for Maintenance Management Systems. Market Trends -- 1.1 Introduction -- 1.2 Key Managerial Areas That Can Be Digitally Supported -- 1.3 Emerging Asset Management Systems APM & -- AIP -- 1.4 The New Am Systems/Platforms Architecture -- References -- 2 A Review of New Digital Technologies Impacting Maintenance Management -- 2.1 Introduction -- 2.2 The 'Internet of Things' (IOT) Technologies -- 2.3 Big Data -- 2.4 Predictive Analytics -- 2.5 Digital Twin Simulations -- 2.6 Augmented Reality -- 2.7 Business Intelligence and Data Visualization Tools -- 2.8 Complementary Technologies -- References -- Part II The Pillars for Digital Maintenance Management Excellence -- 3 Driving the Introduction of Digital Technologies to Enhance the Maintenance Management Process and Framework -- 3.1 Introduction -- 3.2 Starting from the Mm Process and Framework -- 3.3 New Opportunities with Data Availability and Analitycs -- 3.4 Visualizing the New Digital Maintenance Management Framework -- 3.5 The Need for a Business Asset Data Model -- References -- 4 The Definition of the Asset Data Model -- 4.1 Introduction -- 4.2 Methodology Proof of Concept: Data Model for a Criticality Analysis App -- References -- Part III Developments in Advanced Maintenance Management Apps -- 5 Advanced Asset Performance Management (APM) and Asset Investment Planning (AIP) Systems -- 5.1 The APM Toolbox Approach to Asset Maintenance -- 5.1.1 GE Digital's Predix Asset Performance Management -- 5.1.2 SAP APM Solution -- 5.1.3 Siemens APM Products -- 5.1.4 IBM APM Portfolio -- 5.2 The Asset Investment Planning (AIP) Solutions -- 5.2.1 Copperleaf Decision Analytics 5.2.2 The Cosmo Tech Hybrid Approach -- References -- Part IV Technical Challenges in the Use of Digital Technologies for Maintenance -- 6 The Non-ergodicity of Assets -- 6.1 Introduction to the Problem -- 6.2 A Case Study for Train Bearings -- 6.2.1 Axel Bearing Position Model (ML Model 1) -- 6.2.2 Train Bearing Position Model (ML Model 2) -- 6.2.3 Fleet Bearing Position Model (ML Model 3) -- 6.2.4 Testing and Validating the Models with Different Data -- 6.2.5 Conclusions of the Case Study -- References -- 7 The Curse of Dimensionality -- 7.1 Introduction to the Problem -- 7.2 Feature Selection Methods -- 7.3 The Use of Classifiers to Identify Failure Modes -- 7.4 Case Study: Cryogenic Reciprocating Compressors -- 7.4.1 Introduction to the Industrial Process -- 7.4.2 The Design of a CBM Strategy for the Compressors -- 7.4.3 The Selection of Features -- 7.4.4 The Results of the Analysis -- 7.4.5 Discussion of Results -- 7.5 Conclusions -- References -- 8 The Dynamic Measurement of Failure Risk -- 8.1 Introduction to the Problem -- 8.2 Dynamic Risk Level Assessment in Literature -- 8.3 Risk Management (RM), Assessment (RA) and Analysis (RAn) -- 8.4 Introduction to Dynamic Risk Assessment (DRA) -- 8.5 A Failure Mode Dra Method -- 8.6 Final Remarks -- References -- 9 The Dynamic Scheduling of Maintenance -- 9.1 Introduction -- 9.2 CBM Applied to Multi-component Systems -- 9.3 Fleet Maintenance Scheduling -- 9.4 Case Study. Scheduling Train Components' CBM -- 9.4.1 Introduction to the Problem -- 9.4.2 Building the Continuous Time Simulation Model -- 9.4.3 Implementation of a Practical Model in Vensim -- 9.4.4 Conclusions of the Case Study -- 9.5 Conclusions -- References -- Part V Emerging Data-Driven Processes for Asset Performance Management (APM) -- 10 Techniques for Anomalies Detection -- 10.1 Introduction 10.2 Anomalies Detection in Train Bearings -- 10.3 Baseline Analytics Selection -- 10.4 Anomalies Detection Model Implementation -- 10.5 Discussion of Results -- References -- 11 Asset Condition and Operations Efficiency -- 11.1 Introduction -- 11.2 Case Study. Efficiency in Renewable Energy Generation -- 11.2.1 Reviewing the Use of Predictive Techniques -- 11.2.2 Case Study Development -- 11.2.3 Case Study Conclusions -- 11.3 Case Study. LNG Storage Tank Pump Operation Analysis -- 11.3.1 Using Data Mining Together with Predictive Analytics -- 11.3.2 An Introduction to the DM AR Technique -- 11.3.3 The Emerging APM and Reliability Asessment Process -- 11.3.4 Presenting the Process Results -- 11.3.5 Case Study Conclusions -- References -- Part VI Emerging Data-Driven Processes for Asset Investment Planning (AIP) -- 12 Asset Health Indexing and Life Cycle Costing -- 12.1 Introduction -- 12.2 AHI Modelling Methodology -- 12.3 Life Cycle Costing Modelling Methodology -- 12.4 Case Study. Lng Regassification Plant -- 12.4.1 Asset Expense Profile Based on Its Health Index -- 12.4.2 Industrial Case Results and Findings -- References Plant maintenance-Data processing Plant maintenance-Management Erscheint auch als Druck-Ausgabe Crespo Márquez, Adolfo Digital Maintenance Management Cham : Springer International Publishing AG,c2022 978-3-030-97659-0 |
| spellingShingle | Crespo Márquez, Adolfo Digital Maintenance Management Guiding Digital Transformation in Maintenance Intro -- Foreword -- Acknowledgements -- Overview -- Relevant Topics -- Structure of the Book -- Contents -- Part I Fundamental Concepts to Understand Digital Transformation Benefits -- 1 Benefits of Digital Transformation for Maintenance Management Systems. Market Trends -- 1.1 Introduction -- 1.2 Key Managerial Areas That Can Be Digitally Supported -- 1.3 Emerging Asset Management Systems APM & -- AIP -- 1.4 The New Am Systems/Platforms Architecture -- References -- 2 A Review of New Digital Technologies Impacting Maintenance Management -- 2.1 Introduction -- 2.2 The 'Internet of Things' (IOT) Technologies -- 2.3 Big Data -- 2.4 Predictive Analytics -- 2.5 Digital Twin Simulations -- 2.6 Augmented Reality -- 2.7 Business Intelligence and Data Visualization Tools -- 2.8 Complementary Technologies -- References -- Part II The Pillars for Digital Maintenance Management Excellence -- 3 Driving the Introduction of Digital Technologies to Enhance the Maintenance Management Process and Framework -- 3.1 Introduction -- 3.2 Starting from the Mm Process and Framework -- 3.3 New Opportunities with Data Availability and Analitycs -- 3.4 Visualizing the New Digital Maintenance Management Framework -- 3.5 The Need for a Business Asset Data Model -- References -- 4 The Definition of the Asset Data Model -- 4.1 Introduction -- 4.2 Methodology Proof of Concept: Data Model for a Criticality Analysis App -- References -- Part III Developments in Advanced Maintenance Management Apps -- 5 Advanced Asset Performance Management (APM) and Asset Investment Planning (AIP) Systems -- 5.1 The APM Toolbox Approach to Asset Maintenance -- 5.1.1 GE Digital's Predix Asset Performance Management -- 5.1.2 SAP APM Solution -- 5.1.3 Siemens APM Products -- 5.1.4 IBM APM Portfolio -- 5.2 The Asset Investment Planning (AIP) Solutions -- 5.2.1 Copperleaf Decision Analytics 5.2.2 The Cosmo Tech Hybrid Approach -- References -- Part IV Technical Challenges in the Use of Digital Technologies for Maintenance -- 6 The Non-ergodicity of Assets -- 6.1 Introduction to the Problem -- 6.2 A Case Study for Train Bearings -- 6.2.1 Axel Bearing Position Model (ML Model 1) -- 6.2.2 Train Bearing Position Model (ML Model 2) -- 6.2.3 Fleet Bearing Position Model (ML Model 3) -- 6.2.4 Testing and Validating the Models with Different Data -- 6.2.5 Conclusions of the Case Study -- References -- 7 The Curse of Dimensionality -- 7.1 Introduction to the Problem -- 7.2 Feature Selection Methods -- 7.3 The Use of Classifiers to Identify Failure Modes -- 7.4 Case Study: Cryogenic Reciprocating Compressors -- 7.4.1 Introduction to the Industrial Process -- 7.4.2 The Design of a CBM Strategy for the Compressors -- 7.4.3 The Selection of Features -- 7.4.4 The Results of the Analysis -- 7.4.5 Discussion of Results -- 7.5 Conclusions -- References -- 8 The Dynamic Measurement of Failure Risk -- 8.1 Introduction to the Problem -- 8.2 Dynamic Risk Level Assessment in Literature -- 8.3 Risk Management (RM), Assessment (RA) and Analysis (RAn) -- 8.4 Introduction to Dynamic Risk Assessment (DRA) -- 8.5 A Failure Mode Dra Method -- 8.6 Final Remarks -- References -- 9 The Dynamic Scheduling of Maintenance -- 9.1 Introduction -- 9.2 CBM Applied to Multi-component Systems -- 9.3 Fleet Maintenance Scheduling -- 9.4 Case Study. Scheduling Train Components' CBM -- 9.4.1 Introduction to the Problem -- 9.4.2 Building the Continuous Time Simulation Model -- 9.4.3 Implementation of a Practical Model in Vensim -- 9.4.4 Conclusions of the Case Study -- 9.5 Conclusions -- References -- Part V Emerging Data-Driven Processes for Asset Performance Management (APM) -- 10 Techniques for Anomalies Detection -- 10.1 Introduction 10.2 Anomalies Detection in Train Bearings -- 10.3 Baseline Analytics Selection -- 10.4 Anomalies Detection Model Implementation -- 10.5 Discussion of Results -- References -- 11 Asset Condition and Operations Efficiency -- 11.1 Introduction -- 11.2 Case Study. Efficiency in Renewable Energy Generation -- 11.2.1 Reviewing the Use of Predictive Techniques -- 11.2.2 Case Study Development -- 11.2.3 Case Study Conclusions -- 11.3 Case Study. LNG Storage Tank Pump Operation Analysis -- 11.3.1 Using Data Mining Together with Predictive Analytics -- 11.3.2 An Introduction to the DM AR Technique -- 11.3.3 The Emerging APM and Reliability Asessment Process -- 11.3.4 Presenting the Process Results -- 11.3.5 Case Study Conclusions -- References -- Part VI Emerging Data-Driven Processes for Asset Investment Planning (AIP) -- 12 Asset Health Indexing and Life Cycle Costing -- 12.1 Introduction -- 12.2 AHI Modelling Methodology -- 12.3 Life Cycle Costing Modelling Methodology -- 12.4 Case Study. Lng Regassification Plant -- 12.4.1 Asset Expense Profile Based on Its Health Index -- 12.4.2 Industrial Case Results and Findings -- References Plant maintenance-Data processing Plant maintenance-Management |
| title | Digital Maintenance Management Guiding Digital Transformation in Maintenance |
| title_auth | Digital Maintenance Management Guiding Digital Transformation in Maintenance |
| title_exact_search | Digital Maintenance Management Guiding Digital Transformation in Maintenance |
| title_exact_search_txtP | Digital Maintenance Management Guiding Digital Transformation in Maintenance |
| title_full | Digital Maintenance Management Guiding Digital Transformation in Maintenance |
| title_fullStr | Digital Maintenance Management Guiding Digital Transformation in Maintenance |
| title_full_unstemmed | Digital Maintenance Management Guiding Digital Transformation in Maintenance |
| title_short | Digital Maintenance Management |
| title_sort | digital maintenance management guiding digital transformation in maintenance |
| title_sub | Guiding Digital Transformation in Maintenance |
| topic | Plant maintenance-Data processing Plant maintenance-Management |
| topic_facet | Plant maintenance-Data processing Plant maintenance-Management |
| work_keys_str_mv | AT crespomarquezadolfo digitalmaintenancemanagementguidingdigitaltransformationinmaintenance |