eMaintenance

Essential Electronic Tools for Efficiency
 
 
Elsevier (Verlag)
  • 1. Auflage
  • |
  • erschienen am 15. Juni 2017
  • |
  • 552 Seiten
 
E-Book | ePUB mit Adobe-DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-0-12-811154-3 (ISBN)
 

eMaintenance: Essential Electronic Tools for Efficiency enables the reader to improve efficiency of operations, maintenance staff, infrastructure managers and system integrators, by accessing a real time computerized system from data to decision. In recent years, the exciting possibilities of eMaintenance have become increasingly recognized as a source of productivity improvement in industry. The seamless linking of systems and equipment to control centres for real time reconfiguring is improving efficiency, reliability, and sustainability in a variety of settings.

The book provides an introduction to collecting and processing data from machinery, explains the methods of overcoming the challenges of data collection and processing, and presents tools for data driven condition monitoring and decision making. This is a groundbreaking handbook for those interested in the possibilities of running a plant as a smart asset.

  • Provides an introduction to collecting and processing data from machinery
  • Explains how to use sensor-based tools to increase efficiency of diagnosis, prognosis, and decision-making in maintenance
  • Describes methods for overcoming the challenges of data collection and processing


Diego Galar is Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology where he is coordinating several H2020 projects related to different aspects of cyber physical systems, Industry 4.0, IoT or industrial Big Data. He was also involved in the SKF UTC centre located in Lulea focused on SMART bearings, and is actively involved in national projects within the Swedish industry. Dr. Galar is principal researcher at Tecnalia, in Spain, heading the Maintenance and Reliability research group. He has authored more than 300 journal and conference papers, books and technical reports in the field of maintenance, working as member of editorial boards, scientific committees and chairing international journals and conferences. In industry, he has been technological director and CBM manager of international companies, and actively participated in national and international committees for standardization and R&D in the topics of reliability and maintenance. In the international arena, he has been visiting Professor in the Polytechnic of Braganza (Portugal), University of Valencia and NIU (USA). Currently, he is visiting Professor at the University of Sunderland (UK) and University of Maryland (USA), as well as guest Professor at the Pontificia Universidad Católica de Chile.
weitere Ausgaben werden ermittelt
  • Front Cover
  • eMaintenance
  • eMaintenance: Essential Electronic Tools for Efficiency
  • Copyright
  • Contents
  • 1 - Sensors and Data Acquisition
  • 1.1 SENSORS IN MAINTENANCE AND THE NEED TO INTEGRATE INFORMATION
  • 1.1.1 Sensors Put Intelligence Into Maintenance
  • 1.1.1.1 Sensing Maintenance Needs
  • 1.1.1.2 Going Wireless
  • 1.1.1.3 Enhancing Equipment, Both New and Old
  • 1.1.1.4 Taking Intelligence to the Next Level
  • 1.1.2 Basic Sensor Technology
  • 1.1.2.1 Sensor Data Sheets
  • 1.1.2.2 Sensor Performance Characteristics
  • 1.1.2.2.1 Transfer Function
  • 1.1.2.2.2 Sensitivity
  • 1.1.2.2.3 Span or Dynamic Range
  • 1.1.2.2.4 Accuracy or Uncertainty
  • 1.1.2.2.5 Hysteresis
  • 1.1.2.2.6 Nonlinearity (Often Called Linearity)
  • 1.1.2.2.7 Noise
  • 1.1.2.2.8 Resolution
  • 1.1.2.2.9 Bandwidth
  • 1.1.3 Role of Sensors and Objectives of Sensing
  • 1.1.4 Distributed Intelligent Sensors
  • 1.1.5 Infrastructure for Intelligent Systems
  • 1.2 SENSOR FUSION
  • 1.2.1 Principles of Sensor Fusion
  • 1.2.2 Motivation for Sensor Fusion
  • 1.2.3 Limitations of Sensor Fusion
  • 1.2.4 Types of Sensor Fusion
  • 1.2.4.1 C3I Versus Embedded Real-Time Applications
  • 1.2.4.2 Three-Level Categorization
  • 1.2.4.3 Categorization Based on Input/Output
  • 1.2.4.4 Categorization Based on Sensor Configuration
  • 1.2.5 Architectures for Sensor Fusion
  • 1.2.5.1 Joint Directors of Laboratories Fusion Architecture
  • 1.2.5.2 Waterfall Fusion Process Model
  • 1.2.5.3 Boyd Model
  • 1.2.5.4 LAAS Architecture
  • 1.2.5.5 Omnibus Model
  • 1.3 SENSOR NETWORKS: A DISTRIBUTED APPROACH IN LARGE ASSETS
  • 1.3.1 Sensor Network Research in the 21st Century
  • 1.3.2 Technology Trends
  • 1.3.3 Wireless Sensor Network
  • 1.3.3.1 Sensor Node Architecture
  • 1.3.3.2 Characteristics of Wireless Sensor Networks
  • 1.3.3.3 Fields of Application of Wireless Sensor Networks
  • 1.3.3.3.1 Security and Surveillance
  • 1.3.3.3.2 Environmental Monitoring
  • 1.3.3.4 Health Applications
  • 1.3.3.5 Smart Buildings
  • 1.3.3.6 Energy Control Systems
  • 1.3.3.7 Area Monitoring
  • 1.3.3.8 Agriculture Applications
  • 1.3.3.8.1 Agriculture
  • 1.3.3.8.2 Greenhouse Monitoring
  • 1.3.3.9 Industrial Applications
  • 1.3.3.10 Structural Monitoring
  • 1.3.4 New Applications of Sensor Networks
  • 1.3.4.1 Infrastructure Security
  • 1.3.4.2 Environment and Habitat Monitoring
  • 1.3.4.3 Industrial Sensing
  • 1.3.4.4 Traffic Control
  • 1.4 SMART SENSORS
  • 1.4.1 Structure of Smart Sensor
  • 1.4.2 Standards of Smart Sensor Network
  • 1.4.3 Importance and Adoption of Smart Sensor
  • 1.4.3.1 Cost Improvement
  • 1.4.3.2 Reduced Cost of Bulk Cables and Connectors
  • 1.4.3.3 Remote Diagnostics
  • 1.4.3.4 Enhancement of Application
  • 1.4.3.4.1 Self-Calibration
  • 1.4.3.4.2 Computation
  • 1.4.3.4.3 Communication
  • 1.4.3.4.4 Multisensing
  • 1.4.3.5 System Reliability
  • 1.4.3.6 Better Signal to Noise Ratio
  • 1.4.3.7 Sensor Improvement
  • 1.4.4 General Architecture of Smart Sensor
  • 1.4.5 Description of Smart Sensor Architecture
  • 1.4.6 Varieties of Smart Sensors
  • 1.4.7 Smart Sensors for Condition Based Maintenance
  • 1.4.7.1 What is OSA-CBM?
  • 1.4.7.2 Benefits of OSA-CBM
  • 1.5 ENERGY HARVESTING FOR SENSORS AND CONFIGURATION ISSUES
  • 1.5.1 Energy Harvesting Sources
  • 1.5.2 Energy Harvesting for Microelectromechanical Systems
  • 1.5.3 Harvesting Methods
  • 1.5.3.1 Solar Energy
  • 1.5.3.2 Piezoelectricity
  • 1.5.3.3 Radio Frequency Energy
  • 1.5.3.4 Thermal Energy
  • 1.5.3.5 Wind Energy
  • 1.5.4 Applications
  • REFERENCES
  • FURTHER READING
  • 2 - Data Collection
  • 2.1 DATA COLLECTION IN INDUSTRY
  • 2.1.1 Data Needs for Industry Management
  • 2.1.2 Data Collection Strategy
  • 2.1.2.1 Complete Enumeration and Sampling
  • 2.1.2.1.1 Definitions
  • 2.1.2.1.2 Complete Enumeration or Sampling?
  • 2.1.2.1.3 Stratification in Data Collection
  • 2.1.2.3.2 The Effect of Stratification
  • 2.1.3 Data Collection Methods
  • 2.2 DATA CLEANING
  • 2.2.1 Data Cleaning Problems
  • 2.2.1.1 Single-Source Problems
  • 2.2.1.2 Multi-source Problems
  • 2.2.2 Data Cleaning Approaches
  • 2.2.2.1 Data Analysis
  • 2.2.2.2 Defining Data Transformations
  • 2.2.2.3 Conflict Resolution
  • 2.2.3 Tool Support
  • 2.2.3.1 Data Analysis and Reengineering Tools
  • 2.2.3.2 Specialized Cleaning Tools
  • 2.2.3.3 Extraction, Transformation, Loading Tools
  • 2.2.4 Data Cleaning Overview
  • 2.2.4.1 Qualitative Error Detection
  • 2.2.4.2 Error Repairing
  • 2.2.5 Data Cleaning From a Statistical Perspective
  • 2.2.5.1 Data Cleaning With Statistics
  • 2.2.5.2 Data Cleaning for Statistical Analysis
  • 2.2.6 New Challenges
  • 2.3 DATA SANITIZATION
  • 2.3.1 Data Sanitization Techniques
  • 2.3.1.1 Technique: NULL'ing Out
  • 2.3.1.2 Technique: Masking Data
  • 2.3.1.3 Technique: Substitution
  • 2.3.1.4 Technique: Shuffling Records
  • 2.3.1.5 Technique: Number Variance
  • 2.3.1.6 Technique: Gibberish Generation
  • 2.3.1.7 Technique: Encryption/Decryption
  • 2.3.2 Data Sanitization Methods
  • 2.3.2.1 Secure Erase
  • 2.3.2.1.1 Secure Erase Wipe Method
  • 2.3.2.2 DoD 5220.22-M
  • 2.3.2.2.1 DoD 5220.22-M Wipe Method
  • 2.3.2.3 NCSC-TG-025
  • 2.3.2.3.1 NCSC-TG-025 Wipe Method
  • 2.3.2.4 AFSSI-5020
  • 2.3.2.4.1 AFSSI-5020 Wipe Method
  • 2.3.2.5 AR 380-19 Method
  • 2.3.2.5.1 AR 380-19 Wipe Method
  • 2.3.2.6 NAVSO P-5239-26
  • 2.3.2.6.1 NAVSO P-5239-26 Wipe Method
  • 2.3.2.7 RCMP TSSIT OPS-II
  • 2.3.2.7.1 RCMP TSSIT OPS-II Wipe Method
  • 2.3.2.8 CSEC ITSG-06
  • 2.3.2.8.1 CSEC ITSG-06 Wipe Method
  • 2.3.2.9 HMG IS5
  • 2.3.2.9.1 HMG IS5 Wipe Method
  • 2.3.2.10 ISM 6.2.92
  • 2.3.2.10.1 ISM 6.2.92 Wipe Method
  • 2.3.2.11 NZSIT 402
  • 2.3.2.11.1 NZSIT 402 Wipe Method
  • 2.3.2.12 VSITR
  • 2.3.2.12.1 VSITR Wipe Method
  • 2.3.2.13 Gutmann Method
  • 2.3.2.13.1 Gutmann Wipe Method
  • 2.3.2.14 Schneier Method
  • 2.3.2.14.1 Schneier Wipe Method
  • 2.3.2.15 Pfitzner Method
  • 2.3.2.15.1 Pfitzner Wipe Method
  • 2.3.2.16 Random Data Method
  • 2.3.2.16.1 Random Data Wipe Method
  • 2.3.2.17 Write Zero Method
  • 2.3.2.17.1 Write Zero Wipe Method
  • 2.4 DATA COMPRESSION AND TRANSMISSION
  • 2.4.1 Data Compression
  • 2.4.1.1 Fundamental Concept
  • 2.4.1.1.1 Definitions
  • 2.4.1.2 Classification of Methods
  • 2.4.2 Data Compression Strategies
  • 2.4.3 A Data Compression Model
  • 2.4.4 Data Transmission
  • 2.4.4.1 Connection-Oriented and Connectionless Transmissions
  • 2.4.4.2 Synchronous and Asynchronous Transmission
  • 2.4.5 Data Transmission and Open Systems Interconnection Model
  • REFERENCES
  • 3 - Preprocessing and Features
  • 3.1 TIME AND FREQUENCY DOMAINS FOR DATA REPRESENTATION
  • 3.1.1 Time Domain Versus Frequency Domain
  • 3.1.1.1 Time Domain
  • 3.1.1.2 Frequency Domain
  • 3.1.1.3 Frequency Spectrum
  • 3.1.1.4 Bandwidth
  • 3.1.2 Vibration Data Representation for Advanced Technology Facilities
  • 3.1.2.1 Time Versus Frequency Domain in Vibrations
  • 3.1.2.2 Types of Data Signals
  • 3.1.2.2.1 Deterministic Data
  • 3.1.2.2.2 Random Data
  • 3.1.2.3 Instantaneous Versus Time-Averaged Representation
  • 3.1.3 Time Series Data Representation
  • 3.1.3.1 Piecewise Approximation
  • 3.1.3.2 Identification of Important Points
  • 3.1.3.3 Symbolic Data Representations
  • 3.2 FEATURE SELECTION
  • 3.2.1 Filter Methods Used for Feature Selection
  • 3.2.1.1 Relief
  • 3.2.1.2 Correlation-Based Feature Selection
  • 3.2.1.3 Fast Correlated-Based Filter
  • 3.2.2 Wrapper Method Approach
  • 3.2.3 A Statistical View of Feature Selection
  • 3.2.4 A Machine Learning View of Feature Selection
  • 3.2.4.1 General Principles of Validation and Cross-Validation
  • 3.2.4.2 Cross-Validation and Its Variants
  • 3.2.4.2.1 Leave-One-Out
  • 3.2.4.2.2 Virtual Leave-One-Out
  • 3.2.4.3 Performance Error Bars and Tests of Significance
  • 3.2.4.4 Bagging
  • 3.2.4.5 Model Selection for Feature Selection
  • 3.2.4.5.1 Cross-Validation for Feature Selection
  • 3.2.4.5.2 Guaranteed Risk and Feature Selection
  • 3.2.4.5.3 Bagging and Feature Selection
  • 3.2.5 Cross-Validation Versus Overfitting
  • 3.2.6 Feature Selection Algorithms
  • 3.2.6.1 Forward Feature Selection
  • 3.2.6.2 Three Variants of Forward Selection
  • 3.2.6.2.1 Super Greedy Algorithm
  • 3.2.6.2.2 Greedy Algorithm
  • 3.2.6.2.3 Restricted Forward Selection
  • 3.3 FEATURE EXTRACTION
  • 3.3.1 Feature Extraction From the Time Domain
  • 3.3.1.1 State-of-the-Art Features From the Time Domain
  • 3.3.1.2 Normal Negative Likelihood
  • 3.3.1.3 Mean Variance Ratio
  • 3.3.1.4 Symbolized Shannon Entropy
  • 3.3.1.5 Simulation of Feature Performance
  • 3.3.2 Other Types of Feature Extraction Methods
  • 3.3.2.1 Wavelets
  • 3.3.2.1.1 Discrete Wavelet Series
  • 3.3.2.1.2 Discrete Wavelet Transform
  • 3.3.2.1.3 Spline Wavelet Transform
  • 3.3.2.1.4 Discrete B-Spline Wavelet Transform
  • 3.3.2.1.5 Design of Quadratic Spline Wavelets
  • 3.3.2.1.6 Fast Algorithm
  • REFERENCES
  • 4 - Data and Information Fusion From Disparate Asset Management Sources
  • 4.1 ONLINE AND OFF-LINE CONDITION MONITORING INFORMATION
  • 4.1.1 Condition Monitoring Data and Automatic Asset Data Collection
  • 4.1.2 Fusion of Maintenance and Control Data
  • 4.1.3 Data Fusion: A Need for Maintenance Processes
  • 4.1.4 Cloud Computing
  • 4.2 COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEMS
  • 4.2.1 Computerized Maintenance Management System Needs Assessment
  • 4.2.2 Computerized Maintenance Management System Capabilities
  • 4.2.3 Computerized Maintenance Management System Benefits
  • 4.2.4 Computerized Maintenance Management System Resources
  • 4.2.5 The Role of Computerized Maintenance Management System
  • 4.2.5.1 Does Your Organization Need a Computerized Maintenance Management System?
  • 4.2.5.2 Process First
  • 4.2.5.3 Measuring the Process
  • 4.2.5.4 Defining the Requirements
  • 4.2.5.5 Common Components of a Computerized Maintenance Management System
  • 4.2.5.5.1 Labor
  • 4.2.5.5.2 Asset
  • 4.2.5.5.3 Work Management
  • 4.2.5.5.4 Task or Procedures
  • 4.2.5.5.5 Preventive Maintenance
  • 4.2.5.5.6 Materials Management
  • 4.2.5.5.7 Purchasing
  • 4.2.5.5.8 Plug-ins or Add-ons
  • 4.2.6 Computerized Maintenance Management System Implementation
  • 4.2.6.1 Consulting Services
  • 4.2.6.2 System Configuration
  • 4.2.6.3 Training
  • 4.2.6.4 Go Live
  • 4.2.6.5 Postimplementation
  • 4.2.7 Maintenance Knowledge Management Fusing Computerized Maintenance Management System and Condition Monitoring
  • 4.2.7.1 An Integrated Approach to Asset Management
  • 4.2.7.1.1 Integration of Data Sources
  • 4.2.7.1.2 Definition of Integration/Relation Process
  • 4.2.7.1.3 Data Source Collection
  • 4.2.7.1.4 Importation of Relational Data
  • 4.2.7.1.5 Preprocessing and Data Abstraction
  • 4.2.7.1.6 Analysis and Correlation
  • 4.3 SUPERVISORY CONTROL AND DATA ACQUISITION AND AUTOMATION DATA FROM PROGRAMMABLE LOGIC CONTROLLERS AND SIMILAR DEVICES
  • 4.3.1 Supervisory Control and Data Acquisition System
  • 4.3.2 Basics of Supervisory Control and Data Acquisition
  • 4.3.2.1 Human-Machine Interface
  • 4.3.2.2 Supervisory System
  • 4.3.2.3 Remote Terminal Units
  • 4.3.2.4 Programmable Logic Controllers
  • 4.3.2.5 Communication Infrastructure
  • 4.3.2.6 Supervisory Control and Data Acquisition Programming
  • 4.3.3 Architecture of Supervisory Control and Data Acquisition
  • 4.3.4 Types of Supervisory Control and Data Acquisition Systems
  • 4.3.5 Applications of Supervisory Control and Data Acquisition
  • 4.3.5.1 Supervisory Control and Data Acquisition in Manufacturing Industries
  • 4.3.5.2 Supervisory Control and Data Acquisition in Wastewater Treatment and Distribution Plants
  • 4.3.5.3 Supervisory Control and Data Acquisition in Power Systems
  • 4.3.5.4 Wireless Supervisory Control and Data Acquisition
  • 4.3.6 Understanding Supervisory Control and Data Acquisition
  • 4.3.7 Programmable Logic Controller Programming
  • 4.3.7.1 Programmable Logic Controller Operation and Working Principles
  • 4.3.7.2 Programmable Logic Controller Selection
  • 4.3.7.3 Ordering Specifications
  • 4.3.7.4 User Interface
  • 4.3.8 Connections and Protocols
  • 4.3.8.1 Supervisory Control and Data Acquisition Configuration
  • 4.4 ENTERPRISE RESOURCE PLANNING AND OTHER COOPERATIVE INFORMATION RELATED TO THE ASSET
  • 4.4.1 Understanding Enterprise Resource Planning
  • 4.4.2 Core Components of Enterprise Resource Planning
  • 4.4.3 Why Use Enterprise Resource Planning?
  • 4.4.3.1 Key Motivating Factors
  • 4.4.3.2 Tangible and Intangible Benefits
  • 4.4.4 Enterprise Resource Planning Implementation Process
  • 4.4.5 Modeling the Requirements for Enterprise Resource Planning
  • 4.4.6 Model-Based Customization
  • 4.4.7 Modeling the Future: Enterprise Resource Planning Goes e-Business
  • 4.4.8 Conclusion: Enterprise Resource Planning Versus Computerized Maintenance Management System
  • REFERENCES
  • 5 - Diagnosis
  • 5.1 GOALS OF DETECTION, IDENTIFICATION, AND LOCALIZATION OF FAILURES
  • 5.1.1 Diagnostic Framework
  • 5.1.1.1 Introduction and Definitions
  • 5.1.1.2 Basic Definitions
  • 5.1.1.2.1 Fault Diagnosis: Fault-Tolerant Control
  • 5.1.1.2.2 Diagnosis Based on Analytical Models
  • 5.1.1.3 Fault Diagnosis Requirements and Performance Metrics
  • 5.1.1.4 Fault Monitoring and Diagnosis Framework
  • 5.1.1.4.1 Fault-Feature Vector
  • 5.1.2 Artificial Intelligence-Based Machine Condition Monitoring and Fault Diagnosis
  • 5.1.3 Neural Network Alternatives
  • 5.1.3.1 Extended Dynamic Back Propagation Networks
  • 5.1.3.2 Radial Basis Function Networks
  • 5.1.4 Supervising the Diagnostic Neural Network
  • 5.1.5 Other Methods for Fault Diagnosis
  • 5.1.5.1 Statistical Change Detection
  • 5.1.5.2 State-Based Feature Recognition
  • 5.1.5.3 Bayesian Networks
  • 5.1.5.4 Mechanical Face Seal and Drive Shaft Fault Diagnosis
  • 5.1.5.5 Hidden Markov Models
  • 5.2 DATA-DRIVEN VERSUS PHYSICAL MODELS
  • 5.2.1 Introduction
  • 5.2.2 Data-Driven Approaches
  • 5.2.2.1 Data-Driven Modeling
  • 5.2.2.2 Data-Driven Modeling Techniques
  • 5.2.2.2.1 Neural Network
  • 5.2.2.2.2 Gaussian Process Regression
  • 5.2.2.2.3 Fuzzy Rule-Based Systems
  • 5.2.2.2.4 Genetic Algorithms in Model Optimization
  • 5.2.2.2.5 Other Approaches
  • 5.2.3 Physics-Based Approaches
  • 5.2.3.1 Physical Model Adequacy
  • 5.2.4 Physical Model-Based Methods
  • 5.2.4.1 Fatigue and Crack Propagation Models
  • 5.2.4.2 Utility of Finite-Element Analysis in Model-Based Diagnosis
  • 5.2.4.2.1 Finite-Element Method
  • 5.2.4.2.2 Finite-Element Analysis for Fault Diagnosis
  • 5.3 SUPERVISED, SEMISUPERVISED, AND UNSUPERVISED LEARNING: ISSUES AND CHALLENGES
  • 5.3.1 Supervised and Unsupervised Learning
  • 5.3.1.1 Unsupervised Learning
  • 5.3.1.1.1 Pluses and Minuses of Unsupervised Learning
  • 5.3.1.1.2 Principal Components Analysis
  • 5.3.1.1.3 Clustering
  • 5.3.1.2 Supervised Learning
  • 5.3.1.2.1 How Supervised Learning Algorithms Work
  • 5.3.1.2.2 Generalizations of Supervised Learning
  • 5.3.1.2.3 Approaches and Algorithms
  • 5.3.2 Semisupervised Learning
  • 5.3.2.1 When Can Semisupervised Learning Work?
  • 5.3.2.1.1 Semisupervised Smoothness Assumption
  • 5.3.2.1.2 Cluster Assumption
  • 5.3.2.1.3 Manifold Assumption
  • 5.3.2.1.4 Transduction
  • 5.4 NO FAULT FOUND (NFF) AND ISSUES OF COMPLEX SYSTEMS
  • 5.4.1 Introduction to NFF
  • 5.4.2 What Causes NFFs?
  • 5.4.2.1 Cannot Duplicate
  • 5.4.2.2 Retest OK-Produced False Alarms
  • 5.4.2.3 Measurement Uncertainties and Test Equipment Problems
  • 5.4.2.4 System Level Failures That are not Detectable at the Subsystem Unit Under Test Level
  • 5.4.3 Classifying Depot Level Repair Causes
  • 5.4.3.1 Intermittent Failures
  • 5.4.3.2 False Alarms
  • 5.4.3.3 All NFFs
  • 5.4.4 Standards for NFF
  • 5.4.5 Organizational Procedures and Administration
  • 5.4.6 Implications of NFF
  • 5.4.6.1 Financial Implications
  • 5.4.6.2 Safety Considerations of NFFs
  • REFERENCES
  • 6 - Prognosis
  • 6.1 INTRODUCTION
  • 6.1.1 Maintenance and Prognosis
  • 6.1.2 Types of Maintenance
  • 6.1.2.1 Corrective Maintenance
  • 6.1.2.2 Preventive Maintenance
  • 6.1.2.3 Condition-Based Maintenance
  • 6.2 PROGNOSIS TECHNIQUES
  • 6.2.1 Concept of Prognostics
  • 6.2.2 Remaining Useful Life
  • 6.2.3 Technical Approaches
  • 6.2.3.1 Model-Based Approaches
  • 6.2.3.2 Data-Driven Approaches
  • 6.2.3.3 Experience-Based Prognostics
  • 6.2.3.4 Hybrid Approaches
  • 6.3 REMAINING USEFUL LIFE AND PROGNOSTICS
  • 6.3.1 Prognostic Techniques
  • 6.3.1.1 Techniques of Model-Based Approaches
  • 6.3.1.1.1 Particle Filtering for Prognostics
  • 6.3.1.1.2 Physics-Based Fatigue Models
  • 6.3.1.1.3 ARMA and ARIMA Methods
  • 6.3.1.1.3.1 Autoregressive Moving Average Models (p, q)
  • 6.3.1.1.3.2 Autoregressive Integrated Moving Average Models (p, d, q)
  • 6.3.1.1.3.3 Multiplicative Seasonal Autoregressive Integrated Moving Average Models (p, d, q)×(p, d, q)s
  • 6.3.1.1.4 Data-Driven Approaches
  • 6.3.1.1.4.1 Linear Regression
  • 6.3.1.1.4.2 Neural Network Model
  • 6.3.1.1.4.3 Fuzzy Logic Systems
  • 6.3.1.1.4.4 Gaussian Process Regression
  • 6.3.1.1.4.5 Relevance Vector Machine
  • 6.3.1.1.5 Experience-Based Approaches
  • 6.3.1.1.5.1 Bayesian Probability Theory
  • 6.3.1.1.5.2 Weibull Model: Analysis of Time to Failure
  • 6.3.1.1.5.3 Hidden Markov Models
  • 6.3.1.1.5.3.1 The HMMs Case
  • 6.3.1.1.5.3.2 The HSMMs Case
  • 6.3.1.1.5.3.3 The MoG-HMM-Based Method
  • 6.4 SELECTION OF PROGNOSIS TECHNIQUES FOR DIFFERENT TYPES OF ASSETS
  • 6.4.1 Rotating Machines
  • 6.4.1.1 Sensing Techniques and Sensors
  • 6.4.1.1.1 Monitoring Parameters
  • 6.4.1.1.2 Sensing Techniques
  • 6.4.1.1.3 Sensors
  • 6.4.1.2 Feature Extraction
  • 6.4.1.2.1 Detection and Identification
  • 6.4.1.2.2 Feature Classification
  • 6.4.1.3 Prognosis Model
  • 6.4.1.4 Data/Model Fusion
  • 6.4.2 Infrastructures
  • 6.4.2.1 Implementation of Condition-Based Maintenance on Bridges
  • 6.4.2.2 Remaining Fatigue Life Estimation of Structural Components
  • 6.4.2.3 Identification of Critical Components/Connections
  • 6.4.2.4 Remaining Fatigue Life Estimation of Critical Connections
  • 6.4.2.5 Member Replacement/Strengthening Scheme
  • 6.4.3 Complex Systems
  • 6.4.3.1 Prognosis Methods
  • 6.4.3.2 Generic Modeling for Prognostics
  • 6.4.3.3 Functional Prognosis
  • 6.5 CONTEXT-BASED PROGNOSIS: THE INFLUENCE OF INFORMATION AND COMMUNICATION TECHNOLOGY IN REMAINING USEFUL LIFE ESTIMATION
  • 6.5.1 Context-Aware Condition Monitoring
  • 6.5.2 Diagnosis With Anomaly Detection
  • 6.5.3 Context-Driven e-Maintenance
  • 6.6 CONCLUSIONS AND DISCUSSION
  • REFERENCES
  • FURTHER READING
  • 7 - Maintenance Decision Support Systems
  • 7.1 A NEW ERA IN INDUSTRY 4.0: MAINTENANCE 4.0
  • 7.1.1 What is Industry 4.0?
  • 7.1.1.1 Definition and Development
  • 7.1.1.2 Main Characteristics
  • 7.1.1.2.1 Vertical Networking of Smart Production Systems
  • 7.1.1.2.2 Horizontal Integration via a New Generation of Global Value Chain Networks
  • 7.1.1.2.3 Through-Engineering Across the Entire Value Chain
  • 7.1.1.2.4 Acceleration Through Exponential Technologies
  • 7.1.2 Industry 4.0 Key Components
  • 7.1.2.1 Cyber-Physical System
  • 7.1.2.2 Internet of Things
  • 7.1.2.3 Data Mining
  • 7.1.2.4 Internet of Services
  • 7.1.3 Principles of Industry 4.0
  • 7.1.3.1 Interoperability
  • 7.1.3.2 Virtualization
  • 7.1.3.3 Decentralization
  • 7.1.3.4 Real-Time Capability
  • 7.1.3.5 Service Orientation
  • 7.1.3.6 Modularity
  • 7.1.3.7 Security
  • 7.2 VIRTUALIZATION AND EMULATION: THE E-FACTORY FOR FAULT RATE REDUCTION
  • 7.2.1 Terminology
  • 7.2.2 History
  • 7.2.3 Virtualization for Manufacturing and Internet of Things
  • 7.2.3.1 Virtualization
  • 7.2.3.2 Integrated Industry
  • 7.2.3.3 Lightweight Virtualization Using Linux Containers
  • 7.2.3.4 Virtualization in Manufacturing Environments
  • 7.2.3.5 Reference Architectural Model Industry 4.0: Integrated Industry Layers on Modular Industry Computing Architecture
  • 7.2.4 Embedded Virtualization
  • 7.2.4.1 Internet of Things and Embedded Virtualization
  • 7.2.5 Emulation Frameworks
  • 7.3 MULTIVARIATE MAINTENANCE DECISION SUPPORT: A CONSEQUENCE OF INTERNET OF THINGS
  • 7.3.1 Decision Support Systems
  • 7.3.1.1 Definition of System
  • 7.3.2 Representation of the Decision-Making Process
  • 7.3.2.1 The Decision-Making Process
  • 7.3.2.2 Types of Decisions
  • 7.3.2.3 Approaches to the Decision-Making Process
  • 7.3.3 Condition-Based Maintenance Decision Support Systems
  • 7.3.3.1 Composition of Condition-Based Maintenance Systems
  • 7.3.3.2 Configuration of a Condition-Based Maintenance Decision Support System
  • 7.3.3.2.1 Hardware Composition
  • 7.3.3.3 Functions of a Condition-Based Maintenance Decision Support System
  • 7.3.3.3.1 Data Acquisition Module
  • 7.3.3.3.2 Data Processing Module
  • 7.3.3.3.3 Monitoring and Early Warning Module
  • 7.3.3.3.4 State Analysis Module
  • 7.3.3.3.5 State Diagnosis Module
  • 7.3.3.3.6 State Evaluation Module
  • 7.3.3.3.7 State Forecast Module
  • 7.3.3.3.8 Risk Assessment Module
  • 7.3.3.3.9 Decision Suggestion Module
  • 7.3.3.4 Key Technologies in the Fault Diagnosis System
  • 7.3.4 Internet of Things
  • 7.3.4.1 Introduction
  • 7.3.4.2 Definition, Trends, and Elements of Internet of Things
  • 7.3.4.2.1 Definition
  • 7.3.4.2.2 Trends
  • 7.3.4.2.3 Internet of Things Elements
  • 7.3.4.2.3.1 Radio-Frequency Identification
  • 7.3.4.2.3.2 Wireless Sensor Networks
  • 7.3.4.2.3.3 Addressing Schemes
  • 7.3.4.2.3.4 Data Storage and Analytics
  • 7.3.4.2.3.5 Visualization
  • 7.3.4.3 Applications
  • 7.3.4.4 Cloud-Centric Internet of Things
  • 7.3.4.5 Open Challenges and Future Directions
  • 7.3.4.5.1 Architecture
  • 7.3.4.5.2 Energy-Efficient Sensing
  • 7.3.4.5.3 Secure Reprogrammable Networks and Privacy
  • 7.3.4.5.4 Quality of Service
  • 7.3.4.5.5 New Protocols
  • 7.3.4.5.6 Participatory Sensing
  • 7.3.4.5.7 Data Mining
  • 7.3.4.5.8 Geographic Information System-Based Visualization
  • 7.3.4.5.9 Cloud Computing
  • 7.3.4.5.10 International Activities
  • 7.4 THE END OF TRADITIONAL MAINTENANCE APPROACHES: REAL-TIME DECISIONS BASED ON INDUSTRIAL BIG DATA
  • 7.4.1 Big Data: Analytics and Decision-Making
  • 7.4.2 Real-Time Responses With Big Data
  • 7.4.2.1 Systems for Real-Time Response
  • 7.4.2.1.1 The Power of Decision Management Systems
  • 7.4.2.1.2 Impact of Big Data
  • 7.4.2.1.3 Event-Based Decision Management Systems
  • 7.4.2.1.3.1 From Decision Services to Decision Agents
  • 7.4.2.1.3.2 From Data at Rest to Data in Motion
  • 7.4.2.1.3.3 Pattern Detection and Business Decisions
  • 7.4.2.1.3.4 Decisions Shared Across Processes and Events
  • 7.4.2.1.4 Use Cases for Event-Based Decision Management Systems
  • 7.4.2.1.4.1 Real-Time Marketing
  • 7.4.2.1.4.2 Proactive Customer Support
  • 7.4.2.1.4.3 Fraud Detection
  • 7.4.2.1.4.4 Internet of Things
  • 7.4.3 Real-Time Big Data Analytics Applications
  • 7.4.4 Real-Time Big Data Analytics Challenges
  • 7.4.4.1 Real-Time Event Transfer
  • 7.4.4.2 Real-Time Situation Discovery
  • 7.4.4.3 Real-Time Analytics
  • 7.4.4.4 Real-Time Decision-Making
  • 7.4.4.5 Real-Time Responses
  • 7.4.5 Big Data Techniques and Technologies
  • 7.4.5.1 Techniques for Analyzing Big Data
  • 7.4.5.2 Big Data Technologies
  • 7.5 EMAINTENANCE AND MAINTENANCE 4.0: IMPACT OF TECHNOLOGY ON OPERATION AND MAINTENANCE KEY PERFORMANCE INDICATORS
  • 7.5.1 Maintenance 4.0
  • 7.5.1.1 eMaintenance
  • 7.5.1.2 Radio-Frequency Identification
  • 7.5.1.2.1 Evolution of Radio-Frequency Identification
  • 7.5.1.2.2 Operational Aspects of Radio-Frequency Identification Systems
  • 7.5.1.2.3 Radio-Frequency Identification Applications
  • 7.5.1.3 Virtual or Augmented Reality
  • 7.5.1.3.1 Virtual Reality
  • 7.5.1.3.2 Augmented Reality
  • 7.5.1.3.3 Virtual Reality Versus Augmented Reality
  • 7.5.1.4 Visualization
  • 7.5.1.5 Knowledge Sharing and Networking and Assistant Systems
  • 7.5.2 Challenges of Maintenance 4.0
  • 7.5.3 eMaintenance
  • 7.5.3.1 eMaintenance: Pervasive Computing in Maintenance
  • 7.5.3.2 Improvements in Maintenance Types and Strategies Using eMaintenance
  • 7.5.3.2.1 Potential Improvements
  • 7.5.3.2.1.1 Remote Maintenance
  • 7.5.3.2.1.2 Cooperative/Collaborative Maintenance
  • 7.5.3.2.1.3 Immediate/Online Maintenance
  • 7.5.3.2.1.4 Predictive Maintenance
  • 7.5.3.3 Potential Improvements in Maintenance Support and Tools With eMaintenance
  • 7.5.3.3.1 Potential Improvements
  • 7.5.3.3.1.1 Fault/Failure Analysis
  • 7.5.3.3.1.2 Maintenance Documentation/Record
  • 7.5.3.3.1.3 After-Sales Services
  • 7.5.3.4 Potential Improvements in Maintenance Activities With eMaintenance
  • 7.5.3.4.1 Potential Improvements
  • 7.5.3.4.1.1 Fault Diagnosis/Localization
  • 7.5.3.4.1.2 Repair/Rebuilding
  • 7.5.3.4.1.3 Modification/Improvement-Knowledge Capitalization and Management
  • 7.5.4 Type of Maintenance Indicators: Leading Versus Lagging and Hard Versus Soft
  • 7.5.5 Maintenance Performance Indicators in the Literature
  • 7.5.6 Key Performance Indicators
  • 7.5.6.1 Energy Key Performance Indicators
  • 7.5.6.2 Raw Material Key Performance Indicators
  • 7.5.6.3 Operation Key Performance Indicators
  • 7.5.6.4 Control Performance Key Performance Indicators
  • 7.5.6.5 Maintenance Key Performance Indicators
  • 7.5.6.6 Planning Key Performance Indicators
  • 7.5.6.7 Inventory Key Performance Indicators
  • 7.5.6.8 Equipment Key Performance Indicators
  • REFERENCES
  • FURTHER READING
  • 8 - Actuators and Self-Maintenance Approaches
  • 8.1 INTELLIGENT (SMART) MATERIALS FOR MAINTENANCE
  • 8.1.1 Introduction
  • 8.1.1.1 Definition of an Intelligent Material
  • 8.1.2 Concept of Intelligent Materials
  • 8.1.2.1 Sensor Function
  • 8.1.2.2 Memory and Processor Function
  • 8.1.2.3 Actuator Function
  • 8.1.3 Types of Smart Materials
  • 8.1.3.1 Pyroelectric Material
  • 8.1.3.2 Piezoelectric Material
  • 8.1.3.3 Electrostrictive
  • 8.1.3.4 Magneto-Restrictive
  • 8.1.3.5 Shape Memory Alloys
  • 8.1.3.6 Thermoresponsive Materials
  • 8.1.3.7 Optical Fibers
  • 8.1.3.8 pH-Sensitive Materials
  • 8.1.3.9 Chromogenic Systems
  • 8.1.3.10 Polymer Gels
  • 8.1.4 Classification of Smart Materials
  • 8.1.5 Applications of Smart Materials
  • 8.1.5.1 Some Specific Applications
  • 8.1.6 Future Trends in Smart Materials
  • 8.1.6.1 Issues of Smart Structures and Structronic Systems
  • 8.1.7 Improvement in Intelligent/Smart Materials
  • 8.2 SMART DEVICES WITH ACTUATION CAPABILITIES: SMART BEARINGS
  • 8.2.1 Smart Devices
  • 8.2.2 Smart Device Paradigm
  • 8.2.3 Smart Device Architecture
  • 8.2.4 Smart Device Specification
  • 8.2.4.1 Data Transfer Protocol
  • 8.2.4.2 Terminology and Concepts
  • 8.2.4.3 Metadata Service
  • 8.2.4.4 Sensor Metadata Service: getSensorMetadata
  • 8.2.4.5 Actuator Metadata Service: getActuatorMetadata
  • 8.2.4.6 Sensor Service: getSensorData
  • 8.2.4.7 Actuator Service: sendActuatorData
  • 8.2.4.8 User Activity Logging Service: getLoggingInfo
  • 8.2.4.9 Client Application Service: getClients
  • 8.2.4.10 Model Service: getModels
  • 8.2.4.11 Functionalities: Best Practices
  • 8.2.5 Smart Bearings: From Sensing to Actuation
  • 8.2.6 Risk Assessment for Maintenance Actions
  • 8.2.6.1 SMARTness in Bearings
  • 8.2.6.2 Role of Risk in Smart Bearings
  • 8.2.7 Diagnosis and Prognosis as Maintenance Decision Support System Enablers: Risk Assessment
  • 8.2.7.1 Dilemma of Concurrent Failures
  • 8.2.7.2 Benefits of Accurate Diagnosis
  • 8.2.7.3 Importance of Context
  • 8.2.7.4 Network of Smart Bearings
  • 8.2.8 Conclusions
  • 8.3 ROBOTICS IN MAINTENANCE DUTIES
  • 8.3.1 Application Examples and Techniques
  • 8.3.1.1 Nuclear Industry
  • 8.3.1.2 Highways
  • 8.3.1.3 Railways
  • 8.3.1.4 Power Line Maintenance
  • 8.3.1.5 Aircraft Servicing
  • 8.3.1.6 Underwater Facilities
  • 8.3.1.7 Coke Ovens
  • 8.3.1.8 Summary of Robotics Applications in Maintenance and Repair
  • 8.3.2 Emerging Trends
  • REFERENCES
  • FURTHER READING
  • Index
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • Back Cover

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