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E-raamat: eMaintenance: Essential Electronic Tools for Efficiency

(Professor, Luleå Railway Research Center, Luleå University of Technology, Luleå, Sweden), (Professor, Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology, Sweden)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 15-Jun-2017
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128111543
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eMaintenance: Essential Electronic Tools for EfficiencySuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 15-Jun-2017
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128111543
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In recent years we have seen the exciting possibilities of eMaintenance become a recognized 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.

Methods of overcoming the challenges of data collection and processing are explained before the introduction of tools for data driven condition monitoring and decision making.

This book will improve efficiency of operations, maintenance staff, infrastructure managers and system integrators by accessing a real time computerized system from data to decision. This is a guide for everyone interested in the possibilities of running a plant as a smart asset.

  • Includes links to eMaintenance applications and MATLAB code which can be downloaded and used
  • 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

Muu info

This practical guide shows how to use sensor-based tools to improve decision-making and enhance operational efficiency in the industrial plant environment
1 Sensors and Data Acquisition
1(72)
1.1 Sensors in Maintenance and the Need to Integrate Information
1(17)
1.1.1 Sensors Put Intelligence Into Maintenance
3(4)
1.1.2 Basic Sensor Technology
7(3)
1.1.3 Role of Sensors and Objectives of Sensing
10(2)
1.1.4 Distributed Intelligent Sensors
12(3)
1.1.5 Infrastructure for Intelligent Systems
15(3)
1.2 Sensor Fusion
18(16)
1.2.1 Principles of Sensor Fusion
19(1)
1.2.2 Motivation for Sensor Fusion
20(3)
1.2.3 Limitations of Sensor Fusion
23(1)
1.2.4 Types of Sensor Fusion
24(4)
1.2.5 Architectures for Sensor Fusion
28(6)
1.3 Sensor Networks: A Distributed Approach in Large Assets
34(11)
1.3.1 Sensor Network Research in the 21st Century
34(1)
1.3.2 Technology Trends
35(1)
1.3.3 Wireless Sensor Network
36(7)
1.3.4 New Applications of Sensor Networks
43(2)
1.4 Smart Sensors
45(12)
1.4.1 Structure of Smart Sensor
47(1)
1.4.2 Standards of Smart Sensor Network
48(1)
1.4.3 Importance and Adoption of Smart Sensor
49(3)
1.4.4 General Architecture of Smart Sensor
52(1)
1.4.5 Description of Smart Sensor Architecture
53(1)
1.4.6 Varieties of Smart Sensors
54(1)
1.4.7 Smart Sensors for Condition Based Maintenance
55(2)
1.5 Energy Harvesting for Sensors and Configuration Issues
57(16)
1.5.1 Energy Harvesting Sources
57(2)
1.5.2 Energy Harvesting for Microelectromechanical Systems
59(3)
1.5.3 Harvesting Methods
62(4)
1.5.4 Applications
66(1)
References
66(6)
Further Reading
72(1)
2 Data Collection
73(56)
2.1 Data Collection in Industry
73(3)
2.1.1 Data Needs for Industry Management
73(1)
2.1.2 Data Collection Strategy
73(3)
2.1.3 Data Collection Methods
76(1)
2.2 Data Cleaning
76(20)
2.2.1 Data Cleaning Problems
78(4)
2.2.2 Data Cleaning Approaches
82(5)
2.2.3 Tool Support
87(2)
2.2.4 Data Cleaning Overview
89(4)
2.2.5 Data Cleaning From a Statistical Perspective
93(2)
2.2.6 New Challenges
95(1)
2.3 Data Sanitization
96(14)
2.3.1 Data Sanitization Techniques
96(4)
2.3.2 Data Sanitization Methods
100(10)
2.4 Data Compression and Transmission
110(19)
2.4.1 Data Compression
110(6)
2.4.2 Data Compression Strategies
116(1)
2.4.3 A Data Compression Model
117(2)
2.4.4 Data Transmission
119(4)
2.4.5 Data Transmission and Open Systems Interconnection Model
123(1)
References
124(5)
3 Preprocessing and Features
129(50)
3.1 Time and Frequency Domains for Data Representation
129(10)
3.1.1 Time Domain Versus Frequency Domain
129(6)
3.1.2 Vibration Data Representation for Advanced Technology Facilities
135(3)
3.1.3 Time Series Data Representation
138(1)
3.2 Feature Selection
139(19)
3.2.1 Filter Methods Used for Feature Selection
141(1)
3.2.2 Wrapper Method Approach
142(3)
3.2.3 A Statistical View of Feature Selection
145(1)
3.2.4 A Machine Learning View of Feature Selection
146(8)
3.2.5 Cross-Validation Versus Overfitting
154(1)
3.2.6 Feature Selection Algorithms
155(3)
3.3 Feature Extraction
158(21)
3.3.1 Feature Extraction From the Time Domain
158(7)
3.3.2 Other Types of Feature Extraction Methods
165(10)
References
175(4)
4 Data and Information Fusion From Disparate Asset Management Sources
179(56)
4.1 Online and Off-Line Condition Monitoring Information
179(12)
4.1.1 Condition Monitoring Data and Automatic Asset Data Collection
179(4)
4.1.2 Fusion of Maintenance and Control Data
183(4)
4.1.3 Data Fusion: A Need for Maintenance Processes
187(2)
4.1.4 Cloud Computing
189(2)
4.2 Computerized Maintenance Management Systems
191(21)
4.2.1 Computerized Maintenance Management System Needs Assessment
191(1)
4.2.2 Computerized Maintenance Management System Capabilities
191(1)
4.2.3 Computerized Maintenance Management System Benefits
192(1)
4.2.4 Computerized Maintenance Management System Resources
193(1)
4.2.5 The Role of Computerized Maintenance Management System
193(6)
4.2.6 Computerized Maintenance Management System Implementation
199(2)
4.2.7 Maintenance Knowledge Management Fusing Computerized Maintenance Management System and Condition Monitoring
201(11)
4.3 Supervisory Control and Data Acquisition and Automation Data From Programmable Logic Controllers and Similar Devices
212(11)
4.3.1 Supervisory Control and Data Acquisition System
213(1)
4.3.2 Basics of Supervisory Control and Data Acquisition
214(1)
4.3.3 Architecture of Supervisory Control and Data Acquisition
215(1)
4.3.4 Types of Supervisory Control and Data Acquisition Systems
216(1)
4.3.5 Applications of Supervisory Control and Data Acquisition
216(3)
4.3.6 Understanding Supervisory Control and Data Acquisition
219(1)
4.3.7 Programmable Logic Controller Programming
219(3)
4.3.8 Connections and Protocols
222(1)
4.4 Enterprise Resource Planning and Other Cooperative Information Related to the Asset
223(12)
4.4.1 Understanding Enterprise Resource Planning
224(1)
4.4.2 Core Components of Enterprise Resource Planning
224(1)
4.4.3 Why Use Enterprise Resource Planning?
225(1)
4.4.4 Enterprise Resource Planning Implementation Process
226(2)
4.4.5 Modeling the Requirements for Enterprise Resource Planning
228(1)
4.4.6 Model-Based Customization
229(1)
4.4.7 Modeling the Future: Enterprise Resource Planning Goes e-Business
230(2)
4.4.8 Conclusion: Enterprise Resource Planning Versus Computerized Maintenance Management System
232(1)
References
232(3)
5 Diagnosis
235(76)
5.1 Goals of Detection, Identification, and Localization of Failures
235(22)
5.1.1 Diagnostic Framework
238(7)
5.1.2 Artificial Intelligence---Based Machine Condition Monitoring and Fault Diagnosis
245(1)
5.1.3 Neural Network Alternatives
246(3)
5.1.4 Supervising the Diagnostic Neural Network
249(1)
5.1.5 Other Methods for Fault Diagnosis
250(7)
5.2 Data-Driven Versus Physical Models
257(16)
5.2.1 Introduction
257(2)
5.2.2 Data-Driven Approaches
259(9)
5.2.3 Physics-Based Approaches
268(1)
5.2.4 Physical Model---Based Methods
269(4)
5.3 Supervised, Semisupervised, and Unsupervised Learning: Issues and Challenges
273(9)
5.3.1 Supervised and Unsupervised Learning
273(5)
5.3.2 Semisupervised Learning
278(4)
5.4 No Fault Found (NFF) and Issues of Complex Systems
282(29)
5.4.1 Introduction to NFF
284(3)
5.4.2 What Causes NFFs?
287(2)
5.4.3 Classifying Depot Level Repair Causes
289(8)
5.4.4 Standards for NFF
297(2)
5.4.5 Organizational Procedures and Administration
299(1)
5.4.6 Implications of NFF
300(3)
References
303(8)
6 Prognosis
311(60)
6.1 Introduction
311(4)
6.1.1 Maintenance and Prognosis
312(1)
6.1.2 Types of Maintenance
313(2)
6.2 Prognosis Techniques
315(9)
6.2.1 Concept of Prognostics
315(1)
6.2.2 Remaining Useful Life
316(2)
6.2.3 Technical Approaches
318(6)
6.3 Remaining Useful Life and Prognostics
324(25)
6.3.1 Prognostic Techniques
327(22)
6.4 Selection of Prognosis Techniques for Different Types of Assets
349(10)
6.4.1 Rotating Machines
349(3)
6.4.2 Infrastructures
352(4)
6.4.3 Complex Systems
356(3)
6.5 Context-Based Prognosis: The Influence of Information and Communication Technology in Remaining Useful Life Estimation
359(4)
6.5.1 Context-Aware Condition Monitoring
360(1)
6.5.2 Diagnosis With Anomaly Detection
361(2)
6.5.3 Context-Driven e-Maintenance
363(1)
6.6 Conclusions and Discussion
363(8)
References
366(3)
Further Reading
369(2)
7 Maintenance Decision Support Systems
371(104)
7.1 A New Era in Industry 4.0: Maintenance 4.0
371(11)
7.1.1 What is Industry 4.0?
373(6)
7.1.2 Industry 4.0 Key Components
379(2)
7.1.3 Principles of Industry 4.0
381(1)
7.2 Virtualization and Emulation: The e-Factory for Fault Rate Reduction
382(10)
7.2.1 Terminology
382(2)
7.2.2 History
384(1)
7.2.3 Virtualization for Manufacturing and Internet of Things
385(4)
7.2.4 Embedded Virtualization
389(2)
7.2.5 Emulation Frameworks
391(1)
7.3 Multivariate Maintenance Decision Support: A Consequence of Internet of Things
392(36)
7.3.1 Decision Support Systems
392(2)
7.3.2 Representation of the Decision-Making Process
394(7)
7.3.3 Condition-Based Maintenance Decision Support Systems
401(10)
7.3.4 Internet of Things
411(17)
7.4 The End of Traditional Maintenance Approaches: Real-Time Decisions Based on Industrial Big Data
428(20)
7.4.1 Big Data: Analytics and Decision-Making
429(4)
7.4.2 Real-Time Responses With Big Data
433(5)
7.4.3 Real-Time Big Data Analytics Applications
438(1)
7.4.4 Real-Time Big Data Analytics Challenges
439(2)
7.4.5 Big Data Techniques and Technologies
441(7)
7.5 eMaintenance and Maintenance 4.0: Impact of Technology on Operation and Maintenance Key Performance Indicators
448(27)
7.5.1 Maintenance 4.0
449(5)
7.5.2 Challenges of Maintenance 4.0
454(1)
7.5.3 eMaintenance
455(6)
7.5.4 Type of Maintenance Indicators: Leading Versus Lagging and Hard Versus Soft
461(2)
7.5.5 Maintenance Performance Indicators in the Literature
463(2)
7.5.6 Key Performance Indicators
465(2)
References
467(7)
Further Reading
474(1)
8 Actuators and Self-Maintenance Approaches
475(54)
8.1 Intelligent (Smart) Materials for Maintenance
475(14)
8.1.1 Introduction
475(1)
8.1.2 Concept of Intelligent Materials
476(2)
8.1.3 Types of Smart Materials
478(5)
8.1.4 Classification of Smart Materials
483(1)
8.1.5 Applications of Smart Materials
484(4)
8.1.6 Future Trends in Smart Materials
488(1)
8.1.7 Improvement in Intelligent/Smart Materials
489(1)
8.2 Smart Devices With Actuation Capabilities: Smart Bearings
489(23)
8.2.1 Smart Devices
489(2)
8.2.2 Smart Device Paradigm
491(1)
8.2.3 Smart Device Architecture
492(1)
8.2.4 Smart Device Specification
492(8)
8.2.5 Smart Bearings: From Sensing to Actuation
500(1)
8.2.6 Risk Assessment for Maintenance Actions
501(5)
8.2.7 Diagnosis and Prognosis as Maintenance Decision Support System Enablers: Risk Assessment
506(6)
8.2.8 Conclusions
512(1)
8.3 Robotics in Maintenance Duties
512(17)
8.3.1 Application Examples and Techniques
513(7)
8.3.2 Emerging Trends
520(4)
References
524(3)
Further Reading
527(2)
Index 529
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. Uday Kumar is professor of operation and maintenance engineering, director of Luleå Railway Research Center and scientific director of the Strategic Area of Research and InnovationSustainable Transport at Luleå University of Technology, Luleå, Sweden. Before joining Luleå University of Technology, Dr. Kumar was professor of Offshore Technology (Operation and Maintenance Engineering) at Stavanger University, Norway. Dr. Kumar has research interests in the subject area of reliability and maintainability engineering, maintenance modeling, condition monitoring, LCC and risk analysis, etc. He has published more than 300 papers in international jour- nals and peer-reviewed conferences and has made contributions to many edited books. He has supervised more than 25 PhD theses related to the area of reliability and maintenance. Dr. Kumar has been a keynote and invited speaker at numerous congresses, conferences, seminars, industrial forums, workshops, and academic institutions. He is an elected member of the Swedish Royal Academy of Engineering Sciences.
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