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In recent years, a considerable amount of effort has been devoted, both in industry and academia, to improving maintenance. Time is a critical factor in maintenance, and efforts are placed to monitor, analyze, and visualize machine or asset data in order to anticipate to any possible failure, prevent damage, and save costs.

The MANTIS Book highlights the underpinning fundamentals of Condition-Based Maintenance related conceptual ideas, an overall idea of preventive maintenance, the economic impact and technical solution.

The core content of this book describes the outcome of the Cyber-Physical System based Proactive Collaborative Maintenance project, also known as MANTIS, and funded by EU ECSEL Joint Undertaking under Grant Agreement nº 662189. The ambition has been to support the creation of a maintenance-oriented reference architecture that supports the maintenance data lifecycle, to enable the use of novel kinds of maintenance strategies for industrial machinery. The key enabler has been the fine blend of collecting data through Cyber-Physical Systems, and the usage of machine learning techniques and advanced visualization for the enhanced monitoring of the machines.

Topics discussed include, in the context of maintenance: Cyber-Physical Systems, Communication Middleware, Machine Learning, Advanced Visualization, Business Models, Future Trends. An important focus of the book is the application of the techniques in real world context, and in fact all the work is driven by the pilots, all of them centered on real machines and factories.

This book is suitable for industrial and maintenance managers that want to implement a new strategy for maintenance in their companies. It should give readers a basic idea on the first steps to implementing a maintenance-oriented platform or information system.
Acknowledgments xxiii
Foreword xxv
List of Contributors xxvii
List of Figures xxxi
List of Tables xliii
List of Abbreviations xlv
1 Introduction 1(6)
Urko Zurutuza
Michele Albano
Erkki Jantunen
Luis Lino Ferreira
1.1 Maintenance Today
3(1)
1.2 The Path to Proactive Maintenance
3(2)
1.3 Why to Read this Book
5(1)
References
6(1)
2 Business Drivers of a Collaborative, Proactive Maintenance Solution 7(30)
Erkki Jantunen
Alp Akcay
Jaime Campos
Mike Holenderski
Arja Kotkansalo
Riku Salokangas
Pankaj Sharma
2.1 Introduction
7(8)
2.1.1 CBM-based PM in Industry
8(1)
2.1.2 CBM-based PM in Service Business
9(1)
2.1.3 Life Cycle Cost and Overall Equipment Effectiveness
10(1)
2.1.4 Integrating IoT with Old Equipment
11(1)
2.1.5 CBM Strategy as a Maintenance Business Driver
11(4)
2.2 Optimization of Maintenance Costs
15(1)
2.3 Business Drivers for Collaborative Proactive Maintenance
16(9)
2.3.1 Maintenance Optimisation Models
20(1)
2.3.2 Objectives and Scope
21(2)
2.3.3 Maintenance Standards
23(1)
2.3.4 Maintenance-related Operational Planning
23(2)
2.4 Economic View of CBM-based PM
25(2)
2.5 Risks in CBM Plan Implementation
27(5)
2.5.1 Technology
28(2)
2.5.2 People
30(1)
2.5.3 Processes
31(1)
2.5.4 Organizational Culture
32(1)
References
32(5)
3 The MANTIS Reference Architecture 37(56)
Csaba Hegedus
Patricia Dominguez Arroyo
Giovanni Di Orio
Jose Luis Flores
Karmele Intxausti
Erkki Jantunen
Felix Larrinaga
Pedro Malo
Istvan Moldovan
Soren Schneickert
3.1 Introduction
38(3)
3.1.1 MANTIS Platform Architecture Overview
40(1)
3.2 The MANTIS Reference Architecture
41(14)
3.2.1 Related Work and Technologies
42(7)
3.2.1.1 Reference architecture for the industrial internet of things
43(2)
3.2.1.2 Data processing in Lambda
45(2)
3.2.1.3 Maintenance based on MIMOSA
47(2)
3.2.2 Architecture Model and Components
49(6)
3.2.2.1 Edge tier
49(2)
3.2.2.2 Platform tier
51(3)
3.2.2.3 Enterprise tier
54(1)
3.2.2.4 Multi stakeholder interactions
55(1)
3.3 Data Management
55(7)
3.3.1 Data Quality Considerations
57(1)
3.3.2 Utilization of Cloud Technologies
57(1)
3.3.3 Data Storages in MANTIS
58(1)
3.3.4 Storage Types
59(3)
3.3.4.1 Big data file systems
60(1)
3.3.4.2 NoSQL databases
60(2)
3.4 Interoperability and Runtime System Properties
62(12)
3.4.1 Interoperability Reference Model
64(1)
3.4.2 MANTIS Interoperability Guidelines
65(9)
3.4.2.1 Conceptual and application integration
66(4)
3.4.2.2 System interaction model
70(1)
3.4.2.2.1 MANTIS event model
70(1)
3.4.2.2.2 Patterns for interactions
72(1)
3.4.2.3 Implementation integration
72(2)
3.5 Information Security Model
74(6)
3.5.1 Digital Identity
75(1)
3.5.2 Information Model
76(1)
3.5.3 Control Access Policy Specification
77(1)
3.5.4 Additional Requirements
78(2)
3.6 Architecture Evaluation
80(7)
3.6.1 Architecture Evaluation Goals, Benefits and Activities
80(1)
3.6.2 Concepts and Definitions
81(3)
3.6.3 Architecture Evaluation Types
84(3)
3.7 Conclusions
87(1)
References
88(5)
4 Monitoring of Critical Assets 93(52)
Michele Albano
Jose Manuel Abete
Iban Barrutia Inza
Vito Cucek
Karel De Brabandere
Ander Etxabe
Iosu Gabilondo
Zizek Guven
Mike Holenderski
Aitzol Iturrospe
Erkki Jantunen
Luis Lino Ferreira
Istvan Moldovan
Jon Olaizola
Eneko Saenz de Argandona
Babacar Sarr
Soren Schneickert
Rafael Socorro
Hans Sprong
Marjan Sterk
Raul Torrego
Godfried Webers
Achim Woyte
4.1 The Industrial Environment
94(2)
4.1.1 Extreme High/Low Temperatures (Ovens, Turbines, Refrigeration Chambers etc.)
94(1)
4.1.2 High Pressure Environments (Pneumatic/Hydraulic Systems, Oil Conductions, Tires etc.)
95(1)
4.1.3 Nuclear Radiation (Reactors or Close and Near-By Areas)
95(1)
4.1.4 Abrasive or Poisonous Environments
95(1)
4.1.5 Presence of Explosive Substances or Gases
96(1)
4.1.6 Rotating or Moving Parts
96(1)
4.2 Industrial Sensor Characteristics
96(14)
4.2.1 Passive Wireless Sensors
97(7)
4.2.2 Low-Cost Sensor Solution Research
104(1)
4.2.3 Soft Sensor Computational Trust
105(5)
4.3 Bandwidth Optimization for Maintenance
110(4)
4.3.1 Reduced Data Amount and Key Process Indicators (KPI)
111(1)
4.3.2 Advanced Modulation Schemes
112(1)
4.3.3 EM Wave Polarization Diversity
113(1)
4.4 Wireless Communication in Challenging Environments
114(10)
4.4.1 Design Methodology Basis
115(1)
4.4.2 Requirement and Challenge Identification
116(1)
4.4.3 Channel Measurement
117(1)
4.4.4 Interference Detection and Characterization
118(1)
4.4.5 PHY Design/Selection and Implementation
119(1)
4.4.5.1 Single/multi carrier
120(1)
4.4.5.2 High performance/low power
120(1)
4.4.6 MAC Design/Selection and Implementation
120(2)
4.4.6.1 Real-time/deterministic MACs
120(1)
4.4.6.2 Low-power MACs
121(1)
4.4.6.3 High level protocols for error mitigation
122(1)
4.4.7 System Validation
122(2)
4.4.7.1 Channel emulation
123(1)
4.4.7.2 Performance tests
123(1)
4.5 Intelligent Functions in the Sensors and Edge Servers
124(19)
4.5.1 Intelligent Function: Self-Calibration
127(4)
4.5.1.1 Practical application: Press machine torque sensor
128(1)
4.5.1.2 Practical application: X-ray tube cathode filament monitoring
128(2)
4.5.1.3 Practical application: Compressed air system
130(1)
4.5.2 Intelligent Function: Self-Testing (Self-Validating)
131(3)
4.5.2.1 Practical application: Oil tank system
131(1)
4.5.2.2 Practical application: Air and water flow and temperature sensor
132(1)
4.5.2.3 Practical application: Sensors for the photovoltaic plants
132(2)
4.5.3 Intelligent Function: Self-Diagnostics
134(1)
4.5.3.1 Practical application: Environmental parameters
134(1)
4.5.3.2 Practical application: Intelligent process performance indicator
135(1)
4.5.4 Smart Function: Formatting
135(1)
4.5.4.1 Practical applications: Compressed air system
136(1)
4.5.5 Smart Function: Enhancement
136(2)
4.5.5.1 Practical application: Air and water flow and temperature sensor
136(1)
4.5.5.2 Practical application: Railway strain sensor
137(1)
4.5.5.3 Practical application: Conventional energy production
138(1)
4.5.6 Smart Function: Transformation
138(2)
4.5.6.1 Practical application: Pressure drop estimation
139(1)
4.5.7 Smart Function: Fusion
140(8)
4.5.7.1 Practical application: Off-road and special purpose vehicle
140(1)
4.5.7.2 Practical application: MR magnet monitoring (e-Alert sensor)
140(2)
4.5.7.3 Practical application: MR critical components
142(1)
References
143(2)
5 Providing Proactiveness: Data Analysis Techniques Portfolios 145(94)
Alberto Sillitti
Javier Fernandez Anakabe
Jon Basurko
Paulien Dam
Hugo Ferreira
Susana Ferreiro
Jeroen Gijsbers
Sheng He
Csaba Hegedus
Mike Holenderski
Jan-Otto Hooghoudt
Inigo Lecuona
Urko Leturiondo
Quinten Marcelis
Istvan Moldovan
Emmanuel Okafor
Claudio Rebelo de Sa
Ricardo Romero
Babacar Sarr
Lambert Schomaker
Arvind Kumar Shekar
Carlos Soares
Hans Sprong
Soren Theodorsen
Tom Tourwe
Gorka Urchegui
Godfried Webers
Yi Yang
Andriy Zubaliy
Ekhi Zugasti
Urko Zurutuza
5.1 Introduction
146(2)
5.2 Root Cause Failure Analysis
148(11)
5.2.1 Theoretical Background
148(2)
5.2.2 Techniques Catalogue
150(9)
5.2.2.1 Support vector machine
151(1)
5.2.2.2 Limit and trend checking
151(1)
5.2.2.3 Partial least squares regression
152(1)
5.2.2.4 Bayesian network
153(2)
5.2.2.5 Artificial neural network
155(1)
5.2.2.6 K-means clustering
156(1)
5.2.2.7 Attribute oriented induction
157(1)
5.2.2.8 Hidden Markov model
158(1)
5.3 Remaining Useful Life Identification of Wearing Components
159(19)
5.3.1 Theoretical Background
159(1)
5.3.2 Techniques Catalogue
159(1)
5.3.3 Physical Modelling
160(9)
5.3.3.1 Industrial automation
160(2)
5.3.3.2 Fleet's maintenance
162(3)
5.3.3.3 Eolic systems
165(1)
5.3.3.4 Medical systems
166(3)
5.3.4 Artificial Neural Networks
169(3)
5.3.4.1 Deep neural networks
170(2)
5.3.5 Life Expectancy Models
172(5)
5.3.5.1 Time series analysis with attribute oriented induction
172(2)
5.3.5.2 Application to a pump
174(1)
5.3.5.3 Application to industrial forklifts
174(2)
5.3.5.4 Application to a gearbox
176(1)
5.3.6 Expert Systems
177(1)
5.4 Alerting and Prediction of Failures
178(38)
5.4.1 Theoretical Background
179(1)
5.4.2 Techniques Catalogue
179(37)
5.4.2.1 Nearest neighbour cold-deck imputation
180(1)
5.4.2.2 Support vector machine
181(3)
5.4.2.3 Linear discriminant analysis
184(1)
5.4.2.4 Pattern mining
185(2)
5.4.2.5 Temporal pattern mining
187(1)
5.4.2.6 Principal component analysis
188(2)
5.4.2.7 Hidden Semi-Markov model with Bayes classification
190(1)
5.4.2.8 Autoencoders
190(4)
5.4.2.9 Convolutional neural network with Gramian angular fields
194(5)
5.4.2.10 Recurrent neural network with long-short- term memory
199(2)
5.4.2.11 Change detection algorithm
201(3)
5.4.2.12 Fisher's exact test
204(1)
5.4.2.13 Bonferroni correction
205(1)
5.4.2.14 Hypothesis testing using univariate parametric statistics
205(5)
5.4.2.15 Hypothesis testing using univariate non-parametric statistics
210(4)
5.4.2.16 Mean, thresholds, normality tests
214(2)
5.5 Examples
216(16)
5.5.1 Usage Patterns/k-means
216(5)
5.5.1.1 Data analysis
217(2)
5.5.1.2 Results
219(1)
5.5.1.2.1 Plotting
219(1)
5.5.1.2.2 Replicability of results
220(1)
5.5.1.2.3 Summary of results
220(1)
5.5.2 Message Log Prediction Using LSTM
221(8)
5.5.2.1 Data interpretation and representation
222(1)
5.5.2.1.1 Litronic dataset
222(1)
5.5.2.1.2 Data representation
222(1)
5.5.2.2 Predictive models
223(1)
5.5.2.3 Results
224(1)
5.5.2.3.1 Evaluation of predictive models on small number of samples
224(1)
5.5.2.3.2 Evaluation of the ID-LSTM on OHE codes for more significant number of samples
226(2)
5.5.2.4 Discussion
228(1)
5.5.3 Metal-defect Classification
229(11)
5.5.3.1 Data collection
230(1)
5.5.3.2 Experiments
230(1)
5.5.3.3 Discussion
231(1)
References
232(7)
6 From KPI Dashboards to Advanced Visualization 239(72)
Goreti Marreiros
Peter Craamer
Inaki Garitano
Roberto Gonzalez
Manja Gorenc Novak
Ales Kancilija
Quinten Marcelis
Diogo Martinho
Antti Niemela
Franc Novak
Gregor Papa
Spela Poklukar
Isabel Praca
Ville Rauhala Daniel Reguera
Marjan Sterk
Gorka Urchegui
Roberto Uribeetxeberria
Juha Valtonen
Anja Vidmar
6.1 HMI Functional Specifications and Interaction Model
240(26)
6.1.1 HMI Design Principle Followed in the MANTIS Project
241(1)
6.1.2 MANTIS HMI Specifications
242(3)
6.1.2.1 Functional specifications
242(2)
6.1.2.2 General requirements
244(1)
6.1.3 MANTIS HMI Model
245(8)
6.1.3.1 Functionalities supporting high level tasks
247(6)
6.1.4 HMI Design Recommendations
253(3)
6.1.5 MANTIS Platform Interface Requirements
256(6)
6.1.5.1 Analysis of different interface types
256(3)
6.1.5.2 PC HMI
259(3)
6.1.6 Recommendations for Platform Selection
262(3)
6.1.6.1 Web-based HMI
264(1)
6.1.6.2 Responsive design
264(1)
6.1.7 Interface Design Recommendations for MANTIS Platform
265(1)
6.2 Adaptive Interfaces
266(14)
6.2.1 Context-awareness Approach
266(8)
6.2.1.1 Context and context awareness fundamentals
267(1)
6.2.1.2 Context lifecycle in context-aware applications
268(1)
6.2.1.3 Adaptive and intelligent HMIs
269(2)
6.2.1.4 Context awareness for fault prediction and maintenance optimisation
271(1)
6.2.1.5 Context awareness for maintenance personalisation and decision-making
272(1)
6.2.1.6 Context awareness approaches in a proactive collaborative maintenance platform
273(1)
6.2.2 Interaction Based/Driven Approach
274(6)
6.2.2.1 Introduction
275(1)
6.2.2.2 Navigation tracking and storage
276(1)
6.2.2.3 Action logs
277(3)
6.3 Advanced Data Visualizations for HMIs
280(12)
6.3.1 Visualization of Raw Data
280(5)
6.3.1.1 Visualisation tools overview
280(2)
6.3.1.2 Scenario 1: Kibana
282(2)
6.3.1.3 Scenario 2: Textual and graphical data representation
284(1)
6.3.2 Augmented and Virtual Reality
285(7)
6.3.2.1 Scenario 1: Automated vibration monitoring
291(1)
6.3.2.2 Scenario 2: Condition and incoming maintenance alert for plant operators
291(1)
6.4 Usability Testing Methodology for Industrial HMIs
292(13)
6.4.1 Human-system Interaction - Usability Standards
293(6)
6.4.2 Usability Testing Methodology for MANTIS
299(6)
References
305(6)
7 Success Stories on Real Pilots 311(186)
Rafael Socorro
Maria Aguirregabiria
Alp Akcay
Michele Albano
Mikel Anasagasti
Andoitz Aranburu
Mauro Barbieri
Iban Barrutia
Ansgar Bergmann
Karel De Brabandere
Marcel Boosten
Rui Casais
David Chico
Paolo Ciancarini
Paulien Dam
Giovanni Di Orio
Karel Eerland
Xabier Eguiluz
Salvatore Esposito
Catarina Felix
Javier Fernandez-Anakabe
Hugo Ferreira
Luis Lino Ferreira
Attila Franko
Iosu Gabilondo
Raquel Garcia
Jeroen Gijsbers
Mathias Gradler
Csaba Hegedus
Silvia Hernandez
Petri Helo
Mike Holenderski
Erkki Jantunen
Matti Kaija
Ales Kancilija
Felix Larrinaga Barrenechea
Pedro Malo
Goreti Marreiros
Eva Martinez
Diogo Martinho
Asif Mohammed
Mikel Mondragon
Istvan Moldovan
Antti Niemela
Jon Olaizola
Gregor Papa
Spela Poklukar
Isabel Praca
Stefano Primi
Verus Pronk
Vile Rauhala
Mario Riccardi
Rafael Rocha
Jon Rodriguez
Ricardo Romero
Antonio Ruggieri
Oier Sarasua
Eduardo Saiz
Veli-Pekka Salo
Monica Sanchez
Paolo Sannino
Babacar Sarr
Alberto Sillitti
Carlos Soares
Hans Sprong
Daan Terwee
Bas Tijsma
Tom Tourwe
Nayra Uranga
Lauri Valimaa
Juha Valtonen
Pal Varga
Alejandro Veiga
Mikel Viguera
Jaap van der Voet
Godfried Webers
Achim Woyte
Kees Wouters
Ekhi Zugasti
Urko Zurutuza
7.1 Shaver Production Plant
312(14)
7.1.1 Introduction to the Shaver Manufacturing Plant
313(1)
7.1.2 Scope and Logic
314(1)
7.1.3 Data Platform and Sensors
315(1)
7.1.4 Data Analytics and Maintenance Optimization
316(6)
7.1.4.1 Physical models and background
316(1)
7.1.4.2 Process monitoring with Principal Component Analysis & Hotelling's T2
317(2)
7.1.4.3 Product quality prediction with partial least squares regression
319(2)
7.1.4.4 Computational trust
321(1)
7.1.5 Visualization and HMI
322(2)
7.1.6 Maintenance and Inventory Optimization Results
324(1)
7.1.7 Conclusions
325(1)
7.2 Deploying an User Friendly Monitoring System
326(23)
7.2.1 Introduction to the Pultrusion Use Case
326(1)
7.2.2 Scope and Logic
326(3)
7.2.3 Data Platform and Sensors
329(6)
7.2.4 Human Machine Interfaces
335(2)
7.2.5 Maintenance Optimization and Validation Results
337(12)
7.2.5.1 Temperature control system located in the mixing area and in the storage area
337(2)
7.2.5.2 Cooling system for the injection chamber
339(6)
7.2.5.3 Compressed air system from pulling system
345(4)
7.3 Maintenance in Press Forming Machinery
349(47)
7.3.1 Introduction
350(1)
7.3.2 Scope and Logic
351(6)
7.3.2.1 Background information on the press machine
352(2)
7.3.2.2 Background information on the clutch brake component
354(3)
7.3.3 MANTIS Solutions for Press Machine
357(27)
7.3.3.1 Maintenance cloud platform
358(1)
7.3.3.1.1 Solution approach
359(1)
7.3.3.1.2 Results
361(1)
7.3.3.2 Torque measurement using wireless sensors
362(1)
7.3.3.2.1 Solution approach
363(1)
7.3.3.2.2 Results
366(3)
7.3.3.3 Head structural health monitoring
369(1)
7.3.3.3.1 Solution approach
369(1)
7.3.3.3.2 Results
372(2)
7.3.3.4 Bushing status measurement
374(1)
7.3.3.4.1 Solution approach
375(1)
7.3.3.4.2 Results
376(1)
7.3.3.5 Gears wear measurement
376(1)
7.3.3.5.1 Solution approach
376(1)
7.3.3.5.2 Results
377(1)
7.3.3.6 Press forces measurement
377(1)
7.3.3.6.1 Solution approach
378(1)
7.3.3.6.2 Results
380(4)
7.3.4 MANTIS Solutions for Clutch Brake
384(12)
7.3.4.1 Maintenance cloud platform by MGEP
384(1)
7.3.4.1.1 Background
384(1)
7.3.4.1.2 Solution approach
385(1)
7.3.4.1.3 Results
388(1)
7.3.4.2 Maintenance cloud platform by Tekniker
389(1)
7.3.4.2.1 Background
389(1)
7.3.4.2.2 Solution approach
390(1)
7.3.4.3 Friction material slippage
391(1)
7.3.4.3.1 Solution approach
391(1)
7.3.4.3.2 Results
392(1)
7.3.4.4 Brake spring degradation
393(1)
7.3.4.4.1 Solution approach
393(1)
7.3.4.4.2 Results
393(1)
7.3.4.5 Friction material wear
394(1)
7.3.4.5.1 Solution approach
394(1)
7.3.4.5.2 Results
394(1)
7.3.4.6 Piston chamber air leakage
395(1)
7.3.4.6.1 Solution approach
396(1)
7.3.4.6.2 Results
396(1)
7.4 Fault Detection for Metal Benders
396(19)
7.4.1 Introduction to Press Braking
397(2)
7.4.2 Design & Implementation
399(9)
7.4.2.1 Data collected by the machine's sensors
400(1)
7.4.2.2 Wired nodes: The oil sensor
401(1)
7.4.2.3 Wireless nodes: The accelerometer
402(1)
7.4.2.4 Edge gateway
402(1)
7.4.2.5 Communication in the cloud
403(2)
7.4.2.6 Components for data analysis
405(1)
7.4.2.7 Human machine interface
406(2)
7.4.3 Data Analysis
408(6)
7.4.3.1 Data pre-processing
409(1)
7.4.3.2 Failure detection
410(1)
7.4.3.2.1 Parametric models
410(1)
7.4.3.2.2 Non-parametric models
411(1)
7.4.3.2.3 Evaluation and interpretation
411(3)
7.4.4 Conclusions
414(1)
7.5 Off-road and Special Purpose Vehicles
415(14)
7.5.1 Introduction to the Use Case on Vehicles
415(1)
7.5.2 Scope and Logic
416(2)
7.5.3 Data Platform and Sensors
418(6)
7.5.4 Data Analytics and Maintenance Optimization
424(5)
7.5.5 Conclusions
429(1)
7.6 Proactive Maintenance of Railway Switches
429(13)
7.6.1 Introduction to Railway Monitoring
430(1)
7.6.2 Scope and Logic
430(1)
7.6.3 Data Processing
431(4)
7.6.4 Measurement System for Proactive Maintenance of Railway Switches
435(4)
7.6.4.1 New factors collected
437(1)
7.6.4.1.1 Platform level
438(1)
7.6.5 Data Visualization
439(3)
7.6.6 Conclusion
442(1)
7.7 Fault Detection for Photovoltaic Plants
442(5)
7.7.1 Introduction to PV Plants
442(1)
7.7.2 Practical Application of Root Cause Analysis in Photovoltaic Plants
443(4)
7.8 Conventional Energy Production
447(12)
7.8.1 Introduction to the Plant Under Study
448(1)
7.8.2 Scope and Logic
449(2)
7.8.3 Monitoring Rolling Element Bearings
451(2)
7.8.4 IoT-Ticket Platform
453(2)
7.8.5 nmas Measuring System
455(1)
7.8.6 Mantis Cloud Platform
455(4)
7.8.7 Data Analytics and Maintenance Optimization
459(1)
7.8.8 Conclusions
459(1)
7.9 Health Equipment Maintenance
459(35)
7.9.1 Introduction to Health Imaging Systems
460(4)
7.9.1.1 Introduction to magnetic resonance
461(2)
7.9.1.2 Introduction to IGT systems
463(1)
7.9.2 Data Platform
464(1)
7.9.3 Data Lake
465(1)
7.9.4 ETL Scripts
465(1)
7.9.5 Data Warehouse
466(2)
7.9.6 Sensors
468(2)
7.9.7 Analysis and Decision Making Functionalities
470(29)
7.9.7.1 Predictive model deployment and live scoring
470(1)
7.9.7.2 Log pattern finder data
471(1)
7.9.7.3 Data sources
471(1)
7.9.7.4 Inspect and normalize the data
472(1)
7.9.7.5 Data pre-processing
473(1)
7.9.7.6 Data representation
473(1)
7.9.7.7 Equivalent log patterns
474(1)
7.9.7.8 Log pattern selection problem
474(1)
7.9.7.9 Design decisions
475(1)
7.9.7.10 Output
475(1)
7.9.7.11 Failure prediction
476(2)
7.9.7.12 Physical modeling
478(5)
7.9.7.13 Maintenance and inventory optimization
483(1)
7.9.7.14 Model and analysis
483(5)
7.9.7.15 Results and insights
488(3)
7.9.7.16 Visualization and HMI
491(1)
7.9.7.17 E-Alert portal
492(1)
7.9.7.18 Remote monitoring dashboard
492(2)
7.9.7.19 Conclusions
494(1)
References
494(3)
8 Business Models: Proactive Monitoring and Maintenance 497(58)
Michel Inigo Ulloa
Peter Craamer
Salvatore Esposito
Carolina Mega Nino
Mario Riccardi
Antonio Ruggieri
Paolo Sannino
8.1 Maintenance Present and Future Trends
499(9)
8.1.1 Tools
502(3)
8.1.1.1 Total productive maintenance
502(1)
8.1.1.2 Root-cause analysis
502(1)
8.1.1.3 Reliability centered maintenance
503(1)
8.1.1.4 Improving operational reliability
503(1)
8.1.1.5 Criticality analysis
504(1)
8.1.1.6 Risk-based maintenance
504(1)
8.1.1.7 Maintenance optimization models
504(1)
8.1.1.8 Model-based condition monitoring
504(1)
8.1.2 Trends
505(3)
8.1.2.1 Servitization
505(1)
8.1.2.2 Degree of automation
506(1)
8.1.2.3 Top-down vs. bottom-up
507(1)
8.1.2.4 Smart products
508(1)
8.1.2.5 Machine learning
508(1)
8.2 Shift to a Proactive Maintenance Business Landscape
508(10)
8.2.1 Key Success Factors
511(7)
8.2.1.1 User experience
511(1)
8.2.1.2 Privacy
512(1)
8.2.1.3 Scalability
512(1)
8.2.1.4 Technical debt
513(1)
8.2.1.5 Skills
514(1)
8.2.1.6 Organizational structure
514(1)
8.2.1.7 Technology
514(4)
8.3 Proactive Maintenance Business Model
518(9)
8.3.1 Competitive Advantage for Asset Manufacturers
522(1)
8.3.2 Competitive Advantage for Asset Service Providers
522(1)
8.3.3 Competitive Advantage for Asset End Users
523(1)
8.3.4 Value Chains
523(1)
8.3.5 Main Technological and Non-technological Barriers/Obstacles for the Implementation
523(11)
8.3.5.1 Technological barriers
523(3)
8.3.5.2 Non-technological barriers
526(1)
8.4 From Business Model to Financial Projections
527(7)
8.5 Economic Tool to Evaluate Current and Future PMM Business Model
534(11)
8.5.1 Incomes Items
536(2)
8.5.2 Cost Items
538(4)
8.5.3 Schema of Economic Evaluation and Projection Report
542(3)
8.6 Railways Use-Case Financial Business Model
545(5)
8.6.1 Financial Business Benefits Within a Specific Railway Maintenance Solution
546(4)
8.7 Conclusions
550(2)
References
552(3)
9 The Future of Maintenance 555(14)
Lambert Schomaker
Michele Albano
Erkki Jantunen
Luis Lino Ferreira
9.1 Is it Cybernetic or Is it Human?
557(1)
9.2 Real-time Communication in Maintenance?
558(1)
9.3 How to Determine Granularity in Space and Time?
559(1)
9.4 Open or Closed Maintainability?
559(1)
9.5 Insourcing or Outsourcing?
560(1)
9.6 Explicit Modeling or Data-driven Pragmatics?
561(1)
9.7 How to Apply Virtual Reality and Augmented Reality?
561(2)
9.8 Service Robotics for Maintenance
563(1)
9.9 How will the Maintenance Practices Change
564(2)
9.10 Conclusion
566(1)
References
566(3)
Index 569(4)
About the Editors 573
Michele Albano, Polytechnic Institute of Porto, Portugal.

Erkki Jantunen, VTT Technical Research Centre of Finland Ltd., Finland.

Gregor Papa, Joef Stefan Institute, Slovenia.

Urko Zurutuza, Mondragon Unibertsitatea, Spain.
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