The MANTIS Book : Cyber Physical System Based Proactive Collaborative Maintenance

The MANTIS Book Cyber Physical System Based Proactive Collaborative Maintenance

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, preven...

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Detalles Bibliográficos
Otros Autores: Albano, Michele, editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Gistrup, Denmark : River Publishers [2019]
Edición:First edition
Colección:River Publishers series in automation, control and robotics.
Materias:
Cooperating objects (Computer systems)
Data mining.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009703334506719
Tabla de Contenidos:
  • Front Cover
  • Half Title Page
  • RIVER PUBLISHERS SERIES IN AUTOMATION, CONTROL AND ROBOTICS
  • Title Page
  • Copyright Page
  • Dedication Page
  • Contents
  • Acknowledgments
  • Foreword
  • List of Contributors
  • List of Figures
  • List of Tables
  • List of Abbreviations
  • Chapter 1 - Introduction
  • 1.1 Maintenance Today
  • 1.2 The Path to Proactive Maintenance
  • 1.3 Why to Read this Book
  • References
  • Chapter 2 - Business Drivers of a Collaborative, Proactive Maintenance Solution
  • 2.1 Introduction
  • 2.1.1 CBM-based PM in Industry
  • 2.1.2 CBM-based PM in Service Business
  • 2.1.3 Life Cycle Cost and Overall Equipment Effectiveness
  • 2.1.4 Integrating IoT with Old Equipment
  • 2.1.5 CBM Strategy as a Maintenance Business Driver
  • 2.2 Optimization of Maintenance Costs
  • 2.3 Business Drivers for Collaborative Proactive Maintenance
  • 2.3.1 Maintenance Optimisation Models
  • 2.3.2 Objectives and Scope
  • 2.3.3 Maintenance Standards
  • 2.3.4 Maintenance-related Operational Planning
  • 2.4 Economic View of CBM-based PM
  • 2.5 Risks in CBM Plan Implementation
  • 2.5.1 Technology
  • 2.5.2 People
  • 2.5.3 Processes
  • 2.5.4 Organizational Culture
  • References
  • Chapter 3 - The MANTIS Reference Architecture
  • 3.1 Introduction
  • 3.1.1 MANTIS Platform Architecture Overview
  • 3.2 The MANTIS Reference Architecture
  • 3.2.1 Related Work and Technologies
  • 3.2.1.1 Reference architecture for the industrial internet of things
  • 3.2.1.2 Data processing in Lambda
  • 3.2.1.3 Maintenance based on MIMOSA
  • 3.2.2 Architecture Model and Components
  • 3.2.2.1 Edge tier
  • 3.2.2.2 Platform tier
  • 3.2.2.3 Enterprise tier
  • 3.2.2.4 Multi stakeholder interactions
  • 3.3 Data Management
  • 3.3.1 Data Quality Considerations
  • 3.3.2 Utilization of Cloud Technologies
  • 3.3.3 Data Storages in MANTIS
  • 3.3.4 Storage Types
  • 3.3.4.1 Big data file systems.
  • 3.3.4.2 NoSQL databases
  • 3.4 Interoperability and Runtime System Properties
  • 3.4.1 Interoperability Reference Model
  • 3.4.2 MANTIS Interoperability Guidelines
  • 3.4.2.1 Conceptual and application integration
  • 3.4.2.2 System interaction model
  • 3.4.2.2.1 MANTIS event model
  • 3.4.2.2.2 Patterns for interactions
  • 3.4.2.3 Implementation integration
  • 3.5 Information Security Model
  • 3.5.1 Digital Identity
  • 3.5.2 Information Mo
  • 3.5.3 Control Access Policy Specification
  • 3.5.4 Additional Requirements
  • 3.6 Architecture Evaluation
  • 3.6.1 Architecture Evaluation Goals, Benefits and Activities
  • 3.6.2 Concepts and Definitions
  • 3.6.3 Architecture Evaluation Types
  • 3.7 Conclusions
  • References
  • Chapter 4 - Monitoring of Critical Assets
  • 4.1 The Industrial Environment
  • 4.1.1 Extreme High/Low Temperatures (Ovens, Turbines, Refrigeration Chambers etc.)
  • 4.1.2 High Pressure Environments (Pneumatic/Hydraulic Systems, Oil Conductions, Tires etc.)
  • 4.1.3 Nuclear Radiation (Reactors or Close and Near-By Areas)
  • 4.1.4 Abrasive or Poisonous Environments
  • 4.1.5 Presence of Explosive Substances or Gases
  • 4.1.6 Rotating or Moving Parts
  • 4.2 Industrial Sensor Characteristics
  • 4.2.1 Passive Wireless Sensors
  • 4.2.2 Low-Cost Sensor Solution Research
  • 4.2.3 Soft Sensor Computational Trust
  • 4.3 Bandwidth Optimization for Maintenance
  • 4.3.1 Reduced Data Amount and Key Process Indicators (KPI)
  • 4.3.2 Advanced Modulation Schemes
  • 4.3.3 EM Wave Polarization Diversity
  • 4.4 Wireless Communication in Challenging Environments
  • 4.4.1 Design Methodology Basis
  • 4.4.2 Requirement and Challenge Identification
  • 4.4.3 Channel Measurement
  • 4.4.4 Interference Detection and Characterization
  • 4.4.5 PHY Design/Selection and Implementation
  • 4.4.5.1 Single/multi carrier
  • 4.4.5.2 High performance/low power.
  • 4.4.6 MAC Design/Selection and Implementation
  • 4.4.6.1 Real-time/deterministic MACs
  • 4.4.6.2 Low-power MACs
  • 4.4.6.3 High level protocols for error mitigation
  • 4.4.7 System Validation
  • 4.4.7.1 Channel emulation
  • 4.4.7.2 Performance tests
  • 4.5 Intelligent Functions in the Sensors and Edge Servers
  • 4.5.1 Intelligent Function: Self-Calibration
  • 4.5.1.1 Practical application: Press machine torque sensor
  • 4.5.1.2 Practical application: X-ray tube cathode filament monitoring
  • 4.5.1.3 Practical application: Compressed air system
  • 4.5.2 Intelligent Function: Self-Testing (Self-Validating)
  • 4.5.2.1 Practical application: Oil tank system
  • 4.5.2.2 Practical application: Air and water flow and temperature sensor
  • 4.5.2.3 Practical application: Sensors for the photovoltaic plants
  • 4.5.3 Intelligent Function: Self-Diagnostics
  • 4.5.3.1 Practical application: Environmental parameters
  • 4.5.3.2 Practical application: Intelligent process performance indicator
  • 4.5.4 Smart Function: Formatting
  • 4.5.4.1 Practical applications: Compressed air system
  • 4.5.5 Smart Function: Enhancement
  • 4.5.5.1 Practical application: Air and water flow and temperature
  • 4.5.5.2 Practical application: Railway strain sensor
  • 4.5.5.3 Practical application: Conventional energy production
  • 4.5.6 Smart Function: Transformation
  • 4.5.6.1 Practical application: Pressure drop estimation
  • 4.5.7 Smart Function: Fusion
  • 4.5.7.1 Practical application: Off-road and special purpose vehicle
  • 4.5.7.2 Practical application: MR magnet monitoring (e-Alert sensor)
  • 4.5.7.3 Practical application: MR critical components
  • References
  • Chapter 5 - Providing Proactiveness: Data Analysis Techniques Portfolios
  • 5.1 Introduction
  • 5.2 Root Cause Failure Analysis
  • 5.2.1 Theoretical Background
  • 5.2.2 Techniques Catalogue
  • 5.2.2.1 Support vector machine.
  • 5.2.2.2 Limit and trend checking
  • 5.2.2.3 Partial least squares regression
  • 5.2.2.4 Bayesian network
  • 5.2.2.5 Artificial neural network
  • 5.2.2.6 K-means clustering
  • 5.2.2.7 Attribute oriented induction
  • 5.2.2.8 Hidden Markov model
  • 5.3 Remaining Useful Life Identification of Wearing Components
  • 5.3.1 Theoretical Background
  • 5.3.2 Techniques Catalogue
  • 5.3.3 Physical Modelling
  • 5.3.3.1 Industrial automation
  • 5.3.3.2 Fleet's maintenance
  • 5.3.3.3 Eolic systems
  • 5.3.3.4 Medical systems
  • 5.3.4 Artificial Neural Networks
  • 5.3.4.1 Deep neural networks
  • 5.3.5 Life Expectancy Models
  • 5.3.5.1 Time series analysis with attribute oriented induction
  • 5.3.5.2 Application to a pump
  • 5.3.5.3 Application to industrial forklifts
  • 5.3.5.4 Application to a gearbox
  • 5.3.6 Expert Systems
  • 5.4 Alerting and Prediction of Failures
  • 5.4.1 Theoretical Background
  • 5.4.2 Techniques Catalogue
  • 5.4.2.1 Nearest neighbour cold-deck imputation
  • 5.4.2.2 Support vector machine
  • 5.4.2.3 Linear discriminant analysis
  • 5.4.2.4 Pattern mining
  • 5.4.2.5 Temporal pattern mining
  • 5.4.2.6 Principal component analysis
  • 5.4.2.7 Hidden Semi-Markov model with Bayes classification
  • 5.4.2.8 Autoencoders
  • 5.4.2.9 Convolutional neural network with Gramian angular fields
  • 5.4.2.10 Recurrent neural network with long-short-term memory
  • 5.4.2.11 Change detection algorithm
  • 5.4.2.12 Fisher's exact test
  • 5.4.2.13 Bonferroni correction
  • 5.4.2.14 Hypothesis testing using univariate parametric statistics
  • 5.4.2.15 Hypothesis testing using univariate non-parametricstatistics
  • 5.4.2.16 Mean, thresholds, normality tests
  • 5.5 Examples
  • 5.5.1 Usage Patterns/k-means
  • 5.5.1.1 Data analysis
  • 5.5.1.2 Results
  • 5.5.1.2.1 Plotting
  • 5.5.1.2.2 Replicability of results
  • 5.5.1.2.3 Summary of results.
  • 5.5.2 Message Log Prediction Using LSTM
  • 5.5.2.1 Data interpretation and representation
  • 5.5.2.1.1 Litronic dataset
  • 5.5.2.1.2 Data representation
  • 5.5.2.2 Predictive models
  • 5.5.2.3 Results
  • 5.5.2.3.1 Evaluation of predictive models on small number of samples
  • 5.5.2.3.2 Evaluation of the ID-LSTM on OHE codes for more significant number of samples
  • 5.5.2.4 Discussion
  • 5.5.3 Metal-defect Classification
  • 5.5.3.1 Data collection
  • 5.5.3.2 Experiments
  • 5.5.3.3 Discussion
  • References
  • Chapter 6 - From KPI Dashboards to Advanced Visualization
  • 6.1 HMI Functional Specifications and Interaction Model
  • 6.1.1 HMI Design Principle Followed in the MANTIS Project
  • 6.1.2 MANTIS HMI Specifications
  • 6.1.2.1 Functional specifications
  • 6.1.2.2 General requirements
  • 6.1.3 MANTIS HMI Model
  • 6.1.3.1 Functionalities supporting high level tasks
  • 6.1.4 HMI Design Recommendations
  • 6.1.5 MANTIS Platform Interface Requirements
  • 6.1.5.1 Analysis of different interface types
  • 6.1.5.2 PC HMI
  • 6.1.6 Recommendations for Platform Selection
  • 6.1.6.1 Web-based HMI
  • 6.1.6.2 Responsive design
  • 6.1.7 Interface Design Recommendations for MANTIS Platform
  • 6.2 Adaptive Interfaces
  • 6.2.1 Context-awareness Approach
  • 6.2.1.1 Context and context awareness fundamentals
  • 6.2.1.2 Context lifecycle in context-aware applications
  • 6.2.1.3 Adaptive and intelligent HMIs
  • 6.2.1.4 Context awareness for fault prediction and maintenance optimisation
  • 6.2.1.5 Context awareness for maintenance personalisation and decision-making
  • 6.2.1.6 Context awareness approaches in a proactive collaborative maintenance platform
  • 6.2.2 Interaction Based/Driven Approach
  • 6.2.2.1 Introduction
  • 6.2.2.2 Navigation tracking and storage
  • 6.2.2.3 Action logs
  • 6.3 Advanced Data Visualizations for HMIs
  • 6.3.1 Visualization of Raw Data.
  • 6.3.1.1 Visualisation tools overview.

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