Prognostiziert durch Konzepte der Industrie 4.0 und den Cyber-Physischen-Systemen, können autonome Systeme zukünftig dynamisch auf Datenquellen in ihrer Umgebung zugreifen.
Während die gemeinsame Nutzung solcher Datenquellen ein enormes Performanzpotenzial bietet, stellt die benötigte Systemarchitektur vorherrschende Sicherheitsprozesse vor neue Herausforderungen.
Die vorliegende Arbeit motiviert zunächst, dass diese nur zur Laufzeit des Systems adressiert werden könne, bevor sie daraus zwei zentrale Ziele ableitet und verfolgt.
Zum einen wird ein Beschreibungsmodel für die Darstellung von Fehlercharakteristika gemeinsam genutzter Daten vorgestellt.
Dieses generische Fehlermodell erlaubt es zum anderen eine Sicherheitsanalyse zu definieren, die eine spezifische, dynamische Systemkomposition zur Laufzeit mit Hinblick auf die zu erwartenden Unsicherheiten bewerten kann.
Die als Region of Safety betitelte Analysestrategie erlaubt, in Kombination mit dem generischen Fehlermodell, die Sicherheit der auf gemeinsam genutzten Daten basierenden Kollisionsvermeidungsstrategie zweier Roboter noch zur Designzeit zu garantieren, obwohl die spezifischen Fehlercharakteristika der Daten erst zur Laufzeit bekannt werden.:List of Acronyms
List of Theorems
List of Definitions
List of Figures
List of Tables
1. Introduction – Safety in Future Smart Industries
1.1. The Example of Smart Warehouses
1.2. Functional Safety Standards
1.2.1. Overview of Functional Safety Standards
1.2.2. IEC 61508
1.3. Scope of this Thesis
1.3.1. Objectives
1.3.2. Contributions
1.3.3. Outline
1.4. Related Publications by the Author
1.5. Mathematical Notation
2. State of the Art
2.1. State of the Art in Run-Time Safety Assessment
2.1.1. Approaches at the Functional Level
2.1.2. Approaches at the Technical Level
2.1.3. Conclusions
2.2. State of the Art in Failure Modeling
2.2.1. The Definition of (Sensor) Failure Model
2.2.2. Interval-Based Failure Modeling
2.2.3. Distribution-Based Failure Modeling
2.2.4. Failure-Type-Based Failure Modeling
2.2.5. Conclusions
2.3. Conclusions from the State of the Art
3. Generic Failure Model
3.1. Defining the Generic Failure Model
3.1.1. Time- and Value-Correlated Random Distribution
3.1.2. A Failure Type’s Failure Amplitudes
3.1.3. A Failure Type’s State Function
3.1.4. Polynomial Representation of a Failure Type
3.1.5. Discussion on the Fulfillment of the Predefined Criteria
3.2. Converting a Generic Failure Model to an Interval
3.2.1. Converting a Time- and Value-Correlated Random Distribution
3.2.2. A Failure Type’s Interval
3.3. Processing Chain for Generating Generic Failure Models
3.3.1. Identifying Failure Types
3.3.2. Parameterizing Failure Types
3.3.3. Confidence Calculation
3.4. Exemplary Application to Artificial Failure Characteristics
3.4.1. Generating the Artificial Data Set – Manually Designing GFMs
3.4.2. Identifying Failure Types
3.4.3. Parameterizing Failure Types
3.4.4. Confidence Calculation
3.4.5. Comparison to State-of-the-Art Models
3.5. Summary
4. Region of Safety
4.1. Explicitly Modeling Uncertainties for Dynamically Composed Systems
4.2. Regions of Safety for Dynamically Composed Systems
4.2.1. Estimating Regions of Attraction in Presence of Uncertainty
4.2.2. Introducing the Concept of Region of Safety
4.2.3. Discussion on the Fulfillment of the Predefined Criteria
4.3. Evaluating the Concept of Region of Safety
4.3.1. Defining the Scenario and Considered Uncertainties
4.3.2. Designing a Control Lyapunov Function
4.3.3. Determining an Appropriate Value for λc
4.3.4. The Effect of Varying Sensor Failures on Regions of Safety
4.4. Summary
5. Evaluation and Integration
5.1. Multi-Robot Collision Avoidance
5.1.1. Assumptions
5.1.2. Design of the Circle and Navigation Scenarios
5.1.3. Kinematics
5.1.4. Control Policy
5.1.5. Intention Modeling by Model Uncertainty
5.1.6. Fusing Regions of Safety of Multiple Stability Points
5.2. Failure Modeling for Shared Data – A Marker Detection Failure Model
5.2.1. Data Acquisition
5.2.2. Failure Model Generation
5.2.3. Evaluating the Quality of the Failure Model
5.3. Safe Handling of Shared Data in a Collision Avoidance Strategy
5.3.1. Configuration for Region of Safety Estimation
5.3.2. Estimating Regions of Safety
5.3.3. Evaluation Using the Circle Scenario
5.3.4. Evaluation Using the Navigation Scenario
5.4. Summary
6. Conclusions and Future Work
6.1. Summary
6.2. Limitations and Future Work
6.2.1. Limitations and Future Work on the Generic Failure Model
6.2.2. Limitations and Future Work on Region of Safety
6.2.3. Future Work on Safety in Dynamically Composed Systems
Appendices
A. Defining Factors of Risk According to IEC 61508
B. Evaluation Results for the Identification Stage
C. Overview of Failure Amplitudes of Marker Detection Results
Bibliography / The concepts of Cyber-Physical-Systems and Industry 4.0 prognosticate autonomous systems to integrate sources of shared data dynamically at their run-time.
While this promises substantial increases in their performance, the openness of the required system architecture poses new challenges to processes guaranteeing their safety.
This thesis firstly motivates that these can be addressed only at their run-time, before it derives and pursues two corresponding goals.
Firstly, a model for describing failure characteristics of shared data is presented.
Secondly, this Generic Failure Model is built upon to define a run-time safety assessment methodology that enables analyzing dynamic system compositions integrating shared data with respect to the expected uncertainties at run-time.
This analysis strategy, entitled Region of Safety, allows in combination with the generic failure model to guarantee the safety of robots sharing position data for collision avoidance already at design-time, although specific failure characteristics become available only at run-time.:List of Acronyms
List of Theorems
List of Definitions
List of Figures
List of Tables
1. Introduction – Safety in Future Smart Industries
1.1. The Example of Smart Warehouses
1.2. Functional Safety Standards
1.2.1. Overview of Functional Safety Standards
1.2.2. IEC 61508
1.3. Scope of this Thesis
1.3.1. Objectives
1.3.2. Contributions
1.3.3. Outline
1.4. Related Publications by the Author
1.5. Mathematical Notation
2. State of the Art
2.1. State of the Art in Run-Time Safety Assessment
2.1.1. Approaches at the Functional Level
2.1.2. Approaches at the Technical Level
2.1.3. Conclusions
2.2. State of the Art in Failure Modeling
2.2.1. The Definition of (Sensor) Failure Model
2.2.2. Interval-Based Failure Modeling
2.2.3. Distribution-Based Failure Modeling
2.2.4. Failure-Type-Based Failure Modeling
2.2.5. Conclusions
2.3. Conclusions from the State of the Art
3. Generic Failure Model
3.1. Defining the Generic Failure Model
3.1.1. Time- and Value-Correlated Random Distribution
3.1.2. A Failure Type’s Failure Amplitudes
3.1.3. A Failure Type’s State Function
3.1.4. Polynomial Representation of a Failure Type
3.1.5. Discussion on the Fulfillment of the Predefined Criteria
3.2. Converting a Generic Failure Model to an Interval
3.2.1. Converting a Time- and Value-Correlated Random Distribution
3.2.2. A Failure Type’s Interval
3.3. Processing Chain for Generating Generic Failure Models
3.3.1. Identifying Failure Types
3.3.2. Parameterizing Failure Types
3.3.3. Confidence Calculation
3.4. Exemplary Application to Artificial Failure Characteristics
3.4.1. Generating the Artificial Data Set – Manually Designing GFMs
3.4.2. Identifying Failure Types
3.4.3. Parameterizing Failure Types
3.4.4. Confidence Calculation
3.4.5. Comparison to State-of-the-Art Models
3.5. Summary
4. Region of Safety
4.1. Explicitly Modeling Uncertainties for Dynamically Composed Systems
4.2. Regions of Safety for Dynamically Composed Systems
4.2.1. Estimating Regions of Attraction in Presence of Uncertainty
4.2.2. Introducing the Concept of Region of Safety
4.2.3. Discussion on the Fulfillment of the Predefined Criteria
4.3. Evaluating the Concept of Region of Safety
4.3.1. Defining the Scenario and Considered Uncertainties
4.3.2. Designing a Control Lyapunov Function
4.3.3. Determining an Appropriate Value for λc
4.3.4. The Effect of Varying Sensor Failures on Regions of Safety
4.4. Summary
5. Evaluation and Integration
5.1. Multi-Robot Collision Avoidance
5.1.1. Assumptions
5.1.2. Design of the Circle and Navigation Scenarios
5.1.3. Kinematics
5.1.4. Control Policy
5.1.5. Intention Modeling by Model Uncertainty
5.1.6. Fusing Regions of Safety of Multiple Stability Points
5.2. Failure Modeling for Shared Data – A Marker Detection Failure Model
5.2.1. Data Acquisition
5.2.2. Failure Model Generation
5.2.3. Evaluating the Quality of the Failure Model
5.3. Safe Handling of Shared Data in a Collision Avoidance Strategy
5.3.1. Configuration for Region of Safety Estimation
5.3.2. Estimating Regions of Safety
5.3.3. Evaluation Using the Circle Scenario
5.3.4. Evaluation Using the Navigation Scenario
5.4. Summary
6. Conclusions and Future Work
6.1. Summary
6.2. Limitations and Future Work
6.2.1. Limitations and Future Work on the Generic Failure Model
6.2.2. Limitations and Future Work on Region of Safety
6.2.3. Future Work on Safety in Dynamically Composed Systems
Appendices
A. Defining Factors of Risk According to IEC 61508
B. Evaluation Results for the Identification Stage
C. Overview of Failure Amplitudes of Marker Detection Results
Bibliography
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:80305 |
| Date | 16 September 2022 |
| Creators | Jäger, Georg |
| Contributors | Zug, Sebastian, Casimiro, António, Kruse, Rudolf, Technische Universität Bergakademie Freiberg |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0037 seconds