Computational Augmentation of Model Based System Engineering Supporting Mechatronic System Model Development with AI Technologies Mechatronics Research Centre Faculty of Technology De Montfort University, United Kingdom This thesis is submitted in partial fulfilment of the requirements of De Montfort University for the award of Doctor of Philosophy December, 2019 By: Jugraj Singh Table of Contents Introduction .......................................................................................................................... 11 1.1. Introduction ................................................................................................................................ 11 1.2. Motivation and Background....................................................................................................... 12 1.2.1. Research Gap ...................................................................................................................... 15 1.3. Research Scope .......................................................................................................................... 18 1.4. Research Aims and Objectives .................................................................................................. 20 1.5. Chapters Overview ..................................................................................................................... 21 Literature review .................................................................................................................. 22 2.1. System Engineering and MBSE ................................................................................................. 22 2.1.1. Brief MBSE Methodologies Review .................................................................................. 24 2.1.2. Key MBSE Tasks Amenable to Intelligent Computational Support ................................... 26 2.2. Computational Augmentation of MBSE .................................................................................... 27 2.2.1. Use of Semantic Web Technologies for MBSE Support .................................................... 29 2.2.2. Patterns in System Engineering .......................................................................................... 31 2.2.3. Requirement Formalisation ................................................................................................. 33 2.3. Conceptual Physical Architecture Generation ........................................................................... 35 2.3.1. Design Synthesis ................................................................................................................. 36 2.3.2. Design Configuration .......................................................................................................... 42 2.4. Supervised and Unsupervised Machine Learning ...................................................................... 43 2.4.1. Feature Representation Learning with Auto-encoders ........................................................ 45 Problem Formulation of System Model Development ......................................................... 48 3.1. Illustrative Example ................................................................................................................... 49 3.2. Formalisation of Concepts Common across Stages ................................................................... 54 3.3. Formalisation of Key Concepts of Requirement Definition Stage ............................................ 57 3.4. Logical Architecture Construction w.r.t Pre-Defined Use Case Scenarios ................................ 59 3.5. Representing Design Solutions at High Level ........................................................................... 60 3.6. Analyses of Selected Design Solutions at System Level ........................................................... 62 3.7. Proposed Framework ................................................................................................................. 65 1 Jugraj Singh 3.7.1. Representing and storing knowledge .................................................................................. 67 3.7.2. Retrieval of physical descriptive subsystems ...................................................................... 68 3.7.3. Retrieval and classification of simulation models .............................................................. 69 3.8. System Application Domain (SAD)s and their Hierarchy ......................................................... 72 Aiding System Model Development using Logic ................................................................ 75 4.1.1. System Domain Meta-Model .............................................................................................. 79 4.1.2. Formalisation of Textual Requirements .............................................................................. 81 4.2. Logical to Physical Architecture Elaboration ............................................................................ 83 4.2.1. Retrieving Generic Physical Components by Specialising Meta-Model ............................ 85 4.2.2. Domain Specific Constraint Formulation for Mechanical Components Retrieval ............. 90 4.3. Results and Discussion .............................................................................................................. 98 Behaviour Based Design Storage and Retrieval ................................................................. 101 5.1. Motivational Example .............................................................................................................. 102 5.2. Method ..................................................................................................................................... 104 5.3. Representing Design Behaviour Data for Applying Machine Learning .................................. 105 5.3.1. Train and Test Data for Experiments ................................................................................ 106 5.4. Clustering of Electric Circuits.................................................................................................. 107 5.5. Discussion ................................................................................................................................ 118 5.6. Conclusion ............................................................................................................................... 119 5.7. Bridging Data based Design Retrieval with Logic based Descriptive Model Retrieval .......... 121 Discussion and Future Work .............................................................................................. 122 6.1. Knowledge Contributions Overview ....................................................................................... 122 6.2. Knowledge Contribution based on Set Objectives................................................................... 122 6.3. Scalability of Proposed Framework ......................................................................................... 124 6.4. Future Work ............................................................................................................................. 125 References .......................................................................................................................... 127 Appendix ............................................................................................................................ 137 8.1. Comparison Between Auto-encoder Methods for Electrical Circuit Clustering ...................... 137 8.1.1. Cluster with difference of R2 and with same source ........................................................ 137 2 Jugraj Singh 8.1.2. Circuits with similar topology irrespective of source type ............................................... 142 8.1.3. Circuits with same source type and similar topology (R2 difference & L/C swap) ......... 147 8.2. Implementation of 2-D Kinematic Framework in Alloy Analyser .......................................... 152 8.3. List of Manually Designed Circuits ......................................................................................... 170 8.4. MBSE Methodologies Overview ............................................................................................. 175 8.4.1. Harmony SE Methodology ............................................................................................... 175 8.4.2. Object Oriented System Engineering Methodology ......................................................... 177 8.4.3. Vitech MBSE Methodology .............................................................................................. 179 8.4.4. ARCADIA (Architectural Analysis and Design Integrated Approach) ............................ 180 8.5. Design Patterns Representation and Retrieval Techniques ...................................................... 182 8.6. Python Code Implementing MMD-VAE with Tensorflow ..................................................... 182 TABLE OF FIGURES Figure 1-1 Framework of processes constituting product lifecycle as specified by ISO/IEC 15288 and ANSI/EIA 632 standards [33] ............................................................................................................... 19 Figure 2-1 ISO 15288 system lifecycle [33] ......................................................................................... 23 Figure 2-2 Information Model for Model Driven System Design [42] ................................................ 23 Figure 2-3 Composite