Artificial Intelligence for Digitising Industry: Applications

Front Cover
Artificial Intelligence for Digitising Industry Applications
Dedication
Acknowledgement
Contents
Preface
Editors Biography
List of Figures
List of Tables
List of Abbreviations
Section
1 AI Automotive
Abstract
1.0 AI Reshaping the Automotive Industry
1.0.1 Introduction and Background
1.0.2 AI Developments and Future Trends for AI Technologies
1.0.3 AI-Based Applications
Acknowledgements
References
1.1 AI for Inbound Logistics Optimisation in Automotive Industry
Abstract
1.1.1 Introduction and Background
1.1.2 Requirements – User Journeys
1.1.3 Data Flow Principles and Architecture of the MPDSS
1.1.4 Preliminary Analysis of Data and Dataset
1.1.4.1 Data Pre-processing and Visualisation
1.1.4.2 Classification Models
1.1.5 Conclusion
Acknowledgements
References
1.2 State of Health Estimation using a Temporal Convolutional Network for an Efficient Use of Retired Electric Vehicle Batteries within Second-Life Applications
Abstract
1.2.1 Retired Electric Vehicle Batteries for Second-Life Applications
1.2.2 State of Health of Lithium-Ion Batteries
1.2.3 Data Measurement Using the Open-Source BMS foxBMS
1.2.4 Temporal Convolutional Neural Network for State of Health Prediction
1.2.4.1 Causal Convolutions and Receptive Field
1.2.4.2 Dilated Convolutions
1.2.4.3 Residual Block
1.2.5 Results
1.2.6 Conclusion
Acknowledgements
References
1.3 Optimising Trajectories in Simulations with Deep Reinforcement Learning for Industrial Robots in Automotive Manufacturing
Abstract
1.3.1 Introduction
1.3.2 Background
1.3.3 Methodology
1.3.4 Conclusion and Outlook
Acknowledgements
References
1.4 Foundations of Real Time Predictive Maintenance with Root Cause Analysis
Abstract
1.4.1 Introduction and Background
1.4.2 Foundations
1.4.2.1 Model-based Diagnosis
1.4.2.2 Machine Learning Based Diagnosis
1.4.2.3 Artificial Neural Networks for Diagnostics
1.4.3 Related Research
1.4.4 Conclusion
Acknowledgements
References
1.5 Real-Time Predictive Maintenance – Model-Based, Simulation-Based and Machine Learning Based Diagnosis
Abstract
1.5.1 Introduction and Background
1.5.2 Application of Diagnosis Systems Based on Simplified DC e-Motor Model
1.5.2.1 Simplified DC e-Motor Model With Fault Injection
Capabilities
1.5.2.2 Model-based Diagnosis for Simplified DC e-Motor
1.5.2.3 Simulation-Based Diagnosis for Simplified DC e-Motor
1.5.2.4 Machine Learning for Diagnosis of Simplified DC e-Motor
1.5.2.5 Comparisons and Limitations
1.5.3 Conclusion
Acknowledgements
References
1.6 Real-Time Predictive Maintenance – Artificial Neural Network Based Diagnosis
Abstract
1.6.1 Introduction and Background
1.6.1.1 AI-based Diagnosis of E-motors
1.6.1.2 Artificial Intelligence in Vibration Diagnosis
1.6.2 Artificial Neural Network for e-Motor Diagnosis
1.6.2.1 Acausal e-Motor Model with Faults Injection Capability
1.6.2.2 Artificial Neural Network for Inter-turn Short Circuit
Detection
1.6.2.2.1 Selection of suitable condition indicator for fault
detection
1.6.2.2.2 Network structure selection
1.6.2.2.3 Data pre-processing
1.6.2.2.4 Preparation of datasets
1.6.2.2.5 MNN training
1.6.2.2.6 MNN validation
1.6.2.2.7 MNN deployment
1.6.3 Artificial Neural Network based Vibration Diagnosis
1.6.3.1 Vibration Diagnosis of Rotating Machines
1.6.3.2 AI Approaches in Vibration Diagnosis
1.6.3.3 MLP implementable in device at the edge
1.6.4 Conclusion
Acknowledgements
References
Section
2 AI Semiconductor
2.0 AI in Semiconductor Industry
Abstract
2.0.1 Introduction and Background
2.0.2 AI Developments in Semiconductor Industry
2.0.3 Future Trends for AI Technologies and Applications in Semiconductor Industry
2.0.4 AI-Based Applications
Acknowledgements
References
2.1 AI-Based Knowledge Management System for Risk Assessment and Root Cause Analysis in Semiconductor Industry
Abstract
2.1.1 Introduction and Background
2.1.2 Research Areas
2.1.2.1 FMEA and FMEA Consistency Improvement
2.1.2.2 Causal Information Extracting from Free Text
2.1.2.3 Failure Analysis Process, Failure Analysis Reports, and
Ontologies
2.1.2.4 Knowledge Representation
2.1.2.5 Refinement Algorithm
2.1.3 Reflections
2.1.4 Conclusion
Acknowledgments
References
2.2 Efficient Deep Learning Approach for Fault Detection in the Semiconductor Industry
Abstract
2.2.1 Motivation: The Wafer Fault Classification Problem
2.2.2 Related Works
2.2.3 Target Platform Requirements
2.2.4 HW/SW System and Methodology
2.2.4.1 Industrial HW/SW System for On-Device Inference
2.2.4.2 Neural Network Building and Training Using N2D2
2.2.4.3 Neural Network Export and FPGA Implementation Used
for Inference
2.2.5 Conclusion
Acknowledgements
References
2.3 Towards Fully Automated Verification of Semiconductor Technologies
Abstract
2.3.1 Introduction
2.3.2 Background: Interpreting Semiconductor Technologies
2.3.2.1 Methodology: The Integrated Analysis Process
2.3.2.2 Example Analysis: From the Image to the Feature
Extraction
2.3.3 Conclusion
Acknowledgements
References
2.4 Automated Anomaly Detection Through Assembly and Packaging Process
Abstract
2.4.1 Introduction and Background
2.4.2 Dataset Description and Defect Types
2.4.3 Methodology
2.4.3.1 Anomaly Detection
2.4.3.2 Pseudo Anomaly Detection
2.4.3.3 Convolutional Neural Networks
2.4.4 Results and Discussion
2.4.5 Conclusion and Outlooks
Acknowledgment
References
Section 3
AI Industrial Machinery
3.0 AI in Industrial Machinery
Abstract
3.0.1 Introduction and Background
3.0.2 AI Developments and Future Trends in Industrial Machinery
3.0.3 AI-based Applications
Acknowledgements
References
3.1 AI-Powered Collision Avoidance Safety System for Industrial Woodworking Machinery
Abstract
3.1.1 Introduction and Background
3.1.2 Review of Industrial-level Methods for Edge DNNs
3.1.2.1 Compression Techniques
3.1.2.2 Popular Frameworks and Tools
3.1.3 Materials and Methods
3.1.3.1 System Architecture
3.1.3.2 Dataset Collection
3.1.3.3 Detection Methods
3.1.3.4 Continual Learning Setup
3.1.4 Experimental Results
3.1.4.1 Evaluation Metrics
3.1.4.2 Continual Learning Scenario
3.1.4.3 Robustness Against Quantization
3.1.4.4 Latency, Energy and Memory Footprint on STM32H743ZI
3.1.5 Conclusion
Acknowledgements
References
3.2 Construction of a Smart Vision-Guided Robot System for Manipulation in a Dynamic Environment
Abstract
3.2.1 Introduction and Background
3.2.2 Challenges of Enabling Robots to “See”
3.2.2.1 Modularity
3.2.2.2 Operability
3.2.2.3 Computer Vision Algorithms
3.2.2.4 Validation of Algorithms
3.2.3 Requirements
3.2.4 Proposed Solution
3.2.4.1 Hardware and Interface Components
3.2.4.1.1 Robot Interface
3.2.4.1.2 Industrial Robot
3.2.4.1.3 3D Cameras
3.2.4.1.4 Deep Edge Device
3.2.4.2 Software Components
3.2.4.2.1 Computer Vision Algorithms
3.2.4.2.2 Synthetic Data Generation
3.2.4.2.3 Object 3D Reconstruction
3.2.4.2.4 Validation Framework
3.2.4.2.5 Robot Control
3.2.4.3 Hardware/Software Partitioning
3.2.5 Demonstrator Setup and Initial Results
3.2.6 Conclusion and Future Work
Acknowledgements
References
3.3 Radar-Based Human-Robot Interfaces
Abstract
3.3.1 Introduction and Background
3.3.2 Gesture Recognition Using a Machine Learning Approach
3.3.2.1 Concept and Experimental Setup
3.3.2.2 Inference Pipeline, Training Algorithm
Transmit Part
Receive Part
3.3.2.3 Data Recording and Results
Machine Learning Detector Statistics
Machine Learning Real Time Inference
3.3.3 Gesture Recognition Using a Spiking Neural Network
3.3.3.1 Concept and Experimental Setup
3.3.3.2 Inference Pipeline, Training Algorithm
3.3.3.3 Data Recording and Results
3.3.4 Proof of Concept Demonstration
3.3.5 Comparison and Conclusion
Acknowledgements
References
3.4 Touch Identification on Sensitive Robot Skin Using Time Domain Reflectometry and Machine Learning Methods
Abstract
3.4.1 Introduction and Background
3.4.2 State of the Art
3.4.3 Problem Definition
3.4.4 Concepts and Methods
3.4.5 Proof of Concept of the Novel Sensor System
3.4.5.1 Experimental Acquisition of Training Data
3.4.5.2 Training Procedure
3.4.6 Results
3.4.7 Conclusions
Acknowledgements
References
Section 4
AI Food and Beverage
4.0 AI in Food and Beverage Industry
Abstract
4.0.1 Introduction and Background
4.0.2 AI Developments in Food and Beverage Industry
4.0.3 Future Trends for AI Technologies and Applications
4.0.4 AI-Based Applications
Acknowledgements
References
4.1 Innovative Vineyards Environmental Monitoring System Using Deep Edge AI
Abstract
4.1.1 Introduction
4.1.2 Related Work
4.1.3 Edge Intelligence
4.1.4 Communication Technology – LoRaWAN
4.1.5 Environmental Monitoring System
4.1.6 Conclusion
Acknowledgements
References
4.2 AI-Driven Yield Estimation Using an Autonomous Robot for Data Acquisition
Abstract
4.2.1 Introduction
4.2.2 Artificial Intelligence for Grape Detection
4.2.3 Towards an Automated Protocol for Yield Estimation
4.2.4 Assessing the Vine Vitality Using an Embarked LiDAR
4.2.5 Conclusions
Acknowledgements
References
4.3 AI-Based Quality Control System at the Pressing Stages of the Champagne Production
Abstract
4.3.1 Introduction and Background
4.3.2 Methodology
4.3.3 Results and Discussion
4.3.4 Conclusion
Acknowledgements
References
4.4 Optimisation of Soybean Manufacturing Process Using Real-time Artificial Intelligence of Things Technology
Abstract
4.4.1 Introduction
4.4.2 Soybean Production Process Description
4.4.3 Overall Manufacturing System Architecture and Platform
4.4.4 Process Parameters Monitoring
4.4.5 Edge Processing and AI-based Framework for Real-Time Monitoring
4.4.6 Embedded Intelligent Vision and Multi-sensors Fusion Approach
4.4.6.1 Embedded Vision IIoT Systems Evaluation
4.4.7 Experimental Setup
4.4.7.1 Experimental Evaluation and Results
4.4.8 Summary and Future Work
Acknowledgements
References
4.5 AI and IIoT-based Predictive Maintenance System for Soybean Processing
Abstract
4.5.1 Introduction
4.5.2 Maintenance Foundations
4.5.3 Principles of Predictive Maintenance
4.5.4 Soybean Production Process and Maintenance Policies
4.5.4.1 Vibration Analysis
4.5.5 AI-based Predictive Maintenance Framework Methodology
4.5.6 Industrial Integrated System for Soybean Production Equipment
4.5.7 Experimental Set-up and Implementation
4.5.8 Summary and Future Work
Acknowledgements
References
Section
5 AI Transportation
5.0 Applications of AI in Transportation Industry
Abstract
5.0.1 Introduction and Background
5.0.2 AI Developments in Transportation Industry
5.0.3 Future Trends for Applications in Transportation Industry
5.0.4 AI-Based Applications
Acknowledgements
References
5.1 AI-Based Vehicle Systems for Mobility-as-a-Service Application
Abstract
5.1.1 Introduction and Background
5.1.2 AI-Based 3D Object Detection and Tracking for Automated Driving
5.1.2.1 Camera and LiDAR Sensor Data Fusion
5.1.2.2 Experiments and Results
5.1.2.3 Evaluation of the Algorithm and Vehicle Integration
5.1.3 Autonomous Control Prototyping in Simulated Environments
5.1.3.1 Reinforcement Learning Control for Mobile Vehicles
5.1.3.2 The Architecture – Immediate Actions Time-Horizon
5.1.4 Conclusion
Acknowledgements
References
5.2 Open Traffic Data for Mobility-as-a-Service Applications – Architecture and Challenges
Abstract
5.2.1 Introduction and Background
5.2.2 Data Acquisition
5.2.2.1 Bus Traces
5.2.2.2 Traffic Cameras
5.2.2.3 Loop Detectors
5.2.3 Data Processing at the Edge
5.2.3.1 Object Detection
5.2.3.2 Bus GPS Trace
5.2.4 Data Processing in the Cloud
5.2.4.1 Data Quality Monitoring
5.2.4.2 Data Quality Observations
5.2.5 Conclusion
Acknowledgements
References
List of Contributors
Index
Back Cover