Embedded Edge Intelligent Processing for End-To-End Predictive Maintenance in Industrial Applications

This article advances innovative approaches to the design and implementation of an embedded intelligent system for predictive maintenance (PdM) in industrial applications. It is based on the integration of advanced artificial intelligence (AI) techniques into micro-edge Industrial Internet of Things (IIoT) devices running on Armr Cortexr microcontrollers (MCUs) and addresses the impact of a) adapting to the constraints of MCUs, b) analysing sensor patterns in the time and frequency domain and c) optimising the AI model architecture and hyperparameter tuning, stressing that hardware–software co-exploration is the key ingredient to converting micro-edge IIoT devices into intelligent PdM systems. Moreover, this article highlights the importance of end-to-end AI development solutions by employing existing frameworks and inference engines that permit the integration of complex AI mechanisms within MCUs, such as NanoEdgeTM AI Studio, Edge Impulse and STM32 Cube.AI. Both quantitative and qualitative insights are presented in complementary workflows with different design and learning components, as well as in the backend flow for deployment onto IIoT devices with a common inference platform based on Armr Cortexr-M-based MCUs. The use case is an n-class classification based on the vibration of generic motor rotating equipment. The results have been used to lay down the foundation of the PdM strategy, which will be included in future work insights derived from anomaly detection, regression and forecasting applications.publishedVersio

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Digital Twins Approaches and Methods Review

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/ITC-Egypt58155.2023.10206196This paper investigates the recent advances in Digital Twin technologies. The aim is to compare the approaches, available open source and proprietary technologies and methods, their features, and their integration capabilities. The motivation is to enable better design decisions based on the available literature and case studies. Various tools for 3D reconstruction and visualisation, IoT and sensor integration, Physical simulations and other complete platforms provide complete solutions. A conclusion of current challenges and future work identified that the lack of standardisation and interoperability makes the lifetime of a digital twin short, with a high cost and time to build and rebuild if required

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

The Internet of Simulation, a Specialisation of the Internet of Things with Simulation and Workflow as a Service (SIM/WFaaS)

Abstract: A trend seen in many industries is the increasing reliance on modelling and simulation to facilitate design, decision making and training. Previously, these models would operate in isolation but now there is a growing need to integrate and connect simulations together for co-simulation. In addition, the 21st century has seen the expansion of the Internet of Things (IoT) enabling the interconnectivity of smart devices across the Internet. In this paper we propose that an important, and often overlooked, domain of IoT is that of modelling and simulation. Expanding IoT to encompass interconnected simulations enables the potential for an Internet of Simulation whereby models and simulations are exposed to the wider internet and can be accessed on an "as-a-service" basis. The proposed IoS would need to manage simulation across heterogeneous infrastructures, temporal and causal aspects of simulations, as well as variations in data structures. Via the proposed Simulation as a Service (SIMaaS) and Workflow as a Service (WFaaS) constructs in IoS, highly complex simulation integration could be performed automatically, resulting in high fidelity system level simulations. Additionally, the potential for faster than real-time simulation afforded by IoS opens the possibility of connecting IoS to existing IoT infrastructure via a real-time bridge to facilitate decision making based on live data

Orchestration of machine learning workflows on Internet of Things data

Applications empowered by machine learning (ML) and the Internet of Things (IoT) are changing the way people live and impacting a broad range of industries. However, creating and automating ML workflows at scale using real-world IoT data often leads to complex systems integration and production issues. Examples of challenges faced during the development of these ML applications include glue code, hidden dependencies, and data pipeline jungles. This research proposes the Machine Learning Framework for IoT data (ML4IoT), which is designed to orchestrate ML workflows to perform training and enable inference by ML models on IoT data. In the proposed framework, containerized microservices are used to automate the execution of tasks specified in ML workflows, which are defined through REST APIs. To address the problem of integrating big data tools and machine learning into a unified platform, the proposed framework enables the definition and execution of end-to-end ML workflows on large volumes of IoT data. In addition, to address the challenges of running multiple ML workflows in parallel, the ML4IoT has been designed to use container-based components that provide a convenient mechanism to enable the training and deployment of numerous ML models in parallel. Finally, to address the common production issues faced during the development of ML applications, the proposed framework used microservices architecture to bring flexibility, reusability, and extensibility to the framework. Through the experiments, we demonstrated the feasibility of the (ML4IoT), which managed to train and deploy predictive ML models in two types of IoT data. The obtained results suggested that the proposed framework can manage real-world IoT data, by providing elasticity to execute 32 ML workflows in parallel, which were used to train 128 ML models simultaneously. Also, results demonstrated that in the ML4IoT, the performance of rendering online predictions is not affected when 64 ML models are deployed concurrently to infer new information using online IoT data

Quality Assurance in MLOps Setting: An Industrial Perspective

Today, machine learning (ML) is widely used in industry to provide the core functionality of production systems. However, it is practically always used in production systems as part of a larger end-to-end software system that is made up of several other components in addition to the ML model. Due to production demand and time constraints, automated software engineering practices are highly applicable. The increased use of automated ML software engineering practices in industries such as manufacturing and utilities requires an automated Quality Assurance (QA) approach as an integral part of ML software. Here, QA helps reduce risk by offering an objective perspective on the software task. Although conventional software engineering has automated tools for QA data analysis for data-driven ML, the use of QA practices for ML in operation (MLOps) is lacking. This paper examines the QA challenges that arise in industrial MLOps and conceptualizes modular strategies to deal with data integrity and Data Quality (DQ). The paper is accompanied by real industrial use-cases from industrial partners. The paper also presents several challenges that may serve as a basis for future studies.Comment: Accepted in ISE2022 of the 29th Asia-Pacific Software Engineering Conference (APSEC 2022