The advances in industrial edge artificial intelligence (AI) are
transforming the way industrial equipment and machines interact
with the real world, with other machines and humans during
manufacturing processes. These advances allow Industrial Internet
of Things (IIoT) and edge devices to make decisions during the
manufacturing processes using sensors and actuators data. Digital
transformation is reshaping the manufacturing industry, and
industrial edge AI aims to combine the potential advantages of edge
computing (low latency times, reduced bandwidth, distributed
architecture, improved trustworthiness, etc.) with the benefits of
AI (intelligent processing, predictive solutions, classification,
reasoning, etc.). The industrial environments allow the deployment
of highly distributed intelligent industrial applications in remote
sites that require reliable connectivity over wireless and cellular
connections. Intelligent connectivity combines IIoT,
wireless/cellular and AI technologies to support new autonomous
industrial applications by enabling AI capabilities at the edge and
allowing manufacturing companies to improve operational efficiency
and reduce risks and costs for industrial applications. There are
several critical issues to consider when bringing AI to industrial
IoT applications considering training AI models at the edge, the
deployment of the AI-trained inferencing models on the target
reliable edge hardware platforms and the benchmarking of the
solution compared with other implementations. The next-generation
trustworthy industrial AI systems offer dependability by design,
transparency, explainability, verifiability, and standardised
industrial solutions to be implemented into various applications
across different industrial sectors. New AI techniques like
embedded machine learning (ML) and deep learning (DL) capture edge
data, employ AI models and deploy them to hardware target edge
devices from ultra-low-power microcontrollers to embedded devices,
gateways, and on-premises servers for industrial applications.
These techniques reduce latency, increase scalability, reliability,
and resilience, and optimise wireless connectivity, greatly
expanding IIoT capabilities. This book overviews the latest
research results and activities in industrial artificial
intelligence technologies and applications based on the innovative
research, developments and ideas generated by the ECSEL JU AI4DI,
ANDANTE and TEMPO projects. The authors describe industrial AI's
challenges, the approaches adopted, and the main industrial systems
and applications to give the reader a good insight into the
technical essence of the field. The articles provide insightful
material on industrial AI technologies and applications.
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