ABSTRACT
Industrial AI is a systematic discipline that focuses on developing, validating, and deploying machine learning (ML) algorithms for industrial applications with sustainable performance. Combined with state-of-the-art sensing, communication, and Big Data analytics platforms, a systematic Industrial AI methodology will allow the integration of physical systems with computational models.
The concept of Industrial AI encompasses the collective use of a number of technologies, such as Internet of Things (IoT), Cyber-Physical Systems (CPS), and Big Data analytics, under the Industry 4.0 initiative where embedded computing devices, smart objects, and the physical environment interact to reach intended goals.
Industrial AI is in its infancy, but a broad range of industries, including automotive, aerospace, healthcare, semiconductors, energy, transportation, mining, construction, and industrial automation, could harness its power to gain insights into the invisible relationship between various operation conditions and use those insights to optimise their uptime, productivity, and efficiency. In terms of predictive maintenance, Industrial AI can detect incipient changes in a system and predict remaining useful life (RUL), thus optimising maintenance tasks and avoiding disruption to operations.
This chapter analyses Industrial AI in several case studies: AI Factory for Railway, AI Factory for Mining, AI Factory – Augmented Reality (AR) & Virtual Reality (VR) Services, and AI Factory – Cybersecurity Services.
