This article looks at some of the key elements for successful industrial AI implementation, based on a submission by Moxa, a Taiwan-based provider of industrial networking, computing, and automation solutions.
Industrial AI leverages artificial intelligence to achieve production efficiency and downtime reduction. Amid the rise of smart manufacturing, industrial AI is poised for significant growth. This article looks at some of the key elements for successful implementation of industrial AI, based on a
submission by Moxa, a Taiwan-based provider of industrial networking, computing, and automation solutions. The submission is published in a&s Taiwan.
Industrial AI processes data generated by various IIoT devices, such as cameras and equipment sensors, to increase production efficiency and reduce downtime. With industrial AI, defects can be identified quickly, and impending machine failure can be detected early. Given the importance of industrial AI, successful implementation is critical. To this end, Moxa offers three key points that can help accelerate the successful implementation of industrial AI. These are summarize as follows .
Building a unified data foundation
To ensure that AI applications achieve maximum efficiency on the factory floor, data collection is critical, and such collection must encompass all levels – from the sensing level to the network level to the application level. The data must be seamlessly integrated on an AI platform; this is essential for enabling real-time decision-making and providing scalability for future applications.
Supporting complex traffic patterns
Executing real-time AI analytics in a manufacturing environment requires the use of multiple computing mechanisms, depending on the use case. For example, edge computing processes data closer to the source and can efficiently handle analysis that requires immediate response. Cloud, meanwhile, has more computing resources that can handle more complex data. Depending on the use scenario, analytics and AI inference can be performed either in the cloud or at the edge, or through a hybrid approach.
Building a highly adaptive network and cybersecurity architecture
As industrial AI adoption grows, the need for diverse network architectures increases. Both wired and wireless networks must be available to enable better real-time monitoring and tracking, thus enhancing connectivity, productivity, visibility, and replicability across the production floor. Here, a critical role will be played by next-generation industrial transmission technologies such as time-sensitive networking, single-pair Ethernet, and Ethernet-APL.
Crucial role played by networking
Indeed, the AI transformation across the industrial world is a continuous process, and the
upgradability of network and communication infrastructure is the cornerstone that supports this transformation.
“As industrial AI becomes increasingly common in manufacturing, communication networks are no longer just channels that connect machines and equipment – they have become real-time mechanisms for managing AI’s efficiency and performance,” said Shih-Wei Lin, GM for Asia and Taiwan at Moxa.
He adds: “With demand for industrial AI set to increase, we can foresee that network communications will hold the key to the successful implementation of AI in real-world production environments. This involves everything from integrating broader and more granular data to streamlining complex traffic patterns. As AI adoption deepens, only a scalable and resilient network can ensure that AI continues to deliver sustainable and ever-improving results.”
Cybersecurity is key
Network design for industrial AI applications must also take int consideration integrated cybersecurity mechanisms. For example, using IEC 62443 certification frameworks, security can be implemented from the component level to the system level and across the supply chain. Additionally, given the massive real-time data exchange between devices and systems, OT networks should adopt more traditional IT security technologies, and critical systems must be segmented so that production equipment and operations can be quickly and effectively protected in case of cybersecurity incidents.
Final word
The ongoing development of industrial AI underscores the critical role of networking. Networks and communications, which deliver large volumes of data in real time, are enablers of AI performance and efficiency. They allow AI to detect anomalies, enable predictive maintenance, automate processes, and improve production efficiency and quality. With advanced networking, the value of industrial AI can be unlocked to meet market challenges and seize new opportunities.