This website uses cookies primarily for visitor analytics. Certain pages will ask you to fill in contact details to receive additional information. On these pages you have the option of having the site log your details for future visits. Indicating you want the site to remember your details will place a cookie on your device. To view our full cookie policy, please click here. You can also view it at any time by going to our Contact Us page.

Taking steps to implement AI

12 August 2019

Tim Foreman argues that companies should be looking to benefit from the potential of technologies such as ‘AI at the Edge’ strategically and thoughtfully for business success and competitive advantage.

Increasing computing power, growing data volumes and the increased use of sensors means that the discussion about AI on the factory floor is gaining momentum. Adaptive algorithms offer enormous potential for the further developments required for Industry 4.0, such as predictive maintenance and networked production. In this context, AI can help increase Overall Equipment Effectiveness (OEE) to reduce costs and increase productivity. The challenge faced by businesses however, is that many of the market-driven, often cloud-based AI solutions place enormous demands on infrastructure and IT. These solutions work with vast amounts of data that is laborious to prepare and take advantage of. In addition, system concepts for mechanical engineering are often complex and specially tailored to the respective requirements. A reliable use of typical AI algorithms is only possible through extensive testing, constant optimisation and over-dimensioning – which many companies shy away from.
Today there are many AI solutions suited for use in industrial automation. These include open source software and applications based on Machine Learning (ML). Robotics and automation providers are currently developing AI solutions that help small and medium-sized businesses in particular to use AI effectively and efficiently. The following tips will help you get started:

How to get started with AI
• Upgrade data management capabilities: Manufacturing companies are often more conservative when it comes to new technologies, as they work with machines that must run for 20 years or more. This should not mean that these companies they have to lose out when it comes to AI. It is important investigating the benefits that AI and ML will bring to the industrial environment and to over any reluctance to invest in these technologies. Companies should ensure that they can work with large amounts of data and advanced algorithms, the two cornerstones of artificial intelligence. 

• Outline central project questions and approaches: Important questions at the beginning of an AI project include: Which problems and challenges should be tackled? Which strategy and technology are best suited, and are they adaptable and expandable for a variety of projects and use cases? Which managers and employees should be brought on board? Is there the necessary expertise within the company or is there a need to involve external experts? How can a new machine with an integrated data science approach be planned and implemented?

• Define clear and measurable goals: The primary goal of AI deployment is to increase quality and process efficiency, for example through improved predictive maintenance to avoid equipment downtime. The AI-based solution should therefore aim at measurable and noticeable improvements in OEE. It is important to note that even an optimisation of only a few percentage points can lead to considerable increases in efficiency and cost reductions. AI in machine maintenance, for example, can help to reduce the risk of equipment damage and downtime, as problems can be detected early, and immediate action can be taken to eliminate them. Without automation, machine designers and operators would have to create their own analysis and optimisation solutions or use costly cloud solutions.

• Take advantage of AI ‘at the edge’: Instead of laboriously searching through data for patterns, find technology that approaches things differently – for example can the required algorithms can be integrated into the machine control to create the framework for real-time optimisation – at the machine level (the edge). This involves monitoring production lines and machines with real-time sensors, which immediately collect the data and check it for anomalies. 

• Focus on real-time data processing: While cloud-based AI solutions place enormous demands on infrastructure and IT, and the processing of data volumes is a tedious and time-consuming process, AI at the edge suits predictive maintenance and control of machines. It combines line control functions with real-time AI-based data processing. One advantage is that it is possible to reliably identify unforeseen situations and react quickly, improve quality, maintenance cycles and machine lifecycles, and scale as needed. The processes gain intelligence on the basis of previous findings and improvements, and drive the holistic optimisation of the manufacturing process.

Tim Foreman is european R&D manager at Omron.

Contact Details and Archive...

Print this page | E-mail this page