AI robot maintenance may help address UK Government concerns that the UK has too few industrial robots. According to a recent Government report the UK has too few robots in relation to the number of workers when compared to most other industrialised countries.
The report emphasises the future of manufacturing as being dependent on higher levels of productivity through robotics and automation. Perceived barriers included the complex nature of automation and the lack of availability of skills required to deal with it.
Barry Weller, Product Manager at Mitsubishi Electric, considers how artificial intelligence (AI) addresses these perceptions and simplifies robot adoption.
The latest predictive analytics solution supplied with Mitsubishi Electric industrial robots uses Artificial Intelligence as a key feature for optimisation. Called SmartPlus it enables users to understand, schedule and optimise robot maintenance even before installing it on the factory floor. This provides them with the confidence that their robot investment is worthwhile.
Both standard industrial and collaborative robots can create new opportunities for streamlining production and assembly operations. Yet, like any other machines, robots need maintenance support to deliver optimal performance.
Predictive Maintenance
Like any asset, robots will run at peak efficiency when operators have access to predictive maintenance tools. AI systems are the most useful tools for recognising patterns, making predictions and also giving practical advice on actions to take. AI has an unmatched ability to process large data volumes it uses to identify patterns and generate predictive models. It processes and analyses data collected from different sources and uses them to build a model that can deliver useful actionable insight on the status of a robot.
AI Robot Maintenance
Mitsubishi Electric FR-Series industrial robots offer this AI functionality through a plug-in SmartPlus card. SmartPlus AI offers three maintenance functions:
Firstly, consumption degree calculation determines when a robot’s drive parts are likely to need replacement and notifies the maintenance planner.
Secondly, it offers maintenance simulations. Using the same data, the AI robot maintenance system will estimate the robot’s service life and offer a maintenance schedule to suit. This optimises maintenance costs and considers the real-time operating conditions and activities performed by the robot.
Finally, the AI robot maintenance system offers a centralised robot management platform. The data from SmartPlus loads onto many cloud-based analytics solutions to work will upper-level enterprise systems to combine their data with maintenance data from the robot controller. In this way, the solution can deliver reliable predictive models as well as preventative information.
Visualisation for Improved Decisions
Visualisation is an important aspect of AI-based predictive maintenance. It presents the information generated by the model in an accessible and immediate way to plant and maintenance planners.
It builds a knowledge base supporting meaningful decisions and quick actions, without needing specialised skills or training in data mining. A direct result of this are efficient maintenance schedules that maximise equipment use or intervene before breakdowns occur.
