The world is in a state of accelerating digital change. In an industrial context machine learning, advanced analytics and artificial intelligence (AI) are tools with enormous potential to deliver improved predictive maintenance strategies, but how these tools are applied is critical to success.
There has been a shift from the initial, somewhat idealistic, objective to monitor everything and anything on a machine. It makes a lot more sense to identify priorities and focus attention on optimising the machine learning algorithms to spot data pattern anomalies in critical areas first, identifying the areas with the fastest ROI.
Predictive maintenance based on AI offers additional advantages compared to traditional condition monitoring approaches. Increasingly data from the machinery can be merged with process data and evaluated using analysis models and cloud-based solutions. For example, AI can be used to detect deviations at an early stage from the normal state of your production machinery. This reduces unplanned downtime, lowers energy costs and increases efficiency. Reducing unplanned production stoppages directly also increases overall equipment effectiveness.
Delivering the future
Delivering this new vision for predictive maintenance is an interesting challenge. Today’s manufacturers of automation machinery need to look for new ways to support their customers in achieving predictive maintenance based on real time data mining. For companies within the automation technology sector, this requires the ability to merge mechatronics expertise with digital analytical solutions, which is no simple task and requires a whole new set of skills.
At Festo, experience has taught us that it is critical to an AI project's success that we not only provide software expertise, but we also have the knowledge to integrate it into the production environment and provide the experience to interpret the data in the terminology of the application. Our strategic acquisition of specialist software company Resolto has enabled us to fast-track this synthesis and develop a solution that makes predictive maintenance a seamless part of the machine.