How to use Elastic ML for predictive maintenance in manufacturing companies.

Machine Learning and the ability to predict maintenance cycles is important for many different companies. In particular manufacturers increasingly collect more and more data from different IoT sensors in their factories to feed their ML models. Collecting telemetry such as temperature, sound, and frequency, is an efficient way to monitor the health of IoT devices. Leveraging machine learning to detect early warning signs of expensive failures before they occur has become a driving force for improving productivity. For many, predictive analytics leading into predictive maintenance is a top business objective. There are advantages to be had, and KPIs that can greatly benefit, such as: reduction in maintenance costs, decrease in unexpected failures, increased uptime, and increased mean time between device failures.

Learn how the Elastic Stack can help you solve key challenges when implementing predictive maintenance, like data collection, normalization and analytics at the example of manufacturing. Of course this approach is flexible enough to also be used in other industry sectors.

Felix Roessel is working as Principal Solution Architect at elastic for more than 3 years now. He is a passionate developer and always looking for new opportunities to improve development skills. He's also a thought leader in guiding companies with their Industry 4.0 strategie. In that regard he also deeply looks into Machine Learning capabilities to help companies leveraging these technologies.