Machine learning factory automation how to#

This data can include speed, distance, pressure, flow, current, temperature, vibration, environmental factors such as humidity, torque and number of cycles in a component’s lifecycle, etc. Machine learning and AI use basic operational data to learn the healthy state of the process, part, or energy system.

Machine learning factory automation how to#

The end user decides where, when, and how to display information. Presentation of actionable information should be configured for the end user’s workflow. Machine learning and AI systems should be vendor agnostic, not limited to or focused on a brand of component or type, but simply acquiring and analyzing pertinent resident data. Anomalies (outliers) on the map prompt corrective action.Īs a result, machine learning and AI effectively map the big three operational concerns: health of the process, quality of products and energy consumed. The AI system creates a map that represents the region of healthy performance. Machine learning systems are trained by operational data on components, machines, products, and energy systems. Unplanned downtime can fall by more than 20%.

Machine availability can be improved by more than 25%. Festo has found that machine learning/AI can improve process transparency by 100%, lower waste by more than 50%, and decrease product rejection costs by more than 45%. Targeted maintenance is an efficient use of maintenance resources.Īs advanced as they seem, machine learning and AI are not bleeding edge solutions, but practical tools available now. Receiving a solution prior to a shutdown means higher uptime, greater quality, and overall improved throughout. These technologies take predictive analytics to new levels of performance through early identification of out-of-spec operation and communication to team members, either through dashboards or in easy-to-understand messaging that convey vital, on-point information. Smart components allowed industry to move beyond break/fix to preventative maintenance and predictive analytics using machine learning and artificial intelligence (AI). With today’s high costs and liability issues, downtime and poor quality need to approach zero incidents. Fewer personnel possess exceptional diagnostic insights. Today, changeovers occur frequently, and machines are complex and difficult to analyze. In those days, long runs of the same product led to fewer changeovers with less tinkering and adjusting. Before the introduction of smart components to industry, manufacturing and processing operations lived in a break/fix mechanical world where maintenance personnel could practically look at a machine and know what was causing it to stop.