Maximize asset efficiency by implementing Predictive Maintenance

Predictive Maintenance (PdM) is composed of sensors, data analytics and machine learning. The sensors provide condition monitoring during normal operations. The information is streamed to the big data analytics engine where it is processed and fed to a machine learning model which tracks and notifies if there are any likelihood of failures. Predictive maintenance creates significant cost savings by:

  • Optimizing planned downtime
  • Minimizing unplanned downtime
  • Optimizing equipment lifetime
  • Optimizing employee productivity

 

A Wall Street Journal report on “How Manufacturers Achieve Top Quartile Performance“, mentions that “Unplanned downtime costs industrial manufacturers an estimated $50 billion annually. Equipment failure is the cause of 42 percent of this unplanned downtime. Unplanned outages result in excessive maintenance, repair, and equipment replacement.”

The implementation of predictive maintenance is not simple, you need to find the right sensors, place them in the right spots, understand data acquisition equipment, analyze the results and create a predictive machine learning model for that piece of equipment. Our depth of experience along with our partners specialize in putting all the right pieces together in a short time frame allow you to start benefiting today.