Manufacturing, Predictive Maintenance
What Role Does Monitoring and Predictive Maintenance Play in Manufacturing?
Mar
In the manufacturing industry, equipment maintenance is integral to keeping your company running. If a piece of equipment fails, the downtime is expensive — both in equipment repair costs and lost production time. Depending on how much you produce in an average day, a single hour of downtime can cost you anywhere from $1,000 to $10,000 or more. Monitoring and predictive maintenance can help you keep everything moving smoothly. What role do these two tools play in manufacturing?
Predictive Maintenance vs. Preventive Maintenance
Before we get into predictive maintenance and monitoring, we do need to address a different form of maintenance — preventive. Preventive maintenance is merely the practice of fixing small problems before they become big ones. Changing the oil in your car according to the manufacturer’s recommendations is preventive maintenance. You probably already have a schedule in place for preventive maintenance for each piece of equipment — replacing belts, changing oil or grease and inspecting each moving part for signs of wear and tear that could indicate the beginnings of more significant problems.
Predictive maintenance, especially when paired with monitoring, takes preventive maintenance to a whole new level.
What Is Predictive Maintenance?
You most likely already have a maintenance schedule in place for going over each piece of machinery on your production floor to help keep everything running. Each piece has its manufacturer-recommended maintenance schedule, and keeping up with that can prevent any unscheduled downtime. Sometimes, though, equipment fails. What if you could predict those failures and fix the problems before they take your production floor offline?
That’s where predictive maintenance comes in.
A predictive maintenance algorithm examines your maintenance and repair history, using data you’ve collected over months or years. It’s programmed to look for patterns human observers might miss. These patterns can indicate when a piece of equipment might fail outside its regular maintenance schedule. With enough information, these programs can even predict future failures.