What is predictive maintenance?
Predictive maintenance is a system used to predict the failure of a machine, allowing for the repair or replacement of parts before they actually fail. It's often associated with predictive analytics because it relies on data from past failures to predict when machines will fail in the future. This allows for repairs to be scheduled ahead of time, which saves money and reduces downtime.
Predictive maintenance can be used for any kind of machinery that needs regular maintenance—from heavy industrial equipment like turbines to consumer products like appliances.
How does predictive maintenance work?
Predictive maintenance relies on systems to identify when equipment is at a high likelihood of failure. Prediction is achieved through a variety of methods:
- Machine learning algorithms are used to analyze data from past failures and other sources such as weather forecasts to predict future failures.
- Sensors are placed on the equipment which monitor its status and relay information back to a central system for analysis. This can include things like temperature sensors or alerts from other machines about when they've experienced problems due to interference with this one (for example).
- An individual operator may notice something amiss with an asset, either by visual inspection, asset log deviation from norm, or by hearing unusual noises coming from it while performing their regular tasks (such as turning it off at night or during lunch breaks).