Predictive maintenance is the process of attempting to predict when operational equipment may fail and deploying preventive maintenance to avoid any downtime. This method is data-driven and uses predictive data analytics to monitor a variety of equipment conditions, ranging from production metrics to structural conditions that could indicate possible machinery or equipment failure.
The aim of predictive maintenance is to reduce repair and upkeep costs, as well as reduce downtime as much as possible by mitigating the likelihood of a catastrophic equipment failure.
See it in action:
Instead of focusing on constant maintenance, predictive methods focus on providing the bare minimum maintenance to maintain a high uptime, as well as minimize the need for reactive maintenance along with the accompanying costs. Instead of focusing on adaptive methods, predictive maintenance uses metrics to determine when upkeep should occur and how frequently.
For instance, a car that is driven for 10,000 miles may be automatically scheduled for maintenance and an oil change based on its odometer and not related to any other immediate concerns. The best predictive maintenance models are those that are most effective at predicting failures and providing enough advanced warning of upcoming downtime to avoid any interruptions:
How Can I Use Predictive Maintenance?
One of the most common and important uses of predictive maintenance is related to operation-critical machinery or devices. Data servers that hold sensitive company information, for instance, must constantly be monitored to ensure organizational infrastructure doesn’t collapse or experience blackouts.
Similarly, machinery that is vital to manufacturing processes must also be closely tracked to avoid any downtime or minimize its associated maintenance costs. In both cases, it’s impractical to dedicate employee time to specifically monitor all machinery, so predictive maintenance is instead deployed to keep operations running without increasing costs.
In factories and plants, manufacturing analytics are connected to predictive maintenance suites to provide minute-to-minute updates on production levels, lubrication, component conditions, and more in order to keep production consistent. The Internet of Things is also playing a growing role in the predictive maintenance space thanks to its interconnectivity and its data communication capacity.
Many times, IoT devices are used to provide real-time data and monitoring to ensure better and more consistent maintenance. Predictive maintenance can even be used in electrical systems and circuitry to detect fluctuations in amperage, power spikes, and areas where current is being affected.