How do you optimize equipment reliability with predictive maintenance?
To optimize equipment reliability, facility managers typically employ predictive maintenance (PdM) strategies in tandem with Internet of Things (IoT) technology. This helps them to set up assets to report exactly when they need maintenance. With these systems in place, it is easy to gauge equipment’s state and determine whether or not it needs maintenance, leading to higher reliability overall.
Figure out when things are going to break (and don’t let them)
Reliability is a matter of things either breaking down or not. With this in mind, maintenance managers use tools like vibration analysis, infrared imaging, and noise monitoring to monitor assets and optimize reliability. These tools paint an entire picture of an asset’s health at all times and allow teams to dispatch technicians at the exact moment they are needed or, even better, before they are even needed. This process of fixing issues before they really have the chance to come up increases the overall reliability of your equipment.
Customized data sets dependent on equipment
PdM programs allow maintenance teams to set up either asset- or area-specific monitoring systems, depending on their needs. This means that a particularly vital piece of equipment, or equipment that breaks down frequently, can have its own dedicated monitoring setup. At the same time, a large row of similar, non-critical equipment can use the same system as one.
First, this does a lot for increasing a facility’s reliability. Second, this optimizes the amount of money spent on the facility’s system as a whole. Technicians can closely monitor the most critical equipment, while not needing to focus their time so heavily on non-critical assets.
Understand root causes
One of the most important components of equipment maintenance is understanding root causes. PdM allows for excellent root cause analysis because it indicates precisely what factor causes failures and helps to control for that factor. This results in a boost in reliability.