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Predicting Failures and Performing Preventive Actions at the Right Time

JUNO Innovate can accelerate your rate of adoption for AI use cases pertaining to the management & maintenance of machines and assets.

Benefits of Predictive Maintenance AI Solutions:

Reduced downtime

Reduced downtime

Predictive maintenance can help to reduce unplanned downtime by up to 50%.

Extended equipment lifespan

Extended equipment lifespan

Predictive maintenance can help to extend the lifespan of equipment by up to 30%.

Reduced maintenance costs

Reduced maintenance costs

Predictive maintenance can help to reduce overall maintenance costs by up to 25%.

How Predictive Maintenance Works

Predictive maintenance works by collecting data from sensors that are attached to equipment. This data is then analyzed using machine learning algorithms to predict when equipment is likely to fail. The predictive maintenance system provides alerts to maintenance personnel when the system predicts that equipment is likely to fail. Maintenance personnel can then schedule maintenance proactively to address the potential problem before it causes equipment failure.

How Predictive Maintenance Works
Predictive Maintenance Use Case for Energy Plants

Predictive Maintenance Use Case for Energy Plants

One example of how predictive maintenance can be used in energy plants is to predict failures of rotating equipment, such as turbines and generators. Rotating equipment is critical to the operation of energy plants, and failures of this equipment can lead to significant downtime.

Predictive maintenance systems can be used to collect data from sensors that are attached to rotating equipment. This data can then be analyzed to identify patterns that indicate that equipment is likely to fail. For example, the predictive maintenance system may identify a pattern of increasing vibration levels, which can indicate that a bearing is failing.

When the predictive maintenance system identifies a potential problem, it can generate an alert to maintenance personnel. Maintenance personnel can then schedule maintenance to address the potential problem before it causes equipment failure.

Predictive Maintenance Use Case for Cloud Operations

Using JUNO Innovate you can experiment with how AI can be used for predictive maintenance in cloud operations to monitor metrics such as CPU utilization, memory usage, and disk I/O. In our JUNO Innovate lab, AI algorithms can be used to identify patterns in this data that may indicate that a problem is developing. For example, if CPU utilization is consistently high, this may indicate that a server is overloaded and may soon fail.

Once a potential problem has been identified, AI can be used to recommend corrective action. For example, AI may recommend that a server be scaled up or that a new server be added to the load balancer. AI can also be used to automate the process of implementing corrective actions, which can save cloud operations teams a significant amount of time and effort.

There are several more use cases for predictive maintenance within out JUNO INNOVATE lab. Together, we can revolutionize the digital and AI landscape to make new possibilities in our world. Start your use case and utilize one of our pre-built models now

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