Predictive Maintenance Analytics

The ability to anticipate equipment failure before it happens is a gamechanger for any industrial enterprise. From pharmaceutical manufacturers to power plants, prolonging the equipment lifecycle is of the utmost importance, and taking a predictive approach to maintenance is critical. 

However, predictive maintenance does not come without the assistance of tools, technology, and software — after all, many equipment problems are undetectable to the naked eye. That’s where solutions such as predictive maintenance analytics can help. 

Predictive maintenance analytics can assist industrial enterprises with improving efficiency and minimizing unplanned downtime by gathering and analyzing equipment data in real-time. Using data-driven insights, companies can better understand the signs that indicate potential equipment failure and develop a preventative maintenance strategy accordingly.


What is predictive maintenance analytics?

Businesses that heavily rely on physical assets or machinery in their everyday operations put heavy stock in equipment maintenance — and for good reason. Equipment breakdowns can result in decreased productivity, sunk costs and customer dissatisfaction. Therefore, it makes sense that many enterprises are choosing to take a more proactive approach to equipment maintenance management

Predictive maintenance analytics is a tool that enterprises in the industrial sphere can use to better anticipate equipment failure and avoid unnecessary downtime. Predictive maintenance analytics marries real-time equipment data with data analytics and machine learning to evaluate current and future equipment performance. By monitoring equipment conditions in real-time, companies can gain a deeper understanding of equipment status to identify factors that may indicate a malfunction is about to occur. In turn, companies can utilize this information to develop a data-driven maintenance strategy and reduce equipment downtime to improve productivity.


How does predictive maintenance analytics work?

Predictive maintenance analytics relies on data gathered by predictive maintenance tools such as gauges, sensors and meters that are used to monitor equipment conditions. These tools gather important data on machinery, equipment, and other physical assets that may otherwise go undetected, including subtle changes in sound, temperature or vibration. All these factors are important, as they may indicate the need for repairs to avoid equipment malfunction. 

Predictive maintenance tools are connected through the Industrial Internet of Things (IIoT), which syncs the data gathered by these tools to the cloud. There, predictive maintenance analytics incorporates machine learning and statistical algorithms to evaluate the machine’s or asset’s health to gauge future performance. This helps organizations gain better insight into the “symptoms” that suggest the need for repairs.


Data-driven insights for proactive maintenance management

Predictive maintenance analytics can help virtually any enterprise that relies on physical equipment. By using powerful tools to monitor and analyze equipment conditions, companies can better identify the warning signs of equipment failure and develop a proactive maintenance strategy to improve uptime and efficiency. 

Here are just a few examples of how predictive maintenance analytics can benefit specific industries:

  • Utilities - Data analytics for utilities may incorporate predictive maintenance analytics to help utility companies monitor equipment performance and prevent power outages.

  • Pharmaceuticals - Predictive maintenance analytics can be integrated with pharma analytics to effectively measure asset performance and predict possible malfunctions that may affect production.

  • Power generation - Strategic power plant maintenance may involve using predictive maintenance analytics to identify signs that an outage may eventually occur. Power plants can then use this information to develop a preventive maintenance plan accordingly. 
Predictive maintenance analytics is a powerful tool used to support enterprises in improving overall efficiency and productivity. By implementing a data-driven predictive maintenance strategy, companies can extend the longevity of valuable equipment, boost ROI and increase profitability.



What is meant by predictive maintenance?
Predictive maintenance is a maintenance management strategy that integrates real-time equipment data and analytics to evaluate equipment and machinery conditions and assess the need for maintenance or repairs. Predictive maintenance is a proactive maintenance strategy, which means performing maintenance before equipment failure occurs. This is a very different approach from reactive maintenance, which entail performing a repair after equipment failure occurs. By using predictive maintenance analytics and tools, companies can improve their efficiency, minimize downtime and maximize the equipment lifecycle.

What is predictive maintenance analytics used for?
Predictive maintenance analytics is used to better predict the need for equipment repairs based on real-time equipment data. By using sensors, meters and other IoT predictive maintenance tools, predictive maintenance analytics allows businesses to gain a better understanding of equipment performance and identify signs of potential outages or malfunctions. Businesses can use this information to develop a more informed preventive maintenance strategy.

What does predictive maintenance involve?
Predictive maintenance involves using predictive maintenance tools such as sensors, software and data analytics to monitor and analyze equipment performance. This helps maintenance teams identify factors that signal the need for repairs to prevent unplanned downtime. Companies can then use this information to better understand equipment behavior and develop a proactive, data-informed maintenance management strategy to increase overall productivity.

What are predictive maintenance tools?
Predictive maintenance tools are tools that are used to monitor equipment conditions to anticipate possible failures. Some examples of predictive maintenance tools include sensors, gauges, and meters that measure variables such as equipment temperature, vibrations, sounds and other factors that are important in predictive maintenance analysis. Predictive maintenance tools gather this data and send it to the cloud, where it is then synced with predictive maintenance analytics to evaluate equipment status. Predictive maintenance analytics can help detect the early signs of equipment malfunction, allowing enterprises to make more informed decisions regarding maintenance management.