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How IoT Powers Predictive Maintenance for Manufacturing

How IoT Powers Predictive Maintenance for Manufacturing

The worst thing that can happen to any manufacturing is downtime. For example, companies that manufacture automobiles can lose millions of dollars for every hour of downtime.

Unscheduled maintenance activities also interfere with the smooth running production processes, which also cause extra downtime and send ripples throughout other processes.

How can manufacturers save their businesses from the crashes? Predict them, and what they predict must be something they can take care of.

In addition to downtime and equipment crashes, there are a number of issues that manufacturers face, and IoT-powered predictive maintenance software is often a one-stop solution.

What IoT changes in predictive maintenance

IoT manufacturing solutions answer the following questions:

  • How much time is left until the equipment fails?
  • What is more likely to cause the failure?
  • What are the failure rates?

And this list of questions goes on. Answering them reveals the equipment status and failure possibilities – a set of insights that are crucial for the healthcare of the enterprise.

The IoT gives manufacturers the ability to obtain data used to answer these questions and changes the way it is used further. Thekeychangesare:

  • Sensors allow for real-time monitoring of equipment performance. The IoT-based predictive maintenance solutions aid in monitoring thousands of different equipment units, that can be located across numerous facilities of an enterprise. This challenge of monitoring and managing such a huge number of machines is perfect for IoT to show its capabilities to store and analyze data. It can be a great help when it is crucial to manage machines at their peak performance.
  • Algorithms obtain data to monitor patterns in real time. If it is possible to apply data science to the data received from the industrial equipment, it is easier to dig deeper into the performance patterns. This process implies having historical data work for the sake of machines learning from repeating scenarios. The more information on the equipment behavior there is, the better machines learn. As one can see, the predictive capabilities will not be available from the very implementation of such predictive maintenance software solutions. The system needs time to collect data in order to apply the aforementioned algorithms.

As seen, predictive maintenance software backed up by IoT appliances reinvents the maintenance processes. A significant number of companies apply such solutions, from small businesses to large caps. As they share their experience, we can see whether the outcomes are positive.

How companies apply IoT for predictive maintenance

As IoT provides a wide range of possibilities, businesses have a wealth of options of implementation. The following companies have harvested the best benefits of the IoT-powered maintenance solutions:

Nasa monitors assets status.

NASA Armstrong Flight Research Center has deployed a Siemens predictive maintenance solution for assets monitoring. The solution provides data analysis and actionable advice on how to enhance the maintenance of the plant cooling system. The system is crucial for the plant healthcare, so NASA wanted a solution that can provide maintenance managers with reliable data on how the assets perform.  Now managers can monitor air handlers, cooling towers and fans in real time, gain insights on how to improve the maintenance of these assets, thanks to Siemens IoT-based system showing the vibration and speed of the assets.

BP integrates operational data.

BP utilizes a predictive maintenance solution to increase the efficiency and reliability of oil and gas production. An IoT-powered solution Plant Operations Advisor helps to eliminate downtime and allows field engineers to respond to diverse issues faster.

Rail carriers power maintenance with expert knowledge.

Rail carriers use predictive maintenance software to identify the effectiveness of predictions and avoid subsystems crashes. The IoT solution provided is often a knowledge-based and data-driven software that allows the engineers to influence the data analysis process with their domain expertise. To extract the right data, the system monitors such components as rotating parts, door systems, wheel bearings, and so on.

As you can see, IoT powers predictive maintenance for manufacturing in a number of ways. A wealth of companies have already appreciated the benefits of IoT-based predictive maintenance, and there are more companies to do so in the future.

 

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