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HomeArshon TechnologyWinning Project of Arshon Technology and Ontario University: Digital Twins for Predictive...

Winning Project of Arshon Technology and Ontario University: Digital Twins for Predictive Maintenance

Arshon Technology is proud to have collaborated with the Ontario Tech University on a project that won first place at the Engineering Capstone Competition. The project, titled "IoT Enabled Vibration Monitoring Toward Smart Maintenance," was completed by a team of multidisciplinary students and showcased our company's Smart Structural Sensors (SSS) technology.

This great team was also mentored by our researchers at AD2MLabs, and particularly by Andrew Bondoc, Mohsen Tayefeh, and Mazi Hosseini (Arshon Technology Inc.). 

In this blog, we'll explain how Digital Twins can help with predictive maintenance and share details about the winning project.

Collaboration with Arshon Technology
Image by Ahmad_Barari on linkedin

What is Predictive Maintenance and How Can Digital Twins Help?

Predictive maintenance is the practice of predicting when maintenance should be performed on machinery and equipment to prevent downtime and reduce maintenance costs. However, this requires a large amount of data to be collected and analyzed in real-time. This is where Digital Twins come in.

A Digital Twin is a virtual copy of a physical object or system that can be used to simulate and predict behavior in real-time. By creating a digital twin of a piece of equipment, engineers can monitor its performance and detect early signs of wear and tear, allowing them to schedule maintenance activities proactively.

Using Digital Twins for Predictive Maintenance

Digital Twins can facilitate predictive maintenance by providing engineers with a virtual replica of their machinery. This enables them to perform simulations and test different maintenance strategies without disrupting the actual system. By using Digital Twins, engineers can make more informed decisions about when to perform maintenance, which can lead to significant cost savings and reduce downtime.

Digital Twins can be used to monitor equipment in real-time and identify potential issues before they become significant problems. This is achieved by collecting data from sensors attached to the equipment and transferring it to the cloud. Once in the cloud, the data is analyzed using complex mathematical algorithms that can identify patterns and anomalies that may indicate an issue with the equipment. The results of this analysis are then sent back to the equipment, allowing maintenance teams to take action before any serious damage occurs.

One of the key advantages of using Digital Twins for predictive maintenance is that it eliminates the need for complex processing equipment on the machinery site. Instead, all of the data is sent to the cloud, where it can be analyzed using high processing power. This makes the process faster and more efficient, reducing the risk of equipment failure and costly downtime.

The Winning Project: IoT Enabled Vibration Monitoring Toward Smart Maintenance

The winning project at the Engineering Capstone Competition showcased the power of Digital Twins and our Smart Structural Sensors (SSS) technology. The team used our LIVE Digital Twins philosophy to create a virtual replica of the equipment being monitored. By combining the SSS data with the Digital Twin, the team was able to simulate the behavior of the equipment and predict when maintenance would be required. This allowed for proactive maintenance scheduling, which reduced downtime and maintenance costs.

Conclusion:

At Arshon Technology, we are committed to developing innovative solutions that help our clients reduce downtime and save on maintenance costs. The collaboration with the Ontario Tech University on the winning project highlights the power of Digital Twins and our Smart Structural Sensors (SSS) technology for predictive maintenance. By leveraging these technologies, engineers can monitor the performance of machinery and equipment in real-time and predict when maintenance activities should be performed, leading to significant cost savings and reduced downtime.

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