Many industrial companies are already taking advantage of artificial intelligence (AI) for predictive maintenance, and the number is only going to grow in the years ahead. Edge AI offers a number of advantages over traditional centralized AI solutions, making it the perfect solution for processing massive amounts of data locally and feeding AI algorithms. Today we’ll discuss the value of Edge AI for predictive maintenance and why more companies are turning to it. We’ll also look at some of the challenges that come with using Edge AI and how to overcome them.

Predictive maintenance is a process that uses data to forecast when equipment is likely to fail. By using predictive maintenance, companies can schedule downtime for maintenance and repairs and avoid unexpected failures. This significantly reduces the likelihood of unplanned downtime while increasing uptime and cost savings.

One of the biggest benefits of Edge AI is its ability to process data locally. This is important for several reasons. First, it reduces the amount of data that needs to be sent over a network to the cloud or data center. Second, it allows for real-time processing, which is essential for running a predictive maintenance AI model. Finally, local Edge processing improves resiliency by running the AI model on-premise without the need for a network connection, removing the risk of network outages.

Despite these advantages, there are a few challenges that come with using Edge AI for predictive maintenance. It can be difficult to collect the data necessary to train Edge AI models and challenging to deploy and manage Edge AI solutions at scale. Additionally, Edge AI solutions may not be able to match the performance of centralized hyper-scale AI solutions. ClearBlade and Elipsa have developed comprehensive Edge and AI software to address these challenges, making Edge AI easy to manage and deploy. 

By partnering together, ClearBlade and Elipsa deliver the power of Edge AI to operations personnel (non-programmers) so they can configure and manage predictive maintenance on their equipment and critical assets. ClearBlade’s no-code Intelligent Assets application allows operators to track, monitor, and control any asset in their business while Elipsa’s Artificial Intelligence of Things (AIoT) software uses machine learning to analyze data from sensors and devices. This data is then used to predict when equipment will need maintenance or when equipment is operating outside of peak efficiency. Elipsa’s AIoT software is integrated with Intelligent Assets allowing operators to turn on predictive maintenance AI for any asset, alerting operators on potential failures before they happen. Together, our tools are enabling predictive maintenance across all industries, not just manufacturing. Effectively, we are eliminating the need for custom projects and bringing predictive maintenance and intelligent monitoring to the masses. 

It’s no secret that AI is revolutionizing the industrial sector. Predictive maintenance is one of the most popular applications of AI in the industry, and for good reason. By using data to forecast when equipment is likely to fail, companies can save money by avoiding unexpected failures and schedule repairs at their convenience. This can lead to significant cost savings and increased uptime.

If you’re looking for a comprehensive predictive maintenance solution, then look no further than ClearBlade and Elipsa. Our team has the experience and expertise to help your business get up and running with the latest in IoT, Edge, and AI technology. We understand that every business is different, so we offer a variety of capabilities to fit your needs. From Edge computing and data collection to visualization and analytics, we can provide you with everything you need to make the most out of your industrial IoT deployment. Ready to take the next step? Contact us today to learn more.