Highly accurate predictive maintenance for machinery and manufacturing 

AI’s ability to process huge amounts of data faster can provide greater reliability, efficiency and cost-savings to existing predictive maintenance processes

The sounds and vibrations machinery makes have powerful stories to tell. The secret to translating those stories is deep learning. 

AI and deep learning techniques can revolutionize predictive maintenance processes and unlock the insights buried in large data sets, from images to audio. Through the creation of effective models, we can rapidly and efficiently determine asset state, estimate lifespan and predict problems long before they occur. This can significantly lower operating costs while maximizing production output for manufacturers and other businesses reliant upon plant equipment or machinery.

How machines can be made more reliable and inspections less risky

Unplanned maintenance can have a huge negative impact on a business, bringing production and output to a screeching halt. Even scheduled maintenance is often unnecessarily expensive and only a “best guess” at when machines might need servicing.

Predictive maintenance is the solution to both of these subpar methods. So where does deep learning enter this equation? Here’s one example to help illustrate the connection.

It's not uncommon that machine operators know just from the sound of the machine that something is not right, even when all the available sensors fail to raise alarm. In a predictive maintenance model, you can capture what the operator has learned over years of working with the machine in question, creating a unique opportunity to improve the machine’s uptime and lifespan. Deep learning's ability to automatically learn machine audio patterns opens the door to significantly improve maintenance procedures. Similar procedures use image or video (such as thermal imaging) to achieve the same outcome.

Regression analysis on images, in particular, gives information about the level to which a machine is broken — not just that it is broken. For instance, when monitoring solar panels, you can use images to predict the level of damage they’ve received, which lends nuance over a flat yes-or-no answer.

Here’s another interesting use case for deep learning. Within the domain of predictive maintenance, companies can ensure less risky field inspections to create a better working situation for all employees. And with the continuous monitoring that deep learning enables, you can get a much higher level of quality than with random sample checks.

Get more insight into how Peltarion supports manufacturing and predictive maintenance

The Peltarion Platform is set up to support deep learning methods and replicate groundbreaking research results, while enabling non-experts to both apply and better understand the deep learning process. For a deeper dive into how Peltarion can support predictive maintenance use cases, view our recorded webinars about Operational AI in action for Manufacturing.

AI-enhanced predictive maintenance... will reduce annual maintenance costs by 10%, downtime by 20% and inspection costs by 25%

McKinsey

02/ Read about industries using predictive maintenance