Predictive maintenance ensures that problems do not arise, but it does mean that time and money are spent on things that may not have needed to be fixed yet. With the help of AI, companies can make predictive maintenance more accurate, thereby saving both time and money.
Predictive maintenance has one big objective: maintain things well to prevent breakages, which could affect production, costs, and safety
“Sooner or later, all components will wear out and need to be replaced, which is why predictive maintenance is needed,” says Madhav Mishra, senior scientist at RISE.
But the problem with traditional preventive maintenance is that it's hard to know when it's actually needed. Companies have tried to solve this by doing it at certain time intervals, with the result that they either spend time and resources on things that there was absolutely nothing wrong with or, even worse, they are too late. The part has already broken.
“If this is on an aircraft up in the air, for example, things could be really bad, but it can also lead to big problems in less dangerous situations.”
AI gives accuracy
To make predictive maintenance more accurate, several companies today use AI and machine learning, so as to be able to predict more precisely when efforts are needed.
“By using AI to analyse the large amounts of data that virtually all companies have access to today, they can create better routines for their predictive maintenance. Then they can predict when something will break, order spare parts in time and avoid large costs caused by production stoppages.”
By using AI and data analysis, companies can move from just selling products to also selling services as well
Knowledge and tools the key to getting started
Some companies have made significant progress in this area, while others have not even considered it yet. So the question is: what is needed to get started?
"On the one hand, you need the right tools and sufficient computational power to use them, and on the other hand, you need the right knowledge. We see how many companies build knowledge in data analysis by hiring data scientists, who can do the analysis and can help companies create new concepts and processes.
In other words, it takes some effort to be able to start using AI in predictive maintenance but, according to Madhav Mishra, the benefits are great once you get started. Partly because you save costs, but also because it provides the opportunity to develop new business models. As Artificial intelligence (AI) is a broad concept that applies to a wide range of technologies It can be anything from statistical analysis to machine learning, deep learning, federated learning also known as collaborative learning or even meta-heuristics, where problem-independent techniques are applied to a wide range of problems.
“By using AI and data analysis, companies can move from just selling products to also selling services as well. Instead of just selling a product, you can also sell service, with guaranteed uptime, something that raises the value in the market.”
RISE offers expertise
To help companies get started with using AI in predictive maintenance, RISE offers a range of experts both in maintenance work and in data analysis and AI.
“With the aid of our AI centre, we can offer help no matter how far the company has come. We have a clear model that we work from and can, for example, arrange workshops where we work with the customer to investigate their needs or we put together teams to analyse different forms of problems. The important thing is to start right now, because AI as part of predictive maintenance will be very important in the future.”