How AI is Changing Maintenance in Manufacturing

Artificial intelligence, or AI, has dominated the news of late. You’ve likely heard of ChatGPT, Sydney and other platforms that use AI-driven natural language processing tools to revolutionize the way companies interact with customers and the way we humans interact online.

There is another very exciting application of artificial intelligence that’s gaining traction among manufacturers. New AI-driven tools are analyzing mountains of equipment and environmental data to identify and address maintenance issues before they ever happen. This predictive approach holds a lot of promise for manufacturers because it can reduce both costs and downtime.

Predictive vs Preventive Maintenance

Before we get into the benefits of predictive maintenance, let’s briefly talk about the differences between predictive and preventive maintenance. Both concepts focus on scheduled maintenance for equipment. Preventive maintenance is done on set schedules based on best practices for how a piece of machinery is typically used. AI-driven predictive maintenance, on the other hand, analyzes the actual use of a machine so that maintenance is only done when it’s needed.

With predictive maintenance, the AI platform looks at things like the age of the equipment, how much it’s been used, the operating conditions, data generated by the equipment itself (both current and historical data), and even audio and video data from sensors in the manufacturing space to create an individualized plan for each specific piece of machinery. By quickly analyzing this massive amount of data in real-time, AI can alert management to anomalies or emerging issues before they become a crisis

The Benefits of AI-Driven Predictive Maintenance

Once you know there’s an issue, your maintenance teams can address it in a timely manner. This leads to all manner of other ancillary benefits such as:

Longer lifecycles. Well-maintained equipment lasts longer. Predictive maintenance can increase a machine’s life by 20% to 40%.

Lower operating costs. Predictive maintenance has been shown to reduce overall maintenance costs by 5% to 10%. This is in part because it can minimize the impacts of cascading damage, which occurs when the failure of one part subsequently damages others.

Improved energy efficiency. Well-maintained equipment tends to be more energy efficient, which in turn lowers a company’s operating expenses.

Less downtime. Predictive maintenance improves uptime by 30% to 50%. If a piece of machinery breaks unexpectedly, downtime for repairs can be significant. Addressing potential issues before they become a crisis saves the company time, and ultimately mitigates lost revenues associated with the machine being out of service.

Improved productivity. Predictive maintenance increases productivity by 25%, according to experts, largely because maintenance is only performed when it’s needed. AI can also perform a root-cause analysis on recurring issues and recommend operational changes that optimize productivity.

Improved safety. Predictive maintenance can be used to develop safeguards and alerts that trigger inspections. Depending on the piece of equipment, a sudden failure can pose a safety risk for the operator, so addressing issues before they become a problem creates a safer working environment.

Improved quality. It’s critical that precision machines operate up to specification, or else the quality of the finished product declines. Predictive maintenance can look at things like vibrations, sounds, electrical current, and more to ensure the equipment is performing as expected — and turning out high quality products.

Naturally, there are costs associated with installing sensors and software to gather and analyze data, but predictive maintenance offers manufacturers a great opportunity to streamline operations and reduce costs.