How Intelligent Bearings Strengthen the Foundations of AI Manufacturing
Chris Johnson, managing director of bearing specialist SMB Bearings, explains how intelligent bearings enhance predictive maintenance and data accuracy, enabling AI-driven factories to maximise uptime and energy efficiency.
www.smbbearings.com
Artificial intelligence (AI) is no longer a futuristic concept for manufacturing — it’s already at work on factory floors. A survey by the National Association of Manufacturers found that 72 per cent of manufacturers have seen AI cut costs and boost efficiency. But while attention often centres on software and data platforms, it’s important to remember that AI is only as powerful as the machines it monitors. Here
Conversations around AI in manufacturing often focus on data leaks, cloud platforms or software that tracks production trends. But software and algorithms can’t deliver results on their own—they depend on machines that run smoothly, efficiently and reliably. When equipment fails or generates poor-quality data, the advantages of AI quickly disappear.
Research from Deloitte reinforces this, highlighting that three quarters of manufacturers have increased investment in data lifecycle management to support AI adoption. Yet nearly 70 per cent still cite poor data quality and validation as their biggest challenge.
Manufacturers are also taking a more measured approach, prioritising high ROI use cases that can only succeed if they’re built on solid operational foundations. In other words, for AI to deliver on its promise, the focus needs to extend beyond algorithms to the performance of the machines feeding them data.
It’s here, but it needs reliable machinery
Because of this, machinery components play a critical role. Bearings may be among the smallest components in production systems, but they are also some of the most important. Without them, motors, conveyors, robots and pumps would quickly fail. Increasingly, they are not just mechanical parts, but intelligent components providing the very data that fuels AI systems.
The essential function of a bearing is to support moving parts and reduce friction. In simple terms, they keep the machine turning. However, in the context of AI-driven factories, their role is much broader. Bearings are often the first components to show signs of wear, making them natural indicators of a machine’s overall condition.
Even small changes in a bearing, like a rise in temperature, a shift in vibration pattern or a slight increase in noise, can reveal that equipment is under stress. If these changes are ignored, they can quickly escalate into overheating, shaft misalignment or total machine failure. This is why bearings are among the first elements targeted in predictive maintenance programmes.
Instead of waiting for a breakdown or replacing parts on a rigid schedule, manufacturers can monitor bearings to detect early warning signs. That means repairs and replacements happen when they’re needed — not too late, not too soon.
In fact, the National Association of Manufacturers report also highlights predictive maintenance as one of the leading applications of AI in manufacturing, with more than half of manufacturers surveyed calling it a primary use case in their operations.
From mechanical part to intelligent sensor
Until recently, monitoring bearing health required external tools like handheld vibration sensors or scheduled inspections. While these tools are still widely used, many modern bearings are being designed with sensors integrated into them. These intelligent bearings can measure vibration, temperature, load and lubrication in real time.
This turns the bearing into a valuable data source. Instead of acting as a silent component, it becomes a communicator, sharing its condition and helping the wider system understand how a machine is performing.
For example, if a sensor inside a bearing detects a gradual increase in heat, this information can trigger an AI system to flag the likelihood of future failure. Engineers can then investigate and act long before production is disrupted.
The integration of sensors in bearings is part of a wider trend in manufacturing where physical components are becoming digital assets. They contribute to the “digital twin” of a production system, a virtual model that mirrors the real machine’s condition. Bearings, with their constant movement and critical role, provide some of the most useful data to feed these digital twins.
Turning data into uptime
The difference intelligent bearings make is not just technical but also financial. Unplanned downtime is one of the biggest costs in manufacturing. A single hour of lost production in a high-volume plant can run into tens or even hundreds of thousands of pounds. Traditional maintenance approaches — either waiting for breakdowns or replacing parts on fixed schedules — rarely provide the balance between reliability and cost-effectiveness.
By using data from intelligent bearings, manufacturers can move to predictive and even prescriptive maintenance. Predictive maintenance warns that a problem is coming, while prescriptive maintenance goes further by recommending the best course of action.
For instance, if a bearing is showing unusual vibration, the AI system might suggest slowing down the machine temporarily to prevent further damage until a planned replacement can be made.
This targeted approach means fewer unnecessary part changes, better use of maintenance resources and fewer interruptions to production. In effect, the bearing becomes a key player in maximising uptime across the factory.
Efficiency, reliability and sustainability gains
The benefits extend beyond maintenance. Bearings are central to machine efficiency. By reducing friction, they allow motors to consume less energy and prevent systems from overheating. When bearings operate smoothly, they directly support lower power use and more consistent performance.
When this performance data is collected and analysed by AI, manufacturers can gain a clear picture of where energy is being used efficiently and where improvements are possible. For example, if a production line is consuming more electricity than expected, bearing data has the potential to help pinpoint whether the issue lies in misalignment, lubrication problems or overloading.
This insight is particularly important as manufacturers face pressure to meet sustainability targets. As both regulators and customers expect greener manufacturing, the role of these small components becomes more strategic than ever.
Bearings and the path to adaptive factories
Looking forward, intelligent bearings will continue to play a key role as factories evolve beyond today’s automation. The next step for manufacturing is greater adaptability — systems that can adjust to changes in demand, material supply or environmental conditions with minimal human intervention.
For this to happen, machines must be able to sense their own condition and act on that information. Intelligent bearings already provide an example of this. By alerting systems to problems and suggesting actions, they contribute to machines that are not just automated but increasingly self-correcting.
www.smbbearings.com
