12 January 2026
Steve McCallion outlines how the Industrial Internet of Things (IIoT) is changing condition monitoring function. He also highlights some of the challenges and benefits.
Unplanned downtime remains one of the biggest risks for operators across all industrial sectors. Equipment failure not only halts production but also drives up costs, impacts safety, and can damage reputation. Rotating machinery such as motors, pumps, and bearings are particularly vulnerable, and when they fail unexpectedly, the consequences for output and profitability can be significant.
In recent years, advances in IIoT technologies have transformed the way manufacturers and plant operators approach asset health. By shifting from periodic, manual inspections to continuous, data-driven monitoring, organisations are now able to predict failures before they occur, unlocking new levels of efficiency and reliability.
IIoT has made condition monitoring more accessible, scalable, and effective. Wireless sensors measuring vibration, bearing condition, temperature, and runtime can now be deployed across hundreds of assets at a lower cost, without the need for complex cabling. This shift has enabled engineers and maintenance teams to capture a continuous stream of health data from critical machinery, covering more equipment than ever before.
The addition of edge analytics means data can be filtered locally, reducing the volumes sent to the cloud while still generating real-time alarms in areas with limited connectivity. Cloud platforms are then able to consolidate this data across multiple sites, providing trend analysis, dashboards, and role-specific alerts. Increasingly, machine learning models are identifying patterns beyond simple thresholds, detecting early failure signatures and driving more precise interventions.
Rubix takes a platform approach to condition monitoring which combines on-site data capture with centralised analytics, supported by expert engineers. This can help move maintenance teams from having a focus on reactive repair and move towards predictive planning – prioritising high-risk assets and scheduling necessary maintenance work in a timely manner before failures occur.
The benefits of IIoT-enabled condition monitoring are already evident. Organisations adopting this approach typically report reduced unplanned downtime, as early warnings shorten repair planning windows and prevent catastrophic failures. By shifting to planned interventions, maintenance costs per asset fall, reliance on emergency callouts decreases, and assets last longer due to fewer unnecessary overhauls.
Continuous monitoring
Continuous monitoring can also highlight production inefficiencies. Issues such as imbalance or misalignment in pumps and motors, for example, can increase energy consumption, and so addressing these issues can protect equipment and reduce operating costs.
Importantly, the technology is also changing how maintenance teams operate. With reliable data in hand, technicians can move away from routine inspections and focus instead on diagnostics, spare parts planning, and process improvements. Rubix’s combination of digital tools and on-site expertise ensures customers can reallocate their skills where they create the greatest value.
Overcoming challenges
Despite the progress, several barriers remain. Integrating condition monitoring outputs into legacy control systems, enterprise resource planning (ERP), and maintenance workflows can be complex, especially where plants operate a wide range of assets. Continuous sensing generates vast amounts of data, and if the right strategies are not in place, managing these streams can become overwhelming. Connectivity limitations mean that edge processing is essential in many environments.
Perhaps the most significant challenge lies in people and processes. Interpreting advanced diagnostics and acting on predictive alerts requires training and cultural change. Without developing these skills, organisations risk underutilising the potential of the technology.
Looking forward, hybrid edge/cloud analytics will become standard, sensor costs will continue to fall, and machine learning will evolve into prescriptive maintenance – recommending specific corrective actions and linking directly with procurement and scheduling.
A practical way forward
The best approach is to start small with a focused pilot. By targeting a handful of high-impact assets, organisations can quickly demonstrate measurable benefits – such as reduced downtime or fewer emergency callouts – that build confidence and justify scaling up.
Steve McCallion is Regional Technical Sales Director at Rubix UKII.
