In industrial manufacturing, unexpected downtime remains one of the most significant risks to productivity and cost efficiency. Traditional maintenance strategies, whether reactive or based on fixed-time intervals, often result in parts being replaced when they do not need to be, or early warning signs slipping through the cracks until they turn into full-blown failures.
Digitalisation is putting maintenance on the front foot, enabling a more data-driven, proactive approach. With continuous condition monitoring and a helping hand from arificial intelligence (AI), maintenance teams can spot problems before they occur and rely on equipment that always performs, come rain or shine — and it is not just about smarter tools; it is about smarter strategy. Going digital lets manufacturers cut down on costly disruptions and downtime, as well as trim energy use in the process.
Pictured right: Oswald Deuchar, global head of modernisation services, ABB Motion ServicesMaintenance has historically relied on visual checks, handwritten logs, and scheduled component replacement based on informed guesswork. Run-to-fail approaches wait for breakdowns to spur action, while time-based maintenance swaps out parts at fixed intervals, whether they need it or not.
Both methods leave room for error, leading to over-servicing in some cases and overlooked issues in others. That is where digital condition monitoring steps in, offering a clear line of sight into the actual health of equipment. These systems help operators make decisions based on data rather than assumptions, replacing reactive responses with targeted, timely actions that keep operations running smoothly and efficiently.
ABB Ability Condition Monitoring for measurement devices is one such approach. This is an on-premise solution which uses edge technology and data analysis to continuously monitor and assess the condition of installed ABB analysers. The data is then used to detect anomalies and trends that may indicate early-stage faults, with automated reports that can be scheduled, requested on demand, or triggered by specific events.
Remote troubleshootingThe solution also allows for troubleshooting the cause remotely, enabling experts to provide support without needing to be physically on site — particularly useful for installations in hard-to-reach or hazardous areas. Overall, condition monitoring helps industrial players save time, money and effort.
While condition monitoring lays the groundwork for more proactive, data-informed maintenance, AI is adding a whole new layer of insight. To support this next step, ABB recently invested in the AI start-up UptimeAI. By providing actionable insights into the health and performance of rotating equipment, such as motors and drives, the partnership aims to help companies achieve operational excellence, reduce unplanned downtime, and enhance overall productivity.
The collaboration combines industrial know-how with advanced AI and machine learning-based algorithms, to boost failure prediction, health forecasting, and maintenance optimisation. It uses a fault library covering more than 1,000 known failure modes and brings together machine learning and expert systems to enhance asset management capabilities.
The initiative will initially be rolled out with heavy industry customers in India, focusing on sectors such as cement, metals, and water infrastructure — industries where uninterrupted operation is essential, and early warnings can make a world of difference.
Digitalisation is no longer a distant goal for industrial maintenance; it is already reshaping how teams manage equipment and slash downtime. With real-time insights and predictive analytics, manufacturers can shift from reacting to failures to anticipating them.
Tools like condition monitoring and AI-powered diagnostics signal where the industry is headed: toward equipment that is not just repaired when it needs to be, but always improving to run efficiently and with fewer surprises along the way. For asset-heavy industries, this kind of proactive approach is critical, and it is fast becoming a cornerstone of resilient, future-ready manufacturing.