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Predictive maintenance uses sensors to monitor a machine's vibration, temperature and current continuously, so developing faults are spotted before they cause a breakdown. Work is planned around the machine's actual condition rather than the calendar, turning unplanned downtime into planned stops. We start with a pilot on 1–3 critical machines and show results against metrics within 30–60 days.
Most maintenance teams are caught between two approaches. Run-to-failure stops the line and brings rush parts, overtime and missed shipments with it. Calendar-based maintenance replaces parts that were still fine and still misses faults that develop between services. Predictive maintenance lets you plan work around the machine's actual condition rather than the calendar.
We fit sensors to your critical machines, collect the data continuously and use an AI model to watch for departures from each machine's normal behaviour. When a problem such as bearing wear, imbalance or overheating starts to develop, your maintenance team is alerted and the repair moves into a planned stop instead of a breakdown. The same team designs and installs everything from the sensors to the software.
Vibration, temperature, current and, where needed, oil-quality sensors are fitted to critical equipment such as motors, pumps, compressors, bearings and gearboxes. Wired or wireless, depending on the site.
Sensor data is pre-processed on an edge gateway on site and sent to the cloud or a local server. If the internet connection drops, data is held locally and sent once it returns.
We first establish each machine's normal operating profile. Changes in the vibration spectrum, bearing-frequency anomalies and temperature rise are then assessed together to identify the likely fault type and how urgent it is.
When a threshold is crossed, the maintenance team receives an email, SMS or app notification. If you use a CMMS, a work order is raised automatically.
On the compressor that supplies the line with compressed air, bearing frequencies start rising in the vibration spectrum and the temperature climbs above the machine's normal profile. The model assesses both signals together; the maintenance team is alerted and, if you use a CMMS, a work order is raised automatically. The replacement bearing arrives in time and the swap is scheduled for the next planned stop. Without predictive maintenance, the same fault would have stopped the line mid-production; now the repair fits around the production schedule.
Number and length of stops on pilot machines, before and after
How far ahead a problem was flagged before it became a failure
Emergency call-outs, overtime and spare-part spend
The first period in which the pilot's value shows in the metrics
There is no need to instrument the whole plant at once. We start with the 1–3 critical machines that cause you the most downtime, review the results against the metrics in the first 30–60 days, and you decide on rollout based on that data. To combine downtime and efficiency data with production tracking, see our MES production management solution; to show machine data on your existing control room screens, see our SCADA and HMI software. To track motor current alongside energy consumption, see our energy monitoring system; where standard sensors are not enough, our custom device and IoT manufacturing service takes over.
Related guides: what predictive maintenance is and how to run a pilot, vibration analysis for bearing fault detection, CMMS maintenance software explained. For more, browse our Industry 4.0 guides.
Let us identify which machine costs you most when it fails, then set out the pilot's scope, the metrics we will track and the cost in a written proposal.
Predictive maintenance uses sensors to monitor a machine's vibration, temperature and current continuously, so developing faults are spotted before they cause a breakdown. Work is planned around the machine's actual condition rather than the calendar. When a problem such as bearing wear, imbalance or overheating starts to develop, the maintenance team is alerted and the repair moves into a planned stop.
Preventive maintenance follows a calendar: it replaces parts that are still fine and can miss faults that develop between services. Run-to-failure stops the line and brings rush parts and overtime. Predictive maintenance plans work around the machine's measured condition, so parts are changed when they need to be and faults are caught while they are still developing.
Vibration, temperature, current and, where needed, oil quality sensors are fitted to critical equipment. Data is pre-processed on an edge gateway and sent to the cloud or a local server; if the connection drops it is buffered locally. An AI model learns each machine's normal profile and flags departures from it. When a threshold is crossed the team is notified, and a work order can open automatically in your CMMS.
Critical equipment, rotating equipment in particular: AC/DC motors and gearboxes, pumps, compressors and vacuum pumps, fans and ventilation units, CNC machine spindles and conveyors. Transformers and power electronics, generators and UPS systems can also be monitored. Wired or wireless sensor connections are chosen to suit the site.
Yes. There is no need to instrument the whole plant at once. We start with the 1–3 critical machines known to cause the most downtime. The pilot measures unplanned stops and their duration, how early problems are spotted and maintenance spend. Results are reviewed against these metrics within the first 30–60 days, and you decide on rollout based on that data.
The cost depends on how many machines go into the pilot, the sensor types required and connectivity conditions on site. During a free site assessment we choose with you the 1–3 critical machines that cause the most downtime, then set out the scope, the metrics we will track, the phases and the cost in a written proposal. The rollout decision is based on the pilot's results in the first 30–60 days.
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