AIoT & Predictive Maintenance

Start with the maintenance decision

A changing vibration pattern or rising operating temperature may deserve attention, but the reading alone does not explain the cause. The useful question is what a technician can investigate or do differently with that information.

We work through the equipment, available sensors and maintenance process before choosing a model. Operating conditions matter: a change caused by a heavier workload should not automatically be treated as a developing fault.

Separate unusual behaviour from a predicted failure

An anomaly model can highlight readings that differ from an established pattern. Predicting a particular failure is a stronger claim and usually needs relevant failure examples and maintenance records.

We review what the data can support. For some equipment, a well-designed threshold or trend rule is a useful first step. For others, a model combining several signals may offer additional warning. We compare the result with a practical baseline.

Measure whether the warning helps

A maintenance pilot needs more than a graph. We look at how much notice a warning provides, how many unnecessary investigations it creates and which known events it misses. Those measures help the team judge whether the system is worth using.

The result should fit the maintenance workflow: a clear alert with supporting history, a place to record the investigation and feedback on what was found. Start with one equipment type and a defined operating context before expanding across the fleet.