ServicesV — Industrial Applications: Production, Quality and Warehouse
Failure Prediction and Maintenance Planning from Sensor Data
Maintenance happens either too early — unnecessary downtime and parts — or too late, as unplanned downtime. The signals that precede a failure are already in the sensor data, but nobody looks: the data stays in the PLC or the SCADA archive while the maintenance team works to a calendar.
We bring the machine’s sensor streams (vibration, temperature, current, pressure, cycle time) together with its maintenance and failure history in a single time-series layer. On top of that we build anomaly detection first, and remaining-useful-life prediction where there are enough recorded failures to learn from. The alert does not land on a dashboard; it lands in the work-order flow the maintenance team already uses.
Maintenance decided by the machine’s own data rather than by the calendar. Every alert carries which measurement crossed which threshold and when, so the technician trusts the data rather than the model.
You can start if the machine emits data at all — PLC, SCADA, an IoT gateway or a separate sensor — and if past failures were recorded. With no failure history the first stage is anomaly detection only; a prediction model needs labelled failures first.
We begin with one line or one family of machines: which sensor, which sampling rate, which failure mode. Once collection runs, we backtest against history — before anything reaches the floor we measure how many days ahead the model would have seen last year’s failure. Thresholds and the work-order link come after that.
Installing sensors and field hardware is not part of this service; we take data from the automation you already have. Nor do we claim any model sees every failure: the backtest shows which failure modes are visible in the data, and the ones that are not get written down.