Predictive analytics for industrial equipment

An R&D prototype of an ML system that predicts the risk of industrial equipment failure from sensor data and technical telemetry

The goal was to build an AI solution that identifies the risk of equipment failure in advance and allows the plant to move from scheduled maintenance to condition-based maintenance.

In the classic approach equipment is serviced on a fixed schedule: some components are replaced earlier than necessary, while other faults are found too late — after the line has already stopped. The result is downtime, emergency repairs and additional costs.

We needed a prototype that analyses streaming sensor data, detects anomalies in equipment behaviour and predicts the probability of failure 48–72 hours ahead.

Project format: R&D prototype of an ML solution
Data type: industrial telemetry and time series
Monitored parameters: vibration, temperature, current, load, rotation speed
Number of parameters: 400+ signals
Test dataset: about 2.5 million records
Forecast horizon: 48–72 hours
Test scenarios: 40
Goal: detect failure risk early and prioritise equipment for maintenance

We designed an ML architecture that works with time series and technical telemetry. Sensor data is cleaned, synchronised, aggregated and checked for outliers. The system then computes features that describe the current condition of the equipment and how its parameters change over time.

The model analyses not only individual sensor readings but also their dynamics: rising vibration, temperature changes under load, unstable current and atypical deviations from normal operation. Based on this it calculates the failure risk and the recommended maintenance priority.

We built a predictive analytics prototype that ingests equipment telemetry, processes time series and calculates the probability of failure for every monitored unit.

First the data is normalised and brought to a single time step. The system then computes aggregated features: averages, deviations, trends, rate of change, anomalous peaks and persistent deviations from the baseline mode.
The forecast combines two models: gradient boosting estimates the risk from engineering features, while a time-series model analyses the sequence of signals and helps detect degradation before it becomes critical.

The results are shown in a monitoring interface: the engineer sees the list of equipment, the current risk, parameter dynamics and the forecast for the next 48–72 hours. An API is available for integration, so events can be pushed into a ticketing system or an industrial dashboard.

The result is an R&D prototype of an ML system for equipment failure prediction. The test dataset covered about 2.5 million telemetry records and more than 400 sensor parameters.

In bench testing the model reached 86% forecast accuracy over a horizon of up to 72 hours. 84% of risk scenarios were identified correctly and one telemetry batch was processed in up to 1.5 seconds.

The prototype makes it possible to spot high-risk equipment early, prioritise maintenance and rely less on manual analysis of readings. The estimated effect of a full rollout is up to 30% fewer unplanned stoppages and up to 20% lower costs from premature component replacement.