Machine Learning (Industrial)
In industry, machine learning trains algorithms on historical sensor and process data to predict failures, detect anomalies, optimise set-points and forecast quality — without being explicitly programmed with the underlying physics. It powers most modern predictive and optimisation tools.
Industrial machine learning learns the normal behaviour of a process or machine from data, then flags deviations or predicts outcomes. Applications include predicting equipment failure, detecting process anomalies, optimising energy and yield, and forecasting product quality. Its accuracy depends on data quality, labelled examples of faults, and domain expertise to frame the problem and interpret results.
Related reading
- Predictive maintenance: a practical guide
- Augury
- AVEVA Predictive Analytics
- Anomaly Detection
- Predictive Maintenance (PdM)
- Digital Twin
Related terms
Anomaly Detection · Predictive Maintenance (PdM) · Digital Twin · Edge Computing (Industrial) · Proximity Sensor · Time-Series Database · Time-Series Forecasting
Related guides
Software
Augury
Machine health monitoring for rotating equipment using vibration and AI.
AVEVA Predictive Analytics
Early-warning analytics for critical process and power assets.
Siemens Senseye Predictive Maintenance
Scalable predictive maintenance that learns from existing condition data.
Where this applies
State of AI in the Chemical Industry 2026 · State of AI in Mining 2026 · State of Industrial Supply Chain & Logistics AI 2026 · How fast the industrial-AI market is growing · Organisational AI adoption: context for manufacturers · Industrial-tech markets at a glance · Industrial robot installations worldwide · Robot density in manufacturing