Industrial businesses generate huge volumes of data — vibration, current, temperature, pressure, images, process logs and more. The challenge is often not collecting data, but turning it into better decisions.
Fault Detection & Predictive Maintenance
Predict machine failures before they happen using variables such as vibration, current, temperature, noise and pressure. Machine learning can identify complex patterns associated with degradation and estimate failure risk — helping reduce catastrophic downtime and unnecessary maintenance.
Anomaly Detection
Detect unusual operating conditions automatically from historical data. Unlike traditional rule-based systems, anomaly detection can identify unexpected behaviour even when examples of previous failures are unavailable.
Sensor Fusion & Calibration
Many industrial systems rely on multiple sensors with different viewpoints, offsets, noise characteristics and nonlinear distortions. Machine learning can combine and calibrate these signals to produce more accurate and reliable measurements and conclusions.
Yield & Process Optimisation
Want to maximise throughput, yield or product quality? Machine learning can learn the relationship between process inputs and outcomes, helping identify operating conditions that improve performance and reduce waste.
Examples include:
• Manufacturing
• Energy systems
• Medical devices
• Scientific instrumentation
• Robotics and automation
Our PhD consultants develop advanced machine learning, optimisation and sensor data processing algorithms to solve real industrial problems.
Learn more about our sensor algorithm consulting services
