Swiss Energy Company

Building a predictive maintenance model to detect abnormal measurements earlier, leading to better maintenance planning and cost reduction

Challenge

• Currently no reliable long-term anomaly detection system
• Slow-shifting anomalies are hard to detect with human surveillance as they can take months
• Goal to have a predictive maintenance system to spot these issues early and plan maintenance more effectively

Solution

• Unit8 built a predictive machine learning model for physical variables of interest (e.g., temperature or vibration)
• Define a threshold for values to be throughout the day and sends automatic alerts if a value differs significantly from model predictions

Business impact

• Engineers able to detect abnormal changes in measurements much earlier
• Maintenance can now be planned effectively
• Shorter stops in electricity production leading to lower costs due to plant shutdown