Industrial Robot Anomaly Detection: Failures Predicted 5 Days Ahead

In today's automated automotive manufacturing environment, multiple industrial robots execute vital processes such as assembly, painting, and welding at every stage of the production line. With over 10,000 robotic arms in operation, unexpected failures occur annually, resulting in process downtime. Striking the right balance between maintenance needs, cost constraints, and equipment lifespan is imperative. Optimizing predictive maintenance is crucial for sustaining the efficiency of robotic equipment in production.

By leveraging our self-developed anomaly detection algorithm, rooted in unsupervised learning, we successfully identified equipment anomalies well in advance. Through the creation of customized models tailored to specific production processes and environments, we detected equipment anomalies at least five days ahead of time. Additionally, monitoring and managing over 400 robotic arm models in a unified environment enabled proactive preparation for failures, resulting in a significant reduction in downtime and a substantial boost in productivity.