Industrial Robot Anomaly Detection: Failures Predicted 5 Days Ahead

  • Automobile
  • Anomaly detection


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.


MakinaRocks leveraged its advanced unsupervised learning-based anomaly detection algorithm to address this challenge. By employing a deep learning-based model, we meticulously analyzed and learned the data distribution patterns of robotic arms during normal operation. This approach enables us to accurately predict critical failures well in advance, thereby allowing for proactive maintenance interventions. Moreover, we implemented a sophisticated AI operating environment (MLOps) to seamlessly manage a diverse fleet of robotic arms sourced from various manufacturers.

Value Delivered

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 from diverse manufacturers in a unified environment, we enabled proactive preparation for failures. This resulted in a significant reduction in downtime and a substantial boost in productivity.

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