Anomaly Detection and Concept Drift Adaptation for Dynamic Systems: A General Method with Practical Implementation Using an Industrial Collaborative Robot

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-03, Vol.23 (6), p.3260
• Main Authors: Kermenov, Renat, Nabissi, Giacomo, Longhi, Sauro, Bonci, Andrea
• Format: Article
• Language: English
• Subjects: Adaptation; Algorithms; Anomalies; anomaly detection; Behavior; Collaboration; collaborative robotics; concept drift adaptation; Data acquisition; Data transmission; Dynamical systems; fault detection; Fault diagnosis; Flexible manufacturing systems; Knowledge; machine learning; Robotics; Robots; Sensors; Working conditions
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23063260

Industrial collaborative robots (cobots) are known for their ability to operate in dynamic environments to perform many different tasks (since they can be easily reprogrammed). Due to their features, they are largely used in flexible manufacturing processes. Since fault diagnosis methods are generally applied to systems where the working conditions are bounded, problems arise when defining condition monitoring architecture, in terms of setting absolute criteria for fault analysis and interpreting the meanings of detected values since working conditions may vary. The same cobot can be easily programmed to accomplish more than three or four tasks in a single working day. The extreme versatility of their use complicates the definition of strategies for detecting abnormal behavior. This is because any variation in working conditions can result in a different distribution of the acquired data stream. This phenomenon can be viewed as concept drift (CD). CD is defined as the change in data distribution that occurs in dynamically changing and nonstationary systems. Therefore, in this work, we propose an unsupervised anomaly detection (UAD) method that is capable of operating under CD. This solution aims to identify data changes coming from different working conditions (the concept drift) or a system degradation (failure) and, at the same time, can distinguish between the two cases. Additionally, once a concept drift is detected, the model can be adapted to the new conditions, thereby avoiding misinterpretation of the data. This paper concludes with a proof of concept (POC) that tests the proposed method on an industrial collaborative robot.