Anomaly Detection in Robotic Aerospace Drilling using Data Driven Methods*

Demand for automation of the repetitive drilling operations in aerospace assembly is growing. With millions of holes per airframe, critical skills shortages, and complicated products, the case for automation is strong. However, industrial robots are not equipped to detect the wide range of possible anomalies such as tool damage or poor process conditions during drilling. The need for trustworthy monitoring is a serious barrier to adoption – a key enabler is that anomalies are detected early, preventing damage to components, machinery, or operators. Existing research shows that machine learning can be highly capable in anomaly detection in industrial processes, and this work focuses on best applying it to the specific anomaly conditions of a robotic drilling cell. This paper investigates a method using clustering and proposes a second classification stage, on motor current data from a novel robotic drilling cell. Time series data is collected on 109 example cycles and 18 artificial anomalies including tool breakage, incorrect parameters, and workpiece defects. A comparison of methods is performed to select the best foundation for a solution. All implementations show parity or improvement over the current standard in drilling, with accuracies over 90% attainable with all methods given sufficient data. Comparison shows that the clustering methods performed the best, with an achievable accuracy of 100% on the tested anomalies and no false alarms. The limitations of the system are identified and possible methods to improve accuracy are discussed. The impact of training data size on performance indicators is investigated and compared.