Echo State Networks: A Non-Intrusive Approach to Anomaly Detection in Manufacturing

Kendric Hood, Kelin Rumsey, Mark Cola, Bruce Madigan, Christina Scovel, Mike McAninch, Nathan DeBardeleben

Abstract: This paper investigates the applicability of artificial neural network (ANN) for developing non-destructive tests (NDT) with nonintrusive load monitoring (NILM) in manufacturing. Specifically, with Gas metal arc welding (GMAW). For GMAW it is shown that power drawn by the welder is sufficient to accurately identifying anomalies with an ANN. ANNs can utilize raw data without requiring subject matter experts for preprocessing or feature engineering. Echo State Networks (ESN) can learn from only one data point, one example weld. This is due to their use of the pseudo-inverse matrix method for training. Allowing implementation of NILM in a wide range of manufacturing processes where large amounts of training data are unavailable or impractical to collect. The comparative analysis shows models that train with backpropagation, such as transformers, demand a large amount of training data to get results similar to ESNs, thus they are unrealistic in scenarios with limited training data availability.CCS Concepts• Applied computing → Industry and manufacturing; • Computing methodologies → Machine learning; Artificial intelligence.

External IDs:dblp:conf/sc/HoodRCMSMD24