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EchoStateNetworks:ANon-IntrusiveApproachtoAnomaly DetectioninManufacturing
SessionAI4S: 5th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications
DescriptionThis paper investigates the applicability of artificial neural network (ANN) for developing non-destructive tests (NDT) with non-intrusive 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.
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.