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UID:submissions.supercomputing.org_SC24_sess757_ws_ai4s106@linklings.com
SUMMARY:EchoStateNetworks:ANon-IntrusiveApproachtoAnomaly  DetectioninManu
 facturing
DESCRIPTION:Kendric Hood (Kent State University, Los Alamos National Labor
 atory (LANL))\n\nThis paper investigates the applicability of artificial n
 eural network (ANN) for developing non-destructive tests (NDT) with non-in
 trusive load monitoring (NILM) in manufacturing. Specifically, with Gas me
 tal arc welding (GMAW). \nFor GMAW it is shown that power drawn by the wel
 der is sufficient to accurately identifying anomalies with an ANN. ANNs ca
 n utilize raw data without requiring subject matter experts for preprocess
 ing 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-i
 nverse matrix method for training. Allowing implementation of NILM in a wi
 de range of manufacturing processes where large amounts of training data a
 re unavailable or impractical to collect. \nThe comparative analysis shows
  models that train with backpropagation, such as transformers, demand a la
 rge amount of training data to get results similar to ESNs, thus they are 
 unrealistic in scenarios with limited training data availability.\n\nTag: 
 Artificial Intelligence/Machine Learning\n\nRegistration Category: Worksho
 p Reg Pass\n\nSession Chairs: Murali Emani (Argonne National Laboratory (A
 NL)); Gokcen Kestor (Barcelona Supercomputing Center (BSC); University of 
 California, Merced); and Dong Li (University of California, Merced)\n\n
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