Nexceris Innovation Holdings, LLC

Nexceris, LLC

Nicholas Brannigan Frank

Stephen Randall Cummings

Scott Lawrence Swartz

Bradley Charles Glenn

William John Dawson

Davion Matthew Hill

Benjamin H Gully

This disclosure relates to systems and methods for monitoring and classifying released gases in an enclosed system having a gas source, by a gas sensor that has been a priori pre-trained to distinguish an off-gas event (OGE) or a thermal run off event (TRE) from non-OGE interfering gases release. The pre-training utilizes one of a machine learning (ML) or a deep learning (DL) algorithm to pre-train the gas sensor to detect a plurality of known gas analyte to generate sensor signals with respective unique characteristics, extracting features from the sensor signals to establish a decision boundary or an estimated probability of a false positive release of the OGE or the TRE from the non-OGE type of interfering gas release. The established decision boundaries or probability distributions are implemented as candidate model for field deployment to classify the released gases to distinguish whether OGE or TRE takes place.

Systems and methods for monitoring a gas analyte

Systems and methods are described herein for monitoring a gas source for a gas analyte. The gas source can be monitored for release of the gas analyte, for example, during a given gas source state. A sensor signal can be generated characterizing an amount of the gas analyte being released by the gas source. The gas sensor signal can be evaluated relative to a threshold. An alert signal can be generated based on a result of the comparison to provide a warning that unwanted and/or hazardous amounts of gas is being released by the gas source.

Systems and methods for monitoring for a gas analyte

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Columbus, OH, United States of America

Nicholas Brannigan Frank