公开(公告)号：US11933774B1

公开(公告)日：2024-03-19

申请号：US18060111

申请日：2022-11-30

Applicant: Picarro Inc.

Inventor： Sean MacMullin ,  Chris W. Rella ,  Aaron Van Pelt ,  Alex Balkanski ,  Yonggang He ,  Sze M. Tan ,  David Steele ,  Tim Clark

IPC: G01N33/00 ,  G01M3/04

CPC classification number: G01N33/0075 ,  G01M3/04 ,  G01N33/0047

Abstract: In some embodiments, data from multiple vehicle-based natural gas leak detection survey runs are used by computer-implemented machine learning systems to generate a list of natural gas leaks ranked by hazard level. A risk model embodies training data having known hazard levels, and is used to classify newly-discovered leaks. Hazard levels may be expressed by continuous variables, and/or probabilities that a given leak fits within a predefined category of hazard (e.g. Grades 1-3). Each leak is represented by a cluster of leak indications (peaks) originating from a common leak source. Hazard-predictive features may include maximum, minimum, mean, and/or median CH4/amplitude of aggregated leak indications; estimated leak flow rate, determined from an average of leak indications in a cluster; likelihood of leak being natural gas based on other indicator data (e.g. ethane concentration); probability the leak was detected on a given pass; and estimated distance to leak source.

公开(公告)号：US11525819B1

公开(公告)日：2022-12-13

申请号：US17249795

申请日：2021-03-12

Applicant: Picarro Inc.

Inventor： Sean MacMullin ,  Chris W. Rella ,  Aaron Van Pelt ,  Alex Balkanski ,  Yonggang He ,  Sze M. Tan ,  David Steele ,  Tim Clark

IPC: G01N33/00 ,  G01M3/04

Abstract: In some embodiments, data from multiple vehicle-based natural gas leak detection survey runs are used by computer-implemented machine learning systems to generate a list of natural gas leaks ranked by hazard level. A risk model embodies training data having known hazard levels, and is used to classify newly-discovered leaks. Hazard levels may be expressed by continuous variables, and/or probabilities that a given leak fits within a predefined category of hazard (e.g. Grades 1-3). Each leak is represented by a cluster of leak indications (peaks) originating from a common leak sources. Hazard-predictive features may include maximum, minimum, mean, and/or median CH4/amplitude of aggregated leak indications; estimated leak flow rate, determined from an average of leak indications in a cluster; likelihood of leak being natural gas based on other indicator data (e.g. ethane concentration); probability the leak was detected on a given pass; and estimated distance to leak source.

公开(公告)号：US10948471B1

公开(公告)日：2021-03-16

申请号：US15996069

申请日：2018-06-01

Applicant: Picarro Inc.

Inventor： Sean MacMullin ,  Chris W. Relia ,  Aaron Van Pelt ,  Alex Balkanski ,  Yonggang He ,  Sze Tan ,  David Steele ,  Tim Clark

IPC: G01N33/00 ,  G01M3/04

Abstract: In some embodiments, data from multiple vehicle-based natural gas leak detection survey runs are used by computer-implemented machine learning systems to generate a list of natural gas leaks ranked by hazard level. A risk model embodies training data having known hazard levels, and is used to classify newly-discovered leaks. Hazard levels may be expressed by continuous variables, and/or probabilities that a given leak fits within a predefined category of hazard (e.g. Grades 1-3). Each leak is represented by a cluster of leak indications (peaks) originating from a common leak sources. Hazard-predictive features may include maximum, minimum, mean, and/or median CH4/amplitude of aggregated leak indications; estimated leak flow rate, determined from an average of leak indications in a cluster; likelihood of leak being natural gas based on other indicator data (e.g. ethane concentration); probability the leak was detected on a given pass; and estimated distance to leak source.