公开(公告)号：US20200241514A1

公开(公告)日：2020-07-30

申请号：US16743301

申请日：2020-01-15

Applicant: ExxonMobil Research and Engineering Company

Inventor： Weike Sun ,  Antonio R. Paiva ,  Peng Xu

IPC: G05B23/02

Abstract: An example method can comprise creating a non-linear neural network based model of a system based on historical operational data of the system and receiving first sensor data from a plurality of sensors associated with the system. Predicted next sensor data can be determined based on the received first sensor data and the non-linear network model. Second sensor data can be received from the plurality of sensors, and a measure of deviation between the predicted next sensor data and the received second sensor data is calculated. In response to the measured deviation exceeding a predefined threshold; it can be determined that a fault has occurred.

公开(公告)号：US11314242B2

公开(公告)日：2022-04-26

申请号：US16743301

申请日：2020-01-15

Applicant: ExxonMobil Research and Engineering Company

Inventor： Weike Sun ,  Antonio R. Paiva ,  Peng Xu

IPC: G05B23/02

Abstract: An example method can comprise creating a non-linear neural network based model of a system based on historical operational data of the system and receiving first sensor data from a plurality of sensors associated with the system. Predicted next sensor data can be determined based on the received first sensor data and the non-linear network model. Second sensor data can be received from the plurality of sensors, and a measure of deviation between the predicted next sensor data and the received second sensor data is calculated. In response to the measured deviation exceeding a predefined threshold; it can be determined that a fault has occurred.

公开(公告)号：US20200048709A1

公开(公告)日：2020-02-13

申请号：US16533857

申请日：2019-08-07

Applicant: ExxonMobil Research and Engineering Company

Inventor： Antonio R. Paiva ,  Giovanni Pilloni

IPC: C12Q1/6876 ,  C12Q1/6869 ,  G16B20/00 ,  G16B30/20 ,  G16B30/10 ,  G16B40/10 ,  G16B50/30

Abstract: Methods and systems for differential expression analysis of ribonucleic acid sequencing (RNA-Seq) data, and in particular to an automated workflow for differential expression analysis of RNA-Seq data. More particularly, an automated workflow for differential expression analysis of RNA-Seq data permitting analysis of pairs of any number of RNA-Seq reads subjected to multiple experimental conditions and tested in replicate.