公开(公告)号：US20190156429A1

公开(公告)日：2019-05-23

申请号：US15819516

申请日：2017-11-21

Applicant: General Electric Company

Inventor： Benjamin Edward BECKMANN ,  Annarita GIANI ,  John William CARBONE

IPC: G06Q40/00 ,  G06Q20/40

Abstract: An original transaction data store may provide information about a series of transactions over time. A transaction data collection platform may group some of those transactions into a subset of transactions and apply a hash function to that subset to create a signature representing verifiable state information associated with an enterprise. The transaction data collection platform may then record information about the signature via a secure, distributed transaction meta-ledger without recording the subset of transactions. According to some embodiments, a transaction audit platform may retrieve the signature from the meta-ledger and retrieve original information associated with the subset of transactions (e.g., from an offline original transaction data store). The transaction audit platform may then apply the hash function to the original information to re-create an original signature, compare the retrieved signature with the original signature, and output an indication of a verification result of the comparison.

公开(公告)号：US20190230099A1

公开(公告)日：2019-07-25

申请号：US15977558

申请日：2018-05-11

Applicant: General Electric Company

Inventor： Lalit Keshav MESTHA ,  Masoud ABBASZADEH ,  Annarita GIANI

IPC: H04L29/06 ,  G06N7/00

Abstract: Streams of monitoring node signal values over time, representing a current operation of the industrial asset, are used to generate current monitoring node feature vectors. Each feature vector is compared with a corresponding decision boundary separating normal from abnormal states. When a first monitoring node passes a corresponding decision boundary, an attack is detected and classified as an independent attack. When a second monitoring node passes a decision boundary, an attack is detected and a first decision is generated based on a first set of inputs indicating if the attack is independent/dependent. From the beginning of the attack on the second monitoring node until a final time, the first decision is updated as new signal values are received for the second monitoring node. When the final time is reached, a second decision is generated based on a second set of inputs indicating if the attack is independent/dependent.

公开(公告)号：US20200076198A1

公开(公告)日：2020-03-05

申请号：US16489497

申请日：2017-10-12

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Benjamin Edward BECKMANN ,  Annarita GIANI ,  Stephanie KUHNE ,  Peter KOUDAL ,  Dan YANG ,  John William CARBONE ,  Victor Robert ABATE ,  Keith LONGTIN

IPC: H02J3/38 ,  H02J3/32 ,  H02J3/14 ,  H04L9/06 ,  H04L9/32 ,  G06Q20/36

Abstract: A system may include an energy reservoir controller associated with a microgrid's energy reservoir adapted to store energy (e.g., a battery to store electrical energy). A computer processor of the energy reservoir controller may receive indications of digital currency tokens from a token creation platform. At least some of the digital currency tokens may be placed into an available energy container based on an amount of energy stored in the energy reservoir. A consumer within the microgrid may submit a transaction request for energy, and it may be arranged for an amount of energy to be transferred from the energy reservoir to the consumer. Based on the amount of energy transferred to the consumer, a number of digital currency tokens may be moved from the available energy container into a used energy container. Information about the transaction request may then be recorded via a secure, distributed transaction ledger.

公开(公告)号：US20190236489A1

公开(公告)日：2019-08-01

申请号：US15883895

申请日：2018-01-30

Applicant: General Electric Company

Inventor： Peter KOUDAL ,  Walter YUND ,  Annarita GIANI ,  Junrong YAN ,  Dan YANG ,  Benjamin Edward BECKMANN ,  Joseph SALVO ,  John William CARBONE ,  Robert BANKS ,  Patricia MACKENZIE

IPC: G06N99/00 ,  G06F17/30 ,  G06N5/04

CPC classification number: G06N20/00 ,  G06F16/9535 ,  G06N5/04

Abstract: An industrial part modeling system may include a digital twin industrial part modeling platform containing a plurality of learning models, each learning model describing characteristics of an industrial part available to be incorporated into an industrial asset. The system may also include an application server platform and a user interface platform to receive an industrial part search or analysis requests from a user. The application server platform may receive information about the industrial part search or analysis request and execute at least one search or analysis algorithm to evaluate learning models in the digital twin industrial part modeling platform. Based on said evaluation, the application server platform may provide an industrial part search or analysis result report to the user. Moreover, the application server platform may automatically arrange for at least one of a search or analysis algorithm and a learning model to be updated based on interaction with the user.

公开(公告)号：US20190222595A1

公开(公告)日：2019-07-18

申请号：US15958285

申请日：2018-04-20

Applicant: General Electric Company

Inventor： Annarita GIANI ,  Masoud ABBASZADEH ,  Lalit Keshav MESTHA

IPC: H04L29/06 ,  G06K9/62 ,  G06F21/50 ,  G05B19/048 ,  G06F11/00

CPC classification number: H04L63/1425 ,  G05B19/048 ,  G06F11/006 ,  G06F21/50 ,  G06F2201/86 ,  G06K9/6267 ,  G06K9/6297 ,  H04L63/14

Abstract: According to some embodiments, a plurality of monitoring nodes may each generate a series of current monitoring node values over time that represent a current operation of the industrial asset. A node classification computer may determine, for each monitoring node, a classification result indicating whether each monitoring node is in a normal or abnormal state. A disambiguation engine may receive the classification results from the node classification computer and associate a Hidden Markov Model (“HMM”) with each monitoring node. For each node in an abnormal state, the disambiguation engine may execute the HMM associated with that monitoring node to determine a disambiguation result indicating if the abnormal state is a result of an attack or a fault and output a current status of each monitoring node based on the associated classification result and the disambiguation result.