公开(公告)号：US20170024649A1

公开(公告)日：2017-01-26

申请号：US14808402

申请日：2015-07-24

Applicant: General Electric Company

Inventor： Weizhong Yan ,  Lijie YU

IPC: G06N5/04 ,  G06N99/00

CPC classification number: G06N3/0454 ,  G05B23/0283 ,  G06N3/0445

Abstract: Some embodiments are associated with a receipt, at a feature learning platform, of sensor data associated with normal operation of an industrial asset, the sensor data including values for a plurality of sensors over a period of time. The feature learning platform may extract a plurality of features via hierarchically deep learning, which may capture characteristics of normal operation of the industrial asset and provide the learned features to a classification modeling platform. The classification modeling platform may then create classification models utilizing the learned features, and the classification models may be executed to automatically identify a potential anomaly for an operating industrial asset.

Abstract translation: 一些实施例与在特征学习平台处的与工业资产的正常操作相关联的传感器数据的收据相关联，所述传感器数据包括一段时间内的多个传感器的值。 特征学习平台可以通过分级深度学习提取多个特征，其可以捕获工业资产的正常操作的特征并将学习的特征提供给分类建模平台。 然后，分类建模平台可以使用学习的特征来创建分类模型，并且可以执行分类模型以自动识别经营的工业资产的潜在异常。