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公开(公告)号：US11416326B2
公开(公告)日：2022-08-16
申请号：US17006449
申请日：2020-08-28
Applicant: SAP SE
Inventor： Jie He , Jianwei Chen , Lin Cai , Xiaoling Zhou , Xuemin Wang
IPC: G06F11/07
Abstract: A computer-implemented method for failure diagnosis using fault tree can include: receiving a fault tree comprising a node representing a top event, a plurality of nodes representing respective basic events, and one or more logic gates connecting the plurality of nodes representing the respective basic events to the node representing the top event; obtaining reliability parameters corresponding to the basic events; calculating fault tree importance measures corresponding to the basic events; calculating failure impact factors of the top event corresponding to the basic events, wherein the failure impact factors of the top event are products of the corresponding reliability parameters and the corresponding fault tree importance measures; ranking the basic events based on the failure impact factors of the top event; and identifying a most significant contributor to the top event, wherein the most significant contributor is a basic event having the highest failure cause probability of the top event.
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公开(公告)号：US20220066853A1
公开(公告)日：2022-03-03
申请号：US17006449
申请日：2020-08-28
Applicant: SAP SE
Inventor： Jie He , Jianwei Chen , Lin Cai , Xiaoling Zhou , Xuemin Wang
IPC: G06F11/07
Abstract: A computer-implemented method for failure diagnosis using fault tree can include: receiving a fault tree comprising a node representing a top event, a plurality of nodes representing respective basic events, and one or more logic gates connecting the plurality of nodes representing the respective basic events to the node representing the top event; obtaining reliability parameters corresponding to the basic events; calculating fault tree importance measures corresponding to the basic events; calculating failure impact factors of the top event corresponding to the basic events, wherein the failure impact factors of the top event are products of the corresponding reliability parameters and the corresponding fault tree importance measures; ranking the basic events based on the failure impact factors of the top event; and identifying a most significant contributor to the top event, wherein the most significant contributor is a basic event having the highest failure cause probability of the top event.
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公开(公告)号：US11714738B2
公开(公告)日：2023-08-01
申请号：US17399170
申请日：2021-08-11
Applicant: SAP SE
Inventor： Jie He , Jianwei Chen , Xuemin Wang
CPC classification number: G06F11/3089 , G06F11/0772 , G06F11/3075 , G06F17/142 , G06N3/04 , H03M13/1575
Abstract: Methods, systems, and computer-readable storage media for receiving, by an anomalous operation detection service, current signal data representing a driving current applied to a device over a time period, processing, by an anomalous operation detection service, the current signal data through a deep neural network (DNN) module, a frequency spectrum analysis (FSA) module, and a time series classifier (TSC) module to provide a set of indications, each indication in the set of indications indicating one of normal operation of the device and anomalous operation of the device, processing, by an anomalous operation detection service, the set of indications through a voting gate to provide an output indication, the output indication indicating one of normal operation of the device and anomalous operation of the device, and selectively transmitting one or more of an alert and a message based on the output indication.
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公开(公告)号：US20230047772A1
公开(公告)日：2023-02-16
申请号：US17399170
申请日：2021-08-11
Applicant: SAP SE
Inventor： Jie He , Jianwei Chen , Xuemin Wang
Abstract: Methods, systems, and computer-readable storage media for receiving, by an anomalous operation detection service, current signal data representing a driving current applied to a device over a time period, processing, by an anomalous operation detection service, the current signal data through a deep neural network (DNN) module, a frequency spectrum analysis (FSA) module, and a time series classifier (TSC) module to provide a set of indications, each indication in the set of indications indicating one of normal operation of the device and anomalous operation of the device, processing, by an anomalous operation detection service, the set of indications through a voting gate to provide an output indication, the output indication indicating one of normal operation of the device and anomalous operation of the device, and selectively transmitting one or more of an alert and a message based on the output indication.
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