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公开(公告)号：US11860974B2
公开(公告)日：2024-01-02
申请号：US17090112
申请日：2020-11-05
Applicant: Oracle International Corporation
Inventor： Guang C. Wang , Kenny C. Gross , Zexi Chen
IPC: G06F18/00 , G06F18/214 , G06F11/30 , H04L9/40 , G06N5/04 , G06F18/231 , G06N7/01
CPC classification number: G06F18/2148 , G06F11/3075 , G06F11/3089 , G06F18/231 , G06N5/04 , G06N7/01 , H04L63/1416 , H04L63/1425
Abstract: A system is provided for training an inferential model based on selected training vectors. During operation, the system receives training data comprising observations for a set of time-series signals gathered from sensors in a monitored system during normal fault-free operation. Next, the system divides the observations into N subgroups comprising non-overlapping time windows of observations. The system then selects observations with a local minimum value and a local maximum value for all signals from each subgroup to be training vectors for the inferential model. Finally, the system trains the inferential model using the selected training vectors. Note that by selecting observations with local minimum and maximum values to be training vectors, the system maximizes an operational range for the training vectors, which reduces clipping in estimates subsequently produced by the inferential model and thereby reduces false alarms.
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Patent Agency Ranking
公开(公告)号：US20220138316A1
公开(公告)日：2022-05-05
申请号：US17086855
申请日：2020-11-02
Applicant: Oracle International Corporation
Inventor： Zexi Chen , Kenny C. Gross , Ashin George , Guang C. Wang
Abstract: The disclosed embodiments relate to a system that characterizes susceptibility of an inferential model to follow signal degradation. During operation, the system receives a set of time-series signals associated with sensors in a monitored system during normal fault-free operation. Next, the system trains the inferential model using the set of time-series signals. The system then characterizes susceptibility of the inferential model to follow signal degradation. During this process, the system adds degradation to a signal in the set of time-series signals to produce a degraded signal. Next, the system uses the inferential model to perform prognostic-surveillance operations on the set of time-series signals with the degraded signal. Finally, the system characterizes susceptibility of the inferential model to follow degradation in the signal based on results of the prognostic-surveillance operations.
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公开(公告)号：US11921848B2
公开(公告)日：2024-03-05
申请号：US17086855
申请日：2020-11-02
Applicant: Oracle International Corporation
Inventor： Zexi Chen , Kenny C. Gross , Ashin George , Guang C. Wang
CPC classification number: G06F21/554 , G06F17/16 , G06F17/18 , G06N5/04 , G06N20/00
Abstract: The disclosed embodiments relate to a system that characterizes susceptibility of an inferential model to follow signal degradation. During operation, the system receives a set of time-series signals associated with sensors in a monitored system during normal fault-free operation. Next, the system trains the inferential model using the set of time-series signals. The system then characterizes susceptibility of the inferential model to follow signal degradation. During this process, the system adds degradation to a signal in the set of time-series signals to produce a degraded signal. Next, the system uses the inferential model to perform prognostic-surveillance operations on the set of time-series signals with the degraded signal. Finally, the system characterizes susceptibility of the inferential model to follow degradation in the signal based on results of the prognostic-surveillance operations.
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4.发明授权公开(公告)号：US12038830B2
公开(公告)日：2024-07-16
申请号：US17090151
申请日：2020-11-05
Applicant: Oracle International Corporation
Inventor： Rui Zhong , Guang C. Wang , Kenny C. Gross , Ashin George , Zexi Chen
CPC classification number: G06F11/3688 , G06F11/3692 , G06F21/602 , G06N20/00
Abstract: A double-blind comparison is performed between prognostic-surveillance systems, which are located on a local system and a remote system. During operation, the local system inserts random faults into a dataset to produce a locally seeded dataset, wherein the random faults are inserted into random signals at random times with variable fault signatures. Next, the local system exchanges the locally seeded dataset with a remote system, and in return receives a remotely seeded dataset, which was produced by the remote system by inserting different random faults into the same dataset. Next, the local system uses a local prognostic-surveillance system to analyze the remotely seeded dataset to produce locally detected faults. Finally, the local system determines a performance of the local prognostic-surveillance system by comparing the locally detected faults against actual faults in the remotely seeded fault information. The remote system similarly determines a performance of a remote prognostic-surveillance system.
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公开(公告)号：US20220138499A1
公开(公告)日：2022-05-05
申请号：US17090112
申请日：2020-11-05
Applicant: Oracle International Corporation
Inventor： Guang C. Wang , Kenny C. Gross , Zexi Chen
Abstract: The disclosed embodiments relate to a system that trains an inferential model based on selected training vectors. During operation, the system receives training data comprising observations for a set of time-series signals gathered from sensors in a monitored system during normal fault-free operation. Next, the system divides the observations into N subgroups comprising non-overlapping time windows of observations. The system then selects observations with a local minimum value and a local maximum value for all signals from each subgroup to be training vectors for the inferential model. Finally, the system trains the inferential model using the selected training vectors. Note that by selecting observations with local minimum and maximum values to be training vectors, the system maximizes an operational range for the training vectors, which reduces clipping in estimates subsequently produced by the inferential model and thereby reduces false alarms.
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6.发明申请公开(公告)号：US20220138090A1
公开(公告)日：2022-05-05
申请号：US17090151
申请日：2020-11-05
Applicant: Oracle International Corporation
Inventor： Rui Zhong , Guang C. Wang , Kenny C. Gross , Ashin George , Zexi Chen
Abstract: A double-blind comparison is performed between prognostic-surveillance systems, which are located on a local system and a remote system. During operation, the local system inserts random faults into a dataset to produce a locally seeded dataset, wherein the random faults are inserted into random signals at random times with variable fault signatures. Next, the local system exchanges the locally seeded dataset with a remote system, and in return receives a remotely seeded dataset, which was produced by the remote system by inserting different random faults into the same dataset. Next, the local system uses a local prognostic-surveillance system to analyze the remotely seeded dataset to produce locally detected faults. Finally, the local system determines a performance of the local prognostic-surveillance system by comparing the locally detected faults against actual faults in the remotely seeded fault information. The remote system similarly determines a performance of a remote prognostic-surveillance system.
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