公开(公告)号：US20210166499A1

公开(公告)日：2021-06-03

申请号：US16699023

申请日：2019-11-28

Applicant: Oracle International Corporation

Inventor： Amit VAID ,  Neha TOMAR ,  Utkarsh Milind DESAI ,  Vijayalakshmi KRISHNAMURTHY ,  Goldee UDANI

IPC: G07C5/00 ,  G05B23/02 ,  G06F17/11 ,  G06N20/00

Abstract: Embodiments determine an optimized maintenance schedule for a maintenance program that includes multiple levels, each level including at least one asset (i.e., asset type) and at least one of the levels including a plurality of assets. Embodiments receive historical failure data for each of the assets, the historical failure data generated at least in part by a sensor network. For each asset, embodiments generate a probability density function (“PDF”) using kernel density estimation (“KDE”). For each asset, based on a reliability rate threshold, embodiments determine a cumulative density function (“CDF”) using the PDF. For each asset, embodiments determine an optimized time to failure (“TTF”) using the CDF. Embodiments then create the schedule for each level that includes a minimum TTF for the assets at each level.

公开(公告)号：US20210095996A1

公开(公告)日：2021-04-01

申请号：US16587334

申请日：2019-09-30

Applicant: Oracle International Corporation

Inventor： Karthik GVD ,  Utkarsh Milind DESAI ,  Vijayalakshmi KRISHNAMURTHY ,  Goldee UDANI

IPC: G01D1/00 ,  G01D18/00

Abstract: Embodiments determine anomalies in sensor data generated by a sensor. Embodiments receive a first time window of clean sensor data generated by the sensor, the clean sensor data including anomaly free data, and determine if the clean sensor data includes a cyclic pattern. When the clean sensor data has a cyclic pattern, embodiments divide the first time window into a plurality of segments of equal length, where each equal length includes the cyclic pattern. Embodiments convert the first time window and each of the plurality of segments into corresponding curves using Kernel Density Estimation (“KDE”). For each of the plurality of segments, embodiments determine a Kullback-Leibler (“KL”) divergence value between corresponding curves of the segment and the first time window to generate a plurality of KL divergence values.

公开(公告)号：US20210097416A1

公开(公告)日：2021-04-01

申请号：US16585764

申请日：2019-09-27

Applicant: Oracle International Corporation

Inventor： Karthik GVD ,  Utkarsh Milind DESAI ,  Vijayalakshmi Krishnamurthy

IPC: G06N7/00 ,  G06K9/62 ,  G01D21/00 ,  G06F17/13 ,  H04W84/18

Abstract: Embodiments determine anomalies in sensor data generated by a sensor by receiving an evaluation time window of clean sensor data generated by the sensor. Embodiments receive a threshold value for determining anomalies. When the clean sensor data has a cyclic pattern, embodiments divide the evaluation time window into a plurality of segments of equal length, wherein each equal length comprises the cyclic pattern. When the clean sensor data does not have the cyclic pattern, embodiments divide the evaluation time window into a pre-defined number of plurality of segments of equal length. Embodiments convert the evaluation time window and each of the plurality of segments into corresponding curves using Kernel Density Estimation (“KDE”). For each of the plurality of segments, embodiments determine a Kullback-Leibler (“KL”) divergence value between corresponding curves of the segment and the evaluation time window to generate a plurality of KL divergence values.

公开(公告)号：US20200242511A1

公开(公告)日：2020-07-30

申请号：US16458924

申请日：2019-07-01

Applicant: Oracle International Corporation

Inventor： Someshwar Maroti KALE ,  Vijayalakshmi KRISHNAMURTHY ,  Utkarsh Milind DESAI

IPC: G06N20/00

Abstract: Embodiments implement a machine learning prediction model with dynamic data selection. A number of data predictions generated by a trained machine learning model can be accessed, where the data predictions include corresponding observed data. An accuracy for the machine learning model can be calculated based on the accessed number of data predictions and the corresponding observed data. The accessing and calculating can be iterated using a variable number of data predictions, where the variable number of data predictions is adjusted based on an action taken during a previous iteration, and, when the calculated accuracy fails to meet an accuracy criteria during a given iteration, a training for the machine learning model can be triggered.