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.