Patent ID: 11934755
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer-implemented method comprising:
creating, by at least one processor, a digital twin model representing a physical asset comprising one or more components and a lubricant;
capturing, by the at least one processor, a data feed from at least one data collection device connected to the physical asset, wherein the data feed describes operation conditions of the physical asset, including a state of the lubricant, wherein a data collection device of the at least one data collection device is a thermal imaging camera, the data feed includes one or more thermal images of the physical asset, and the one or more thermal images depict an increasing amount of heat present at an interface between two of the one or more components;
determining, by the at least one processor, contributing factors to one or more differences between predicted performance of the one or more components of the digital twin model and performance of the one or more components represented in the data feed, by applying machine learning to data extracted from the data feed;
updating, by the at least one processor, the digital twin model based on the determined contributing factors to reflect the performance of the one or more components of the physical asset and further updating a knowledge corpus with the data;
simulating, by the at least one processor, using the updated digital twin model, a plurality of temporal sequences based on the knowledge corpus to predict one or more actions and resulting effects of the one or more actions on a breakdown of the lubricant over a time period, wherein simulating comprises predicting the increasing amount of the heat present at the interface between the two of the one or more components as a function of the one or more thermal images and the one or more actions predicted to maximize an amount of time before the physical asset experiences the breakdown of the lubricant;
identifying, by the at least one processor, a temporal sequence from the plurality of temporal sequences comprising the one or more actions predicted to maximize the amount of time before the physical asset experiences the breakdown of the lubricant;
outputting, by the at least one processor, a recommendation comprising the one or more actions predicted to maximize the amount of time before the physical asset experiences the breakdown of the lubricant; and
applying, by the at least one processor, the one or more actions predicted to maximize the amount of time before the physical asset experiences the breakdown of the lubricant to the digital twin model.