General Electric Company

Abhinav Saxena

Achalesh Kumar Pandey

Shaddy Abado

Jianbo Yang

Charmin Patel

Fuxiao Xin

Xiaohui Hu

Xiahui Hu

Raw data is received from an industrial machine. The industrial machine includes one or more sensors that obtain the data, and the sensors transmit the raw data to a central processing center. The raw data is received at the central processing center and an unsupervised kernel-based algorithm is recursively applied to the raw data. The application of the unsupervised kernel-based algorithm is effective to learn characteristics of the raw data and to determine from the raw data a class of acceptable data. The class of acceptable data is data having a degree of confidence above a predetermined level that the data was obtained during a healthy operation of the machine. The acceptable data is successively determined and refined upon each application of the unsupervised kernel-based algorithm. The unsupervised kernel-based algorithm is executed until a condition is met.

Apparatus and method for screening data for kernel regression model building

According to some embodiments, system and methods are provided, comprising receiving, at a machine health module, non-parametric data associated with operation of an installed product; generating, via the machine health module, a health status for at least one of a failure type and a remaining useful life of the installed product, based on the received non-parametric data; and generating an operating response of the installed product based on the generated health status. Numerous other aspects are provided.

Machine health monitoring, failure detection and prediction using non-parametric data

A system and method for associating equipment sensor tags with tags associated with analytic programs including embodiments that receive a list of equipment tag names and corresponding descriptions as well as a list of analytic tag names and descriptions. The equipment tag descriptions are normalized to create normalized equipment tag descriptions, and the analytic tag descriptions are normalized to create normalized analytic tag descriptions. A first matrix of vectors that associates content in a dictionary with aspects of the equipment tag names is created using corresponding normalized tag descriptions. A second matrix of vectors that associates the content in a dictionary with aspects of the normalized analytic tag description is created.

Apparatus and method for tag mapping with industrial machines

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San Ramon, CA, United States of America

Abhinav Saxena