The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 27, 2018

Filed:

Sep. 26, 2014
Applicant:

Palo Alto Research Center Incorporated, Palo Alto, CA (US);

Inventors:

Linxia Liao, Mountain View, CA (US);

Rajinderjeet Singh Minhas, Mountain View, CA (US);

Arvind Rangarajan, Santa Clara, CA (US);

Tolga Kurtoglu, San Jose, CA (US);

Johan de Kleer, Los Altos, CA (US);

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G01M 13/00 (2006.01); B23Q 17/09 (2006.01); G05B 19/4065 (2006.01); G05B 23/02 (2006.01);
U.S. Cl.
CPC ...
G01M 13/00 (2013.01); B23Q 17/0995 (2013.01); G05B 19/4065 (2013.01); G05B 23/0283 (2013.01); G05B 2219/34477 (2013.01); G05B 2219/37252 (2013.01); G05B 2219/37258 (2013.01); G05B 2219/50185 (2013.01);
Abstract

A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.


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