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:
May. 11, 2021

Filed:

Feb. 12, 2018
Applicant:

Dalian University of Technology, Dalian, CN;

Inventors:

Tinghua Yi, Dalian, CN;

Haibin Huang, Dalian, CN;

Hongnan Li, Dalian, CN;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 17/18 (2006.01); G06K 9/00 (2006.01); G06F 30/20 (2020.01); G01M 5/00 (2006.01);
U.S. Cl.
CPC ...
G06F 17/18 (2013.01); G06F 30/20 (2020.01); G06K 9/00 (2013.01); G01M 5/0008 (2013.01);
Abstract

The present invention belongs to the technical field of health monitoring for civil structures, and a dynamically non-Gaussian anomaly identification method is proposed for structural monitoring data. First, define past and current observation vectors for the monitoring data and pre-whiten them; second, establish a statistical correlation model for the whitened past and current observation vectors to obtain dynamically whitened data; then, divide the dynamically whitened data into two parts, i.e., the system-related and system-unrelated parts, which are further modelled by the independent component analysis; finally, define two statistics and determine their corresponding control limits, respectively, it can be decided that there is anomaly in the monitoring data when each of the statistics exceeds its corresponding control limit. The non-Gaussian and dynamic characteristics of structural monitoring data are simultaneously taken into account, based on that the defined statistics can effectively identify anomalies in the data.


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