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:
Apr. 18, 2023

Filed:

Jul. 15, 2022
Applicant:

Sichuan University, Chengdu, CN;

Inventors:

Jianbo Wu, Chengdu, CN;

Ziheng Huang, Chengdu, CN;

Zhaoyuan Xu, Chengdu, CN;

Qiao Qiu, Chengdu, CN;

Jun Zheng, Chengdu, CN;

Jinhang Li, Chengdu, CN;

Zhiyuan Shi, Chengdu, CN;

Assignee:

Sichuan University, Chengdu, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01M 99/00 (2011.01); G05B 19/04 (2006.01); G05B 19/418 (2006.01); G05B 23/02 (2006.01); G06F 18/25 (2023.01); G06F 18/214 (2023.01);
U.S. Cl.
CPC ...
G01M 99/005 (2013.01); G05B 19/04 (2013.01); G05B 19/418 (2013.01); G05B 23/0259 (2013.01); G06F 18/2148 (2023.01); G06F 18/253 (2023.01); G06F 2218/04 (2023.01); G06F 2218/10 (2023.01); G06F 2218/12 (2023.01);
Abstract

A method for diagnosing and predicting operation conditions of large-scale equipment based on feature fusion and conversion, including: collecting a vibration signal of each operating condition of the equipment, and establishing an original vibration acceleration data set of the vibration signal; performing noise reduction on the original vibration acceleration data set, and calculating a time domain parameter; performing EMD on a de-noised vibration acceleration and calculating a frequency domain parameter; constructing a training sample data set through the time domain parameter and the frequency domain parameter; establishing a GBDT model, and inputting the training sample data set into the GBDT model; extracting a leaf node number set from a trained GBDT model; performing one-hot encoding on the leaf node number set to obtain a sparse matrix; and inputting the sparse matrix into a factorization machine to obtain a prediction result.


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