Abstract: An online monitoring device of 3D printing equipment includes a signal collection module, a signal processing module, a feature extraction module, a monitoring module and a knowledge base module. A vibration signal of a preset component of the 3D printing equipment is collected by a vibration sensor. The collected vibration signal of each preset component is converted from an analog signal to a digital signal and the spectrum characteristics are extracted. Based on the spectrum characteristics of each preset component, the operation state type of the preset component is obtained by a comparative analysis model. The knowledge base module is configured to store newly added samples and initial samples of the 3D printing equipment. The initial samples include spectrum characteristic information and corresponding fault category of known faults, and the newly added samples include spectrum characteristic information and corresponding fault category of new faults.
Type:
Grant
Filed:
July 7, 2020
Date of Patent:
October 11, 2022
Assignees:
INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES, CLOUD COMPUTING CENTER CHINESE ACADEMY OF SCIENCES, DongGuan, Guangdong (CN)
Inventors:
Gang Xiong, Jiawei Liao, Zhen Shen, Xiuqin Shang, Chao Guo, Jun Yan, Can Luo, Xiao Wang, Feiyue Wang
Abstract: An online monitoring device of 3D printing equipment includes a signal collection module, a signal processing module, a feature extraction module, a monitoring module and a knowledge base module. A vibration signal of a preset component of the 3D printing equipment is collected by a vibration sensor. The collected vibration signal of each preset component is converted from an analog signal to a digital signal and the spectrum characteristics are extracted. Based on the spectrum characteristics of each preset component, the operation state type of the preset component is obtained by a comparative analysis model. The knowledge base module is configured to store newly added samples and initial samples of the 3D printing equipment. The initial samples include spectrum characteristic information and corresponding fault category of known faults, and the newly added samples include spectrum characteristic information and corresponding fault category of new faults.
Type:
Application
Filed:
July 7, 2020
Publication date:
January 14, 2021
Applicants:
INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES, CLOUD COMPUTING CENTER CHINESE ACADEMY OF SCIENCES, DongGuan, Guangdong (CN)
Inventors:
Gang XIONG, Jiawei LIAO, Zhen SHEN, Xiuqin SHANG, Chao GUO, Jun YAN, Can LUO, Xiao WANG, Feiyue WANG