Fault diagnosis is important for the working conditions of 3D printers, because the failure of 3D printers will have a great impact on the quality of printed products and result in unqualified printing. In this paper, the reservoir computing (RC) method and the data collected by the attitude sensor are analyzed to obtain the health status of a 3D printer. Considering the economics and viability of fault diagnosis, a low-cost attitude sensor is installed on the moving platform of the 3D printer to collect tri-axial angular velocity, tri-axial acceleration, and tri-axial magnetic field strength signals. Then, the collected data is divided into training data and test data. The training data is used to establish the optimization parameter of the RC model to improve its performance, and the test data is used to identify the failure patterns using the model. Finally, compared with the SAE and SVM intelligent diagnosis techniques, the RC method achieves the best fault recognition accuracy, which further proves its superiority.
|Number of pages
|International Journal of Performability Engineering
|Published - Dec 2019
Bibliographical noteFunding Information:
This work is supported in part by the National Natural Science Foundation of China (No. 51775112, 51605406), the Natural Sciences Foundation of Guangdong in China (No. 2017A030313690), and the Research Start-up Funds of DGUT (No. GC300501-12, GC300501-26).
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- 3D printer
- Fault diagnosis
- Pattern recognition
- Reservoir computing