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Sensors 2018, 18(4), 1298; https://doi.org/10.3390/s18041298

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

1
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
2
School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China
*
Author to whom correspondence should be addressed.
Received: 15 March 2018 / Revised: 19 April 2018 / Accepted: 19 April 2018 / Published: 23 April 2018
(This article belongs to the Special Issue Sensors for Fault Detection)
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Abstract

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers. View Full-Text
Keywords: delta 3D printer; fault diagnosis; attitude sensor; support vector machine; condition monitoring delta 3D printer; fault diagnosis; attitude sensor; support vector machine; condition monitoring
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He, K.; Yang, Z.; Bai, Y.; Long, J.; Li, C. Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines. Sensors 2018, 18, 1298.

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