He Kun, Yang Zhijun, Bai Yun, Long Jianyu, Li Chuan
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China.
School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China.
Sensors (Basel). 2018 Apr 23;18(4):1298. doi: 10.3390/s18041298.
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.
健康状况是影响3D打印机打印质量的一个关键因素。在这项工作中，提出了一种姿态监测方法，使用支持向量机（SVM）来诊断三角3D打印机的故障。在打印机的移动平台上安装了一个姿态传感器，以监测其三维姿态角、角速度、振动加速度和磁场强度。在涉及12种故障类型和一种正常状态的不同条件下，收集工作打印机的姿态数据。对收集到的数据进行分析以诊断健康状况。为此，在实验中采用二元分类、一对一与最小二乘支持向量机相结合的方法，利用姿态监测数据的所有通道进行故障诊断建模。为了进行比较，姿态监测数据的每个通道都用于模型训练和测试。另一方面，还应用了反向传播神经网络（BPNN）使用相同的数据来诊断故障。当使用支持向量机建模的姿态监测数据的所有通道时，获得了最佳故障诊断准确率（94.44%）。结果表明，基于支持向量机的姿态监测是三角3D打印机故障诊断的有效方法。
