# Transmission Condition Monitoring of 3D Printers Based on the Echo State Network
## 3 Experiments  
The experiment was carried out to validate the effectiveness of the monitoring method for 3D printers. The experimental configuration included a laptop (Inspiron N4110, DELL, Round Rock, TX, USA), a delta 3D printer (SLD-BL600-6, SHILEIDI, Dongguan, China), and a low-cost attitude sensor (BWT901, WIT, Shenzhen, China), as shown in Figure 3. The price of the employed attitude sensor in this work was approximately $20. The attitude sensor resolutions of acceleration and angular velocity were less than \(5.98\times 10^{-5}\) m/sec\({}^{2}\) and \(7.6\times 10^{-3}\) deg/sec, respectively. The delta 3D printer based on the prototype principle of fused deposition modeling (FDM) with a symmetrical parallel transmission mechanism was employed with the experiments in this paper. Compared to the series transmission mechanism, the delta 3D printer with a parallel transmission mechanism shows such advantages such as a compact structure, good stiffness and, especially, printing precision. However, printing quality will decrease with the wear and tear of printer parts including joint bearings and synchronous belts in the transmission chain of a 3D printer. Accordingly, an attitude sensor was installed at the end of the delta 3D printer kinematic chain to reflect the printing condition. The low-cost attitude sensor based on a micro electro mechanical system (MEMS) can be made extremely light and compact. Therefore, the printing quality of the 3D printer after installing the attitude sensor will not be affected. The output of the attitude sensor consists of nine channels including three-axial angular velocity signals, three-axial acceleration signals, and three-axial magnetic field intensity signals. There were three major steps in the experiments, as depicted in Figure 4. The first step was to set-up the printing program (i.e., G-code). After building a cylindrical shell model with a radius of 75 mm in SolidWorks (a 3D modeling software), the 3D model of the cylindrical shell was sliced layer by layer via Repetier-Host (a slicing software) to generate G-code. The second step was to collect monitoring data. Due to the parts that are prone to failure in the transmission chain of a delta 3D printer, which include 12 joint bearings and three synchronous belts, the transmission condition of  
Figure 3: Experimental configuration: (**a**) synchronous belt; (**b**) joint bearing; (**c**) overview. the delta 3D printer was divided into 16 classes, as listed in Table 1. The degree of fault as defined in this work was moderately faulty. When the delta 3D printer executes a printing program under different conditions, the raw monitoring data can be collected with the attitude sensor connected to a laptop. In this work, the frequency of data acquisition by the attitude sensor is set to 100 Hz. Finally, feature extraction for raw monitoring data was performed, including 10 time-domain statistical features and 8 time-frequency features, as introduced in Section 2.1. After the ESN model is built using the aforementioned features, the accuracy of the ESN model can then be obtained through testing. In this work, nine channels of the attitude sensor including three-axial angular velocity signals, three-axial acceleration signals, and three-axial magnetic field intensity signals were employed for sample generation. In the experiment, 60 sets of circular data were collected for each condition and