# Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering
## 4 Experiment
### Dataset  
The SMD dataset consists of sound data collected by microphones installed on SMD assembly machines. As the SMD machine continuously assembles the product, the raw data represent the continuous assembly sounds of the product. As the raw data were too long for training, the data were segmented based on the assembly section of each individual product during the data collection process. The data collection process is the same as in the previous studies [7; 8; 9]. Figure 2 shows an example of the data collection process. In a previous study [9], we collected 30 normal data samples and 6 abnormal data samples. According to the characteristics of SMD products, which are diversified according to the combination of sensors, products are becoming more diversified in the real field. We collected 15 sets of normal data and 5 sets of abnormal data from 15 SMD products, and the new SMD dataset size is 6.1 GB. Abnormal data were actually collected when there was a problem in the assembly process and classified into two categories based on the degree of human recognition. These two error levels are expressed as _Error 1_ and _Error 2_, and are marked as _product name-1_ and _product name-2_. Since there are many types of products and the names are complicated, the first two letters of the product name, with one letter in alphabetical order, are combined and marked in the Abbr. column in the table. Amt. is the abbreviation for amount; the Amt. column in the table represents the number of samples collected for each respective product's data. The previously collected dataset is described in Table 1 and the newly collected dataset is described in Table 2. The bold text in the table means abnormal. \begin{table}
\begin{tabular}{l l r r r r r} \hline \hline
**Name** & **Abbr.** & **Time (\(\mu\pm\sigma\)s)** & **Amt.** & **Name** & **Abbr.** & **Time (\(\mu\pm\sigma\)s)** & **Amt.** \\ \hline AT2\(-\)AB & ATA & \(40.3\pm 2.1\) & 100 & NA\(-\)9473 & NAB & \(13.1\pm 0\) & 53 \\ AT2\(-\)INS8 & ATB & \(8.3\pm 0\) & 63 & RT\(-\)12 DF & RTA & \(36.6\pm 0.9\) & 29 \\ C\(-\)SEREIS & CSA & \(12.\pm 0.8\) & 41 & ST\(-\)2524 & STA & \(20.3\pm 0.7\) & 46 \\ CLR\(-\)085 & CLA & \(34\pm 3.5\) & 51 & ST\(-\)2624 & STB & \(19.5\pm 0.8\) & 43 \\ CT\(-\)C112B & CTA & \(45.4\pm 3.6\) & 22 & ST\(-\)2744 & STC & \(17.4\pm 0.6\) & 76 \\ CT\(-\)C112T & CTB & \(40.1\pm 0.9\) & 24 & ST\(-\)3424 & STD & \(25.9\pm 3.5\) & 81 \\ CT\(-\)C121B & CTC & \(33.9\pm 7.7\) & 18 & ST\(-\)3214 & STE & \(26.8\pm 1.5\) & 108 \\ CT\(-\)C134B & CTD & \(44.4\pm 14.4\) & 33 & ST\(-\)**3214 -1** & **STE\(-\)1** & **27 \(\pm\) 1.2** & **100** \\ CT\(-\)C134T & CTE & \(34.5\pm 2.9\) & 44 & ST\(-\)**3214 -2** & **STE\(-\)2** & **27 \(\pm\) 0.9** & **100** \\ GT\(-\)3214 & GTA & \(28.3\pm 2.6\) & 33 & ST\(-\)3428 & STF & \(30.3\pm 2.7\) & 89 \\ GT\(-\)5112 & GTB & \(25.6\pm 1.7\) & 51 & ST\(-\)3624 & STG & \(28.5\pm 3.6\) & 77 \\ GW\(-\)9 XX & GWA & \(20.8\pm 2.1\) & 93 & ST\(-\)3704 & STH & \(27.9\pm 1.5\) & 64 \\ GT\(-\)4118 & GTC & \(35.9\pm 4.1\) & 43 & ST\(-\)3708 & STI & \(40\pm 2.9\) & 64 \\
**GT\(-\)4118\(-\)1** & **GTC\(-\)1** & **33.9\(\pm\) 3.3** & **14** & **ST\(-\)3708 -1** & **STI\(-\)1** & **31 \(\pm\) 2.1** & **35** \\
**GT\(-\)4118\(-\)2** & **GTC\(-\)2** & **28.4\(\pm\) 2.7** & **100** & **ST\(-\)3708 -2** & **STI\(-\)2** & **36 \(\pm\) 7.4** & **82** \\ HV\(-\)M1230C & HVA & \(49.8\pm 2.6\) & 39 & ST\(-\)3804 & STJ & \(25.2\pm 1.3\) & 83 \\ MG\(-\)A121H & MGA & \(30.1\pm 2\) & 68 & ST\(-\)7111 & STK & \(8.7\pm 0.9\) & 30 \\ NA\(-\)9289 & NAA & \(10.5\pm 0.1\) & 55 & TSIO\(-\)2002 & TSA & \(19.8\pm 0.7\) & 65 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Large SMD dataset consisting of 30 normal products and 6 abnormal products. Bold text refers to abnormal products. Abnormal products were excluded from training.