## 1 Introduction  
Since the 4 th Industrial Revolution, the smartization of the manufacturing industry using information and knowledge has been rapidly growing. Computer numerical control (CNC) [1], machining center tool (MCT), and injection molding machines are typical production equipment applied to smart factories. Various sensors are installed and operated in the factory for detecting defects and maintaining the production facility. It is possible to build a monitoring system using data gathered from sensors. Sensors are used for predictive preservation of production machines, optimization of manufacturing processes, and real-time abnormality detection using applying data analysis [2], machine learning [3; 4; 5; 6] and deep learning [7; 8; 9] technology. According to these changes, various research studies have been conducted in academia based on sensor data. Data collected from the sensor data [10; 11; 12; 13; 14] are stacked up as big data and stored in a data center at the manufacturing site or in a cloud environment. Owing to the rapidly growing computing power the analyze of big data has recently been in the spotlight. However, there is a fatal problem with sensor data gathered from manufacturing facilities. The state of big data from most of the manufacturing facilities is more imbalanced than that of normal data. Furthermore, it is not easy to gather abnormal data unless the equipment intentionally fails because the tools are replaced periodically based on the experience of expert workers. To solve the class imbalance of data, research studies have been conducted to detect outliers in data by learning only normal data. However, previous studies have focused on the correlation between the time of occurrence of the abnormality in all data, and the reasonsof the abnormality. To detect abnormalities in CNC machines, the most representative machine tools in the manufacturing industry, have various data such as physical properties information of raw materials, vibration of motors, and discharge of lubricants. These data are applied as elements of abnormal detection [15, 16]. Owing to the nature of the time series, data is continuously gathered and stored by time. Most big data processed for analysis after storage, consumes considerable time and effort to pre-process time series data. Therefore, the detection of abnormalities in the product unit takes precedence before focusing data on abnormalities and factors that affect their occurrence. In this study, class imbalance was resolved and approached as a computing architecture for data storage methods and data classification. This study focused on product-level collection and analysis, data-oriented collection, and analysis. In addition, we aim to detect the problem state, which is abnormality, during processing of each single product, not to classification the reason of processing problem state. The data used in this study utilizes sensor data collected by CNC. Many studies have been conducted on abnormality detection for CNC predictive preservation [17, 18]. However, related studies have only performed abnormality detection for the entire collected data. To apply it to the real field, product-oriented data storage and abnormality detection are required rather than, the entire data. The contributions of the study are as follows: A new architecture using the SSA [19] technique and the unsupervised deep learning model [20] is proposed. It also increases the usability of big data collected from smart factories through new proposals on how to store data. The validity of the storage and analysis methods of CNC data is verified through experiments. It also applies and compares various proposed techniques to improve class imbalance and demonstrates the performance of the proposed classification model. In addition, an experiment was conducted to confirm the effectiveness of the proposed architecture to determine whether the architecture could record valid performance when applied in practice.