## 1 Introduction  
In the steel industry, which is representative of process manufacturing, 10% to 20% of production costs are spent on equipment maintenance. Operation of key machines efficiently and economically is an important issue to gain an edge in quality and cost competitiveness, which are directly related to corporate survival [1, 2]. If an unplanned plant shutdown occurs in steel production equipment, not only will there be significant economic loss, but it will also have a negative impact on product quality, so it is important to detect equipment abnormalities quickly and handle the problem proactively before catastrophic breakdowns occur. It is known that maintenance in response to unplanned failures is three to nine times the cost of predictive maintenance. According to statistics from the United States Department of Commerce (2003), which investigated industrial economic costs due to unplanned production interruptions, the annual cost of equipment maintenance in US industry amounted to $4.9 million as of 2003. The Federal Energy Management Program (FEMP) reported that 8% to 12% of costs can be saved through predictive maintenance compared to preventive maintenance. The effects of predictive maintenance can be expected to reduce equipment maintenance costs, prevent unplanned plant downtime, and improve productivity and quality. As a result of analyzing failures that occurred at one steel company, 65% of them were considered preventable through inspection and monitoring, and only 35% were found to require break-down maintenance. A gearbox is an important mechanical equipment that transmits power between shafts. Among single-equipment failures, gearbox-related failures were found to be the highest at 38% in one steel company. As the types of steel produced become more diverse, machines operate at higher loads than that for which they were designed. A sudden failure of a gearbox can cause quality defects, expensive repairs, and even accidents. The health of a gearbox can be confirmed by measuring machine vibrations, acoustics, heat, and iron content in lubricants. Among these, vibration signals are the most widely used because they contain much information from inside mechanical equipment [3]. Vibration signals measured in all mechanical devices have a specific spectrum when operating normally, but the spectrum changes when failure occurs, so vibration signals are the most effective way to monitor and diagnose the health of rotating mechanical equipment. However, one problem is that extracting features from vibration signals related to gearbox or bearing failures requires extensive domain expertise and prior empirical knowledge. Recently, the rapid development of artificial intelligence (AI) is accelerating developments in the field of intelligent fault diagnosis, along with research on effective and highly accurate fault detection methods that do not rely on feature extraction [4; 5]. Research has continued in the field of anomaly detection, but supervised learning algorithms, such as classification, regression, and object identification, have been proposed more frequently than unsupervised learning algorithms. Additionally, supervised learning provides various evaluation indicators such as accuracy, precision, and recall. Field vibration data are collected over time without a label indicating whether it is normal or abnormal. As a result, unsupervised learning methods are required due to limitations. In the case of steel manufacturing equipment, many data are collected in real time from vibration and temperature sensors, but since there are many customary events, such as overhauls, minor repairs, and oil replacement, it is difficult to define in advance a label that indicates whether the equipment state is normal or not. Therefore, time series data are collected without prior information on the occurrence of failures, which cannot be used in a classification model to detect machine abnormalities [6; 7].