## 1 Introduction  
The reliable and sustainable operation of machines plays a crucial role in ensuring productivity, quality, and safety. Rotating components are fundamental to industrial machinery, and their degradation can result in mechanical failures, production downtime, and, in severe cases, catastrophic accidents. Consequently, there is a growing demand for early-stage condition monitoring technologies to detect potential issues and prevent failures before they occur [1, 2]. Traditionally, the fault diagnosis of rotating bodies relied primarily on physical inspection and simple sensor-based monitoring methods [3]. However, these approaches are time-consuming and labor-intensive and have many limitations, especially when it comes to detecting defects in early stages [4, 5]. These limitations are becoming more pronounced in the context of increasingly complex manufacturing processes and sophisticated facility operations [6]. Recent advances in artificial intelligence have revolutionized many fields. There are various applications such as anomaly detection, image classification, speech recognition, natural language processing, and autonomous driving, and efforts to utilize these technologies in manufacturing industries are increasing [7]. In particular, research on the predictive maintenance of rotating machinery such as motors, Computer Numerical Control Grinding Machines (CNC Grinding Machines), turbines, and generators, which are all bearing equipment common in machinery, is attracting attention as a key technology for realizing smart factories [8]. Predictive maintenance is an approach that utilizes facility data to predict and prevent machine or equipment failures in advance and is more efficient and effective than conventional methods centered on regular inspections [9]. However, as the complexity of facilities increase, maintenance becomes time-consuming and costly, and existing methods are limited to reacting to problems only after they occur [10]. To solve these problems, research on predictive maintenance utilizing AI technology is actively being conducted [11]. Time series data analysis is becoming a core technology for predictive maintenance. The large amount of sensor data collected in the manufacturing processes is unstructured and complex, which makes the early detection of failures and anomalies very difficult [12]. Recently, transformer-based models have shown excellent performance in processing such time series data, providing higher accuracy and faster processing speed than traditional models such as LSTM-AE and AnoGAN [13; 14]. This makes them well suited for analyzing large-scale data in real time and detecting anomalous patterns [15]. The ultimate objective of this research is to develop a model that accurately detects anomalous patterns in time series vibration data from bearing systems and predicts potential failures at an early stage. This approach aims to proactively prevent unexpected bearing failures, reduce maintenance costs, and enhance the reliability and efficiency of rotating machinery [16]. In this study, we propose SPT-AD, a transformer-based anomaly detection model designed specifically for bearing fault diagnosis. Furthermore, we introduce an algorithm optimized for the unique characteristics of vibration signals and time series data from rotating machinery. Experimental results demonstrate that SPT-AD outperforms models proposed in recent studies, achieving a 6%p improvement in the F1-score while significantly reducing computational overhead. This paper is organized as follows. Section 1 describes the background, objectives, and contributions of this research. Section 2 describes bearing signal processing, existing signal processing techniques, research related to predictive maintenance and anomaly detection, and recent research trends. Section 3 describes the structure, learning method, and key differences in the proposed SPT-AD.