## 5 Conclusions  
In this paper, we proposed SPT-AD, a self-supervised learning model for bearing fault diagnosis, designed to overcome key challenges such as data imbalance and dependence on labeled anomalies in industrial applications. The model utilizes only normal data for training while employing data degradation techniques to generate structural synthetic outliers, allowing it to effectively learn representations of anomalous patterns. This approach is particularly valuable in industrial and manufacturing environments, where labeled fault data are difficult to obtain. Furthermore, data degradation techniques help reduce the need for extensive preprocessing, lowering the barrier to real-world deployment.  
We enhanced the AnomalyBERT model by integrating the Pyramidal Attention Mechanism and the Coarser-Scale Construction Module, addressing limitations in existing anomaly detection models. The Pyramidal Attention Mechanism enables the model to efficiently learn multi-resolution dependencies, improving the detection of both short-term and long-term anomalies in time series data. Meanwhile, the Coarser-Scale Construction Module reduces computational costs through structural simplification while minimizing information loss, enhancing both efficiency and performance.  
Experimental results demonstrate that SPT-AD outperformed existing anomaly detection models, achieving a 6%p improvement in F1-score while significantly reducing computational overhead. However, since the experiments were conducted on a single dataset using only normal data for training, further research is needed to validate the model's robustness across diverse industrial environments and varying operational conditions. The model's reliance on synthetic outliers also presents challenges in detecting complex and atypical fault patterns that may arise in real-world applications.  
The data degradation technique used in this study should also be able to reflect the complex and atypical anomaly patterns that may occur in real-world sites, and we plan to validate and extend the performance of the model utilizing complex time series datacollected from various industrial and manufacturing sites rather than data from limited sets and experimental environments.  
Conceptualization, S.G.; methodology, S.G.; software, S.G.; validation, S.G.; formal analysis, S.G.; investigation, S.G. and T.K.; resources, S.G. and T.K.; data curation, S.G.; writing--original draft preparation, S.G. and T.K.; writing--review and editing, S.G.; visualization, S.G. and T.K.; supervision, J.J.; project administration, J.J. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
This research was supported by the SungKyunkwan University and the BK21 FOUR(Graduate School Innovation) funded by the Ministry of Education(MOE, Korea) and National Research Foundation of Korea(NRF), This work was supported by the Institute of Information & Communications Technology Planning & Evaluation(IITP)-ICT Creative Consilience Program grant funded by the Korea government(MSIT)(IITP-2025-RS-2020-II201821).  
The authors have no conflicts of interest to declare.