SPT-AD: Self-Supervised Pyramidal Transformer Network-Based Anomaly Detection of Time Series Vibration Data

Bearing fault diagnosis is a key factor in maintaining the stability and performance of mechanical systems, necessitating reliable methods for anomaly detection and prediction. Unlike traditional conservative maintenance approaches, the importance of predictive maintenance where real-time condition monitoring enables proactive preventive measures has been growing steadily. In this study, we propose a deep learning method that effectively discriminates between normal and abnormal bearing conditions, while predicting potential faults in advance. To achieve this, we develop a time series anomaly detection model based on a supervised learning transformer architecture. Our proposed model tackles the data imbalance issue by generating four types of synthetic anomalies from normal vibration data and incorporates a pyramid-structured attention module to reduce computational costs and enhance the handling of long-term dependencies. Experimental results on real bearing vibration datasets demonstrate improved F1-scores over 6%p compared to existing models and demonstrate a significant reduction in computational costs in specific experimental environments. By reliably identifying and predicting bearing faults at an early stage, this research contributes to reducing maintenance costs and improving system stability. Furthermore, it is expected to have wide applicability for state monitoring and anomaly detection in various rotating machinery systems.