A Transformer-Based Variational Autoencoder for Training Data Generation in Spindle Motor Vibration-Based Anomaly Detection

In high-speed spindle motors operating above 10,000 rpm, vibration analysis is essential for detecting mechanical anomalies. However, data scarcity and imbalance, especially for rare fault conditions, limit the performance of deep learning-based anomaly detection models. In this study, we define sample scarcity as the limited availability of real labeled vibration sequences for model training, i.e., only 5000 normal and 5000 faulty samples collected from three spindle motors (10,000 real samples in total). We propose a Transformer-based Variational Autoencoder (T-VAE) to generate realistic triaxial acceleration sequences for spindle motor health monitoring. The model integrates positional encoding and multi-head self-attention to capture long-range temporal dependencies in multivariate time-series data, and applies a KL annealing strategy to improve training stability. Using 5000 normal and 5000 faulty vibration samples collected from three spindle motors, the model generates 100,000 synthetic samples per class, which are used to augment training for a downstream CNN-LSTM classifier. Without augmentation, the classifier achieved 95.73% pass detection on normal samples and 81.40% fail detection on faulty samples. After augmentation with Transformer-VAE, performance increased to 98.07% pass detection for normal data and 97.99% fail detection for faulty data. For prediction, we evaluate on an independent dataset of 25,000 normal and 25,000 faulty sequences obtained from eleven different spindle motors not used in training (cross-spindle). The results demonstrate that the T-VAE effectively alleviates the data scarcity problem and significantly improves anomaly detection accuracy for high-speed spindle motor vibration signals. This approach can be directly applied to predictive maintenance systems in real-world manufacturing environments.