Segment-wise Anomaly Detection via Compression Tokens in Industrial Production Lines

We present a predictive maintenance approach for industrial production lines based on multivariate segment-wise time-series analysis. To address the high cost of collecting anomalous samples, we propose a novelty detection framework in which a transformer autoencoder is trained in a semi-supervised fashion exclusively on nominal sequences, and anomaly scores are derived from reconstruction error at test time. We introduce a set of learnable “compression tokens” into the transformer encoder; these tokens serve as the bottleneck from which the decoder reconstructs the input. We compare this model against an MLP-based autoencoder baseline; the results show that the novelty-detection model remains strong, with near-perfect performance under time-aware and device-aware validation, which are the conditions that most faithfully simulate deployment.