Self-Supervised Intelligent Autoencoder with Multi-Scale Convolutional Contrastive Learning for Multivariate Anomaly Detection

Autonomous anomaly detection in multivariate time-series is essential for ensuring safety in industrial and aerospace systems, yet is hindered by high-dimensional signals and limited fault samples. This paper proposes a self-supervised autoencoder that integrates multi-scale convolutions (kernel sizes 3/5/7) with a Transformer encoder augmented by T5-style relative position bias, enabling joint modeling of local transient patterns and long-range temporal dependencies. To enhance representation learning under scarce anomaly labels, we leverage SimCLR-based contrastive pretraining to refine normal-data feature distributions. To address fragmented detections, we design an event-level aggregation strategy that merges window-level reconstruction errors via Top-k peak averaging and rule-based filtering. Evaluated on the SMAP benchmark and our in-house industrial dataset, named CAFUC2, our method achieves event-level F1 of 0.97 and 0.83, respectively outperforming traditional baseline methods and demonstrating robust generalization to real-world flight anomalies.