One-Class Learning-Based Contrastive Reconstruction Framework for the Anomaly Detection of Reciprocating Machinery

Anomaly detection task is an open-set challenge, aiming to identify unseen faulty signals using only healthy signals for training. While data reconstruction frameworks are inherently suited for this task, they often struggle with complex signals due to limited feature extraction capabilities. Contrastive learning offers powerful representation learning, but faces challenges in one-class scenarios and requires effective augmentation techniques. To address these limitations, a novel fault detector is proposed, integrating a redesigned one-class contrastive loss into a data reconstruction framework to endow clustering capability. Learnable feature augmentation is incorporated to ensure effective and generalizable contrastive learning while reducing computational costs by performing augmentation in the feature space. A dilated inception network structure is employed to capture long-distance dependencies in complex input signals. A one-class similarity distance-based threshold is introduced to filter outliers in the healthy signal distribution, and an optimal model selection strategy is proposed based on the minimal threshold during training. The approach is evaluated using two case studies: single-fault and multifault scenarios in a reciprocating compressor. Our method achieves balanced accuracies of 98.44% and 97.81%, respectively, outperforming other methods. These results confirm our detector's capacity to balance false alarms and missed detections effectively, even in challenging multifault conditions.