Partial sensing information-driven threshold cyclic update graph autoencoder for mechanical anomaly detection

Graph neural networks (GNNs) are prominent in multi-sensor fusion for mechanical anomaly detection, but their reliance on the availability of all sensor data may lead to low-quality information in the event of partial sensor failures. Furthermore, these studies mainly rely on fixed thresholds from single operating conditions, limiting adaptability to dynamic scenarios. To address these issues, this paper proposes a new mechanical anomaly detection method called threshold cyclic update graph autoencoder (TCUGAE), driven by partial sensing information. First, a subset of sensor data is selected based on a fused correlation metric, combining linear, nonlinear, and frequency-domain similarities, and the partial sensing information graph (PSG) is constructed to avoid interference from low-quality data. Subsequently, the partial sensing information graph autoencoder (PSGAE) is constructed to optimize a combined loss function, incorporating both reconstruction loss and latent space regularization, for identifying potential anomalies. Finally, a threshold cyclic update (TCU) strategy is developed to dynamically adjust weights and guide the model in adaptively determining anomaly thresholds across multiple operating conditions. The method is applied to analyze a multi-condition imbalanced dataset of multiple mechanical components. Comparative results show the proposed method outperforms existing ones across multiple metrics, demonstrating its robustness and adaptability.