Gyroscope in-assembly drift anomaly detection based on decision re-optimized deep auto-encoder

Abstract

Abstract In gyroscope assembly, frequent drift performance overproof causes massive parts re-assembly and writing off, for which in-assembly gyro drift anomaly detection is required. However, when utilizing common anomaly detection methods, imbalanced assembly data distribution causes severe accuracy reduction and false alarms. To tackle these problems, first, we propose a decision re-optimized deep autoencoder (DRDAE) model to conduct the in-assembly drift anomaly detection under imbalanced assembly data distribution. Second, a decision-based training strategy is introduced to lower the false alarm rate in anomaly detection, for which models based on different training strategies are compared for better performance. Third, a modified SMOTE data augmentation method is utilized to settle the impact of data imbalance under small-sample condition. Experimental results show that the proposed method can achieve in-assembly drift anomaly detection under imbalanced data distribution in high precision and outperforms all other existing methods, lowering the assembly repetition rate and improving assembly efficiency.