Memory-augmented skip-connected autoencoder for unsupervised anomaly detection of rocket engines with multi-source fusion

To ensure the safety and stability of the rocket, it is essential to implement accurate anomaly detection on key parts such as the liquid rocket engine (LRE). However, due to the indistinct features of signals under the interference of extreme conditions and the weak distinguishing ability to exist unsupervised methods, it is difficult to distinguish abnormal samples from normal samples, which leads to the failure of reliable anomaly detection. Aiming at this problem, this paper proposed an unsupervised learning algorithm named Memory-augmented skip-connected deep autoencoder (Mem-SkipAE) for anomaly detection of rocket engines with multi-source data fusion. Unlike traditional autoencoders, the input embedding for the decoder is not generated by an encoder but by a combination of memory items that record prototypical patterns of normal samples. Besides, each layer of the encoder and decoder has a skip connection to fully extract the multi-scale features of the normal sample in multi-dimensional space and suppress over-fitting caused by the memory-augmented network. Compared with existing methods and ablation control groups, experiments on four test sets prove the excellent generalization and satisfactory performances of the proposed Mem-SkipAE. Furthermore, the comparison of the single-source model and multi-source model verifies the effectiveness of multi-source fusion.