Make the Rocket Intelligent at IoT Edge: Stepwise GAN for Anomaly Detection of LRE With Multisource Fusion
Yong Feng, Zijun Liu, Jinglong Chen, Haixin Lv, Jun Wang, Junshe Yuan
Abstract
Anomaly detection (AD) for liquid rocket engine (LRE) is essential to improve the reliability and safety of space launch missions. However, it is difficult for existing methods to implement fast and precise detection with single-source information and absent anomalous samples. The Internet of Things (IoT) enables intelligent LRE AD via edge computing using multisource data. This article proposed an edge-based detection algorithm named stepwise generative adversarial network (StepGAN) for AD of LRE with multisource fusion at the Edge of IoT. StepGAN incorporates deep autoencoder and relativistic generative adversarial network. Through multistage stepwise training with unlabeled normal data, an encoder-generator-discriminator network is obtained to identify LRE anomalies at the edge. In addition, by fusing multisource information at feature level and aggregating neighboring information at decision level during real-time detection, the performance of StepGAN is further improved. To demonstrate its ability for IoT edge-based AD, the proposed method is compared with typical methods, using real static ignition data of LRE as cloud and edge data, and achieves the best result under multiple evaluation indexes. To evaluate the proposed fusing approach, a channel-varying case is carried out and extensive experimental results indicate its effectiveness.