Steady-State Process Fault Detection for Liquid Rocket Engines Based on Convolutional Auto-Encoder and One-Class Support Vector Machine

IEEE Access

Runsheng Hu¹  Yuqiang Cheng¹  Xiaobin Zhu¹  Xing Cui¹  Jianjun Wu¹ 

[1] College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China;

关键词: Convolutional auto-encoder;    one-class support vector machine;    liquid rocket engine;    steady-state process;    fault detection;   

DOI  :  10.1109/ACCESS.2019.2961742

来源: DOAJ

【 摘 要 】

Liquid rocket engines (LREs) are the main propulsive devices of launch vehicles. Due to the complex structures and extreme working environments, LREs are also the components prone to failure. It is of great engineering significance to develop fault detection technologies which can detect fault symptoms in time and provide criteria for further fault diagnosis and control measures to avoid serious consequences during both the ground tests and flight missions. This paper presents a novel fault detection method based on convolutional auto-encoder (CAE) and one-class support vector machine (OCSVM) for the steady-state process of LREs. We train the CAEs by normal ground hot-fire test data of a certain type of large LRE for automatic feature extraction. Then the obtained features are used to train the OCSVMs to accomplish the fault detection task. The results demonstrate that the proposed method outperforms traditional redline system (RS), adaptive threshold algorithm (ATA), and back-propagation neural network (BPNN). We also study the effect of sample sizes and domain knowledge on the performance of the proposed method. The results suggest that appropriate measures that enrich the effective information content in the training data, such as increasing sample size and introducing domain knowledge, can further improve the performance of the proposed fault detection method.

【 授权许可】

Unknown