Closed-Loop Fault Diagnosis of SDR Using Gap Metric, PCA, and Kalman’s Principle

• Published in: International journal of aerospace engineering 2023, Vol.2023, p.1-15
• Main Authors: Wang, Hongfu, Zeng, Qinghua, Wang, An, Zhang, Zongyu, Liu, Weide
• Format: Article
• Language: English
• Subjects: Accuracy; Aerospace engineering; Aircraft; Algorithms; Classification; Closed loops; Consistency; Deep learning; Fault detection; Fault diagnosis; Feedback; Gas flow; Gas generators; Knowledge; Machine learning; Methods; Neural networks; Pressure sensors; Principal components analysis; Principles; Redundancy; Sensors; Support vector machines
• ISSN: 1687-5966; 1687-5974
• DOI: 10.1155/2023/9994093

The measurement of the pressure in the gas generator (GG) plays a decisive role in the closed-loop regulation of the gas flow in a solid ducted rocket ramjet (SDR). Therefore, the fault detection and isolation (FDI) of the pressure sensors is particularly significant. Especially in the GG with double pressure feedback, there are issues such as less available data and sensor fault transmission, which make the FDI more difficult. In this paper, for the GG with double pressure feedback, firstly, the “Consistency Index” was constructed based on the principal component analysis (PCA) algorithm, which made it easier to evaluate the consistency of the measured values. Secondly, based on Kalman’s principle, the redundancy information of the system was used to estimate the pressure in the GG. Finally, the gap metric between the measured pressure and the estimated pressure was employed to characterize the health of the sensors. Compared with the MWPCA algorithm, it was shown that our proposed algorithm was more accurate in fault diagnosis and could avoid the problem of missing alarm when two sensors had consistent faults, which would provide strong support for the safe operation of the SDR and could further promote its application in long-endurance aircrafts.