Improved fault diagnosis for aircraft flap control system based on bond graph. Issue 8 (7th July 2020)

• Record Type: Journal Article
• Authors: Chen, Jie; Jing, Zhengdong; Wu, Chentao; Chen, Senyao; Cheng, Liye
• Is Part Of: Aircraft engineering and aerospace technology. Volume 92:Issue 8(2020)
• Page Start: 1159
• Page End: 1168
• Publication Date: 2020-07-07
• Subjects: Fault diagnosis -- Adaptive threshold -- Aircraft flap control system -- Bond graph; Aerospace engineering -- Periodicals; Aeronautics -- Systems engineering -- Periodicals; Astronautics -- Systems engineering -- Periodicals; Airplanes -- Equipment and supplies -- Periodicals; Space vehicles -- Equipment and supplies -- Periodicals; Aerospace industries -- Periodicals; 629.1
• DOI: 10.1108/AEAT-01-2020-0005
• Languages: English
• ISSNs: 1748-8842

Abstract

Abstract : Purpose: This paper aims to improve the fault detection adaptive threshold of aircraft flap control system to make the system fault diagnosis more accurate. Design/methodology/approach: According to the complex mechanical–electrical–hydraulic structure and the multiple fault modes of the aircraft flap control system, the advanced fault diagnosis method based on the bond graph (BG) model is presented, and based on the system diagnostic BG model, the parameter uncertainty intervals are estimated and a new adaptive threshold is constructed by linear fraction transformation. Findings: To construct a more reasonable and accurate adaptive threshold range to more accurately detect system failures, some typical failure modes' diagnosis process are selected and completed for verification; the simulation results show that the proposed method is effective and feasible for complex systems' fault diagnosis. Practical implications: This study can provide a theoretical guidance and technical support for fault diagnosis of complex systems, which avoid misdiagnosis and missed diagnosis. Originality/value: This study enables more accurate fault detection and diagnosis of complex systems when considering factors such as parameter uncertainty.