基于故障逻辑的民机液压状态监控与故障诊断

doi:10.12305/j.issn.1001-506X.2025.03.16

Abstract

Abstract:

Current monitoring data from civil aircraft are difficult to effectively apply to condition monitoring and fault diagnosis, which limits improvements in safety and reliability. To address this, this paper proposes a decision tree model based on hydraulic system component design and monitoring data to achieve operational condition monitoring of the hydraulic system. Additionally, a transfer learning model based on fault logic and operational data is proposed for fault diagnosis and localization, aiming to enhance condition monitoring capabilities and fault diagnosis efficiency. First, the principles of the hydraulic system are analyzed, and operational monitoring indicators are established based on rated parameters from the Flight Crew Operating Manual (FCOM) and monitoring data, with a decision tree model employed to monitor the hydraulic system's operational condition. Subsequently, fault formation conditions are organized into a logic diagram, and data from the Quick Access Recorder (QAR) are collected in conjunction with the input signal parameters of the logic diagram to develop a transfer learning model for fault diagnosis and localization. Finally, the proposed methods are validated using a case study of a hydraulic low-pressure fault in a specific type of domestically produced civil aircraft. The results demonstrate that the condition monitoring method effectively quantifies the hydraulic system's operational state, while the fault diagnosis method efficiently identifies the causes of faults.

Key words: state monitoring, fault diagnosis and localization, logic diagram, monitoring parameters, transfer learning

CLC Number:

V267

Yunwen FENG, Weihuang PAN, Cheng LU, Jiaqi LIU. Civil aircraft hydraulic state monitoring and fault diagnosis based on fault logic[J]. Systems Engineering and Electronics, 2025, 47(3): 842-854.

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参数	额定运行值	运行范围
P1/psi	3 000	[1 800, 3 300]
T1/℃	30	[-40, 135]
Q1/%	60	[6, 100]

序号	P1/psi	Q1/%	T1/℃
1	3 057	50.9	16.3
2	3 057	50.9	16.3
3	3 058	50.9	16
4	3 058	50.9	16
5	3 061	50.9	16

序号	P1/psi	Q1/%	T1/℃	Cos	OR
1	0.262 5	0.504 2	0.181 5	0.895 6	1
2	0.236 2	0.504 2	0.181 5	0.876 9	1
3	0.210 0	0.504 2	0.181 5	0.856 1	1
4	0.183 7	0.504 2	0.181 5	0.833 0	0
5	0.157 5	0.504 2	0.181 5	0.807 6	0

序号	P1/psi	Q1/%	T1/℃
131	1 908	53.4	28.5
132	1 868	53.4	28.5
133	1 828	53.4	28.5
134	1 788	53.4	28.5
135	1 748	53.4	28.5
136	1 708	53.4	28.5
137	1 668	53.4	28.5
138	1 628	53.4	28.5
139	1 588	53.4	28.5
140	1 548	53.4	28.5

序号	EDP_low	ACMP_low	HYD_P_1	Eng	ACMP_E	ACMP_ON	COUNT_ACMP_ON	Plane_stats	Plane_V	COUNT_Fault_time	classification
1	NOT-LOW	LOW	3 036	57.9	OFF	OFF	0	GND	0	0	NO
2	NOT-LOW	LOW	3 027	52.9	OFF	OFF	0	GND	0	0	NO
3	NOT-LOW	LOW	3 032	48.7	OFF	OFF	0	GND	0	0	NO
4	LOW	LOW	3 061	95.8	OFF	OFF	0	GND	0	0	NO
5	LOW	LOW	3 061	95.9	OFF	OFF	0	GND	0	0	NO
6	LOW	LOW	3 061	95.9	OFF	OFF	0	GND	0	1	Fault1
7	LOW	LOW	3 061	95.9	OFF	OFF	0	GND	0	1	Fault1
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
38 682	LOW	LOW	1 750	0	ON	ON	1	AIR	268.5	1	Fault92
38 725	LOW	LOW	1 750	96	ON	ON	1	AIR	268.5	1	Fault93

Civil aircraft hydraulic state monitoring and fault diagnosis based on fault logic

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