Health Assessment of Landing Gear Retraction/Extension Hydraulic System Based on Improved Risk Coefficient and FCE Model

Abstract

The health of the landing gear retraction/extension(R/E) hydraulic system may be assessed using fuzzy comprehensive evaluation (FCE), however the traditional FCE method depends solely on human assessment by specialists, which is excessively subjective. To address the issue of excessive human subjective variables in the assessment, an improved FCE model based on enhanced risk coefficient is provided, which includes four consideration indexes: failure probability, failure severity, failure detection difficulty, and failure repair difficulty. To reduce subjective human judgment errors entirely due to expert experience, the improved FCE takes into account the likelihood of failure using a statistical method, the severity of failure using a fault simulation analysis based on the LMS Imagine.Lab AMESim simulation platform, and the difficulty of fault detection and repair using the aircraft manufacturer’s professional maintenance information. As part of the evaluation model, the range of health assessment values and accompanying treatment methods are included, making it easier to implement on a daily basis in aircraft maintenance. As a final step, the simulation is evaluated, and the simulated faults are calculated.

Fault Detection of Landing Gear Retraction/Extension Hydraulic System Based on Bond Graph-Linear Fractional Transformation Technique and Interval Analytic Redundancy Relations

“…According to the literature [32,33], the basic framework of the landing gear retraction/extension (R/E) hydraulic system can be constructed, as shown in Figure 2. It is basically the same system architecture diagram as the previously published article [31], but with slight differences. Hydraulic power is provided by both electric motor-driven pumps and engine-driven pumps, along with other accessories.…”

“…The previous literature [31] mainly focuses on evaluating the system's health after fault detection. This paper is an application-oriented innovation that focuses on detecting faults accurately, so that the process of fault detection can be improved.…”

Health assessment of an electro-hydraulic servo pump control system for servomotor based on LGA deep neural network

“…Macaluso [ 14 ] proposed a PHM health assessment system based on critical factor analysis and detection (FMECA), which enabled accurate assessment of system status with fewer sensors. Duan [ 15 ] presented a fuzzy comprehensive evaluation (FCE) health assessment method based on enhanced risk coefficient optimization, which reduced subjective judgment errors caused by expert experience and improved the accuracy of health assessment. Mei [ 16 ] proposed a method that combines the cloud models with the Dempster Shafer evidence theory and achieved health status assessment of complex hydraulic systems.…”

Failture and Maintenance of Aircraft Landing Gear

“…1) of the aircraft is related to the smooth take-off and landing of the aircraft. Therefore, the technical staff should pay special attention to the maintenance of the aircraft landing gear in order to ensure the safety of the aircraft to a greater extent [1][2][3].…”