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 in the landing gear retraction/extension hydraulic system is difficult due to uncertainties in component parameters and sensor measurement values. This work lies in the introduction of linear fractional transformation technology and uncertainty analysis theory for the construction of the diagnostic bond graph of the landing gear retraction/extension hydraulic system. Thus, interval analytical redundancy relations can be derived as well as fault signature matrices. By using the fault signature matrix, existing faults can be detected and isolated preliminary. Furthermore, interval analytical redundancy relations can be used to detect system faults in detail. The analysis results of the failure cases of the internal and external leakage of the actuator and landing gear selector valve reversing stuck show that compared to the traditional analytical redundancy relations, this method takes into account the negative factors of uncertainty, so it can effectively reduce missed diagnosis and misdiagnosis; compared to the traditional absolute diagnostic threshold, the interval diagnostic threshold is more accurate and sensitive.
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