Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

The fuel system, which aims to provide sufficient the engine maintain thrust and power, is one of most critical systems in aircraft. However, possible degradation modes, such as leakage blockage, can lead component failure, affect performance, even cause serious accidents. As an advanced maintenance strategy, Condition Based Maintenance (CBM) effective coverage, by combining state-of-the-art sensors with data acquisition analysis techniques guide before asset’s becomes serious. Artificial Intelligence (AI), particularly machine learning (ML), has proved supporting CBM, for analyzing generating predictions regarding health condition, thus influencing plans. from engineering perspective, output ML algorithms, usually form data-driven neural networks, come into question practice, it be non-intuitive lacks ability unambiguous signals maintainers, making difficult trust. Engineers are interested a deterministic decision-making process how being revealed; algorithms should able certify convince engineers approve recommended actions. Explainable AI (XAI) emerged potential solution, providing some logic on derived input given, may help users understand diagnostic result algorithm. In order inspire advise scientists who about develop use approaches systems, this paper explores literature experiment, simulation, AI-based diagnostics system make informed statement progress that been made intelligent fault emphasizing necessity giving well highlighting areas future research.