# Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System  
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1 Integrated Vehicle Health Management Centre, School of Aerospace, Transport, and Manufacturing, Cranfield University, Bedfordshire MK43 0 AL, UK; [EMAIL_ADDRESS]  
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Footnote 1: [[https://www.cranfield.ac.uk](https://www.cranfield.ac.uk)]([https://www.cranfield.ac.uk](https://www.cranfield.ac.uk))  
real-time data on the system's operational status. These sensors serve as the primary data source for diagnosing faults, enabling a timely detection of issues and supporting the reliability and safety of fuel delivery. During normal operation, components within the aircraft fuel system (AFS) undergo continuous degradation at varying rates, which can eventually lead to functional failures. Common failure modes include tank pressure failure, leakage, blockage, and partial or complete fuel pump loss, each of which impacts sensor readings differently. Detecting, isolating, and repairing faults in the AFS promptly is essential. Therefore, any fault detection system must provide robust and accurate predictions and produce interpretable results for domain operators to minimise ambiguity. Additionally, since multiple fault modes may coexist, it is critical to study AFS fault modes in the context of simultaneous component degradation or failure. ML-based fault diagnosis has garnered significant attention in industry applications. Previous work in this study ([4]) has identified several machine learning (ML) techniques with the potential for classifying faults in complex systems like an aircraft fuel system (AFS). For a comprehensive review of ML methods applicable to fault diagnosis, readers may refer to this prior work. Choosing an effective ML algorithm requires understanding dataset size requirements, as training data quantity influences algorithm performance and overfitting risk. While traditional machine learning (ML) typically requires less data than deep learning, which involves neural networks with multiple layers of nonlinear transformations, complex ML algorithms, often considered traditional ML models with complex rules or ensemble learning approaches, still require larger datasets and more thorough feature engineering. Limited systematic research on dataset size effects exists; however, one study on medical datasets compared six supervised ML algorithms [5], finding that data distribution affected algorithm performance more than dataset size. Simpler algorithms like naive Bayes performed most robustly on smaller datasets, followed by support vector machines (SVMs) and then neural networks (NNs), while decision trees (DTs) showed less robustness but a slightly higher average accuracy than SVMs. The approximate data requirements for common ML algorithms, in ascending order, are as follows: simpler algorithms (e.g., logistic regression, naive Bayes) \(<\) decision trees \(<\) SVMs \(<\) NNs or random forests. While existing ML-based fault diagnosis algorithms can accurately detect individual faults, system-level diagnosis in systems with multiple degradations or faults often still requires multi-algorithm methods [6] and parallel computing [7], which can be resource-intensive in terms of storage and computation. Advances in multiclass ML algorithms have enabled researchers to identify up to seven distinct failure modes [8; 9; 10; 11; 12] or varying severity levels of a single fault [13], though, typically, under the assumption of only one active fault in the system. More recently, studies have begun to explore diagnosing fault combinations; for instance, ref. [14] examined six failure modes and a limited set of dual-fault combinations. However, a comprehensive approach capable of diagnosing all possible fault combinations within a complex system, such as an aircraft fuel system, remains lacking. Developing a robust multi-fault diagnosis method that can efficiently handle a full range of fault combinations is essential and represents the primary challenge this paper seeks to address.