Browsing by Author "Jia, Lilin"

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    Cross-condition fault diagnosis of an aircraft environmental control system (ECS) by transfer learning
    (MDPI, 2023-12-09) Jia, Lilin; Ezhilarasu, Cordelia Mattuvarkuzhali; Jennions, Ian K.
    Fault diagnosis models based on machine learning are often subjected to degradation in performance when dealing with data that are differently distributed than the training data. Such an occasion is common in reality because machines usually operate under various conditions. Transfer learning is a solution for the performance degradation of cross-condition fault diagnosis problems. This paper studies how transfer learning algorithms transfer component analysis (TCA) and joint distribution alignment (JDA) improve the cross-condition fault diagnosis accuracy of an aircraft environmental control system (ECS). Both methods work by transforming the source and target domain data into a feature space where their distributions are aligned to allow a uniform classifier to act accurately in both domains. This paper discovered that both TCA and JDA produce significantly more accurate results than traditional methods on target domains with unlabelled ECS data taken at different operating conditions than the source domain. Additionally, when dealing with unlabelled data from unknown conditions bearing a different composition of classes in the target domain, TCA is found to be more robust and accurate, generating an average predictive accuracy of 95.22%, which demonstrates the ability of transfer learning in solving similar problems in the real-world application of fault diagnosis.
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    Fault diagnosis across aircraft systems using image recognition and transfer learning
    (MDPI, 2025-03-02) Jia, Lilin; Ezhilarasu, Cordelia Mattuvarkuzhali; Jennions, Ian K.
    With advances in machine learning, the fault diagnosis of aircraft systems is becoming more efficient and accurate, which makes condition-based maintenance possible. However, current fault diagnosis algorithms require abundant and balanced data to be trained, which is difficult and expensive to obtain for aircraft systems. One solution is to transfer the diagnostic knowledge from one system to another. To achieve this goal, transfer learning was explored, and two approaches were attempted. The first approach uses relational similarity between the source and target domain features to enable the transfer between two different systems. The results show it only works when transferring from the fuel system to ECS but not to APU. The second approach uses image recognition as the intermediate domain linking the distant source and target domains. Using a deep network pre-trained with fuel system images or the ImageNet dataset finetuned with a small amount of target system data, an improvement in accuracy is found for both target systems, with an average of 6.90% in the ECS scenario and 5.04% in the APU scenario. This study outlines a pioneering approach that transfers knowledge between completely different systems, which is a rare transfer learning application in fault diagnosis.