Advances in Prognostics and Health Management for Aircraft Landing Gear—Progress, Challenges, and Future Possibilities

Raouf, Izaz; Kumar, Prashant; Cheon, Yubin; Tanveer, Mohad; Jo, Soo-Ho; Kim, Heung Soo

doi:10.1007/s40684-024-00646-4

Int. J. of Precis. Eng. and Manuf.-Green Tech.

2024

DOI: 10.1007/s40684-024-00646-4

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Advances in Prognostics and Health Management for Aircraft Landing Gear—Progress, Challenges, and Future Possibilities

Izaz Raouf,

Prashant Kumar,

Yubin Cheon

et al.

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2024

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Cited by 2 publications

References 94 publications

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Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network

Raouf,

Kumar,

Soo Kim

2024

Expert Systems with Applications

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Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network

Raouf,

Kumar,

Soo Kim

2024

Expert Systems with Applications

View full text Add to dashboard Cite

A Bearing Fault Diagnosis Method in Scenarios of Imbalanced Samples and Insufficient Labeled Samples

Cheng,

Lu,

Liang

et al. 2024

Applied Sciences

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In practical working environments, rolling bearings are one of the components that are prone to failure. Their vibration signal samples are faced with challenges, mainly including the imbalance between normal and fault samples as well as an insufficient number of labeled samples. This study proposes a sample-expansion method based on generative adversarial networks (GANs) and a fault diagnosis method based on a transformer to solve the above issues. First, selective kernel networks (SKNets) and a genetic algorithm (GA) were introduced to construct a conditional variational autoencoder–evolutionary generative adversarial network with a selective kernel (CVAE-SKEGAN) to achieve a balance between the proportion of normal and faulty samples. Then, a semi-supervised learning–variational convolutional Swin transformer (SSL-VCST) network was built for the fault classification, specifically introducing variational attention and semi-supervised mechanisms to reduce the overfitting risk of the model and solve the problem of a shortage of labeled samples. Three typical operating conditions were designed for the multi-case applicability verification. The results show that the method proposed in this study had good application effects when solving both sample imbalances and labeled-sample deficiencies and improved the accuracy of fault diagnosis in the above scenarios.

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Advances in Prognostics and Health Management for Aircraft Landing Gear—Progress, Challenges, and Future Possibilities

Cited by 2 publications

References 94 publications

Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network

Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network

A Bearing Fault Diagnosis Method in Scenarios of Imbalanced Samples and Insufficient Labeled Samples

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