LIGHTWEIGHT MULTI-SCALE CONVOLUTIONAL TRANSFORMER FOR AIRCRAFT FAULT DIAGNOSIS USING VIBRATION ANALYSIS
DOI:
https://doi.org/10.15588/1607-3274-2025-1-7Keywords:
fault detection, deep learning, rotating machinery, signal processing, transformer, neural networksAbstract
Context. Fault diagnosis in rotating machinery, especially in aircraft, plays an important role in health monitoring systems. Early and accurate fault detection can significantly reduce the cost of repair and increase the lifetime of the mechanism. To detect the fault efficiently, intelligent methods based on traditional machine learning and deep learning techniques are used. The object of the research is the process of detecting faults in aircraft based on vibration analysis.
Objective of the work is the development of a deep learning method for fault diagnosis in rotating machinery with a high accuracy rate.
Method. The proposed method employs Transformer architecture. The first stage of processing the vibration signal is the multiscale feature extractor. This stage allows the model to examine input signals in different scales and reduce the impact of the noise.
The second stage is the Convolutional Transformer neural network. The convolution was introduced to the Transformer to combine locality and long-range dependencies feature extraction. The Self-attention mechanism of the Transformer was changed to Channel Attention, which reduces the number of parameters but maintains the strength of the attention. To maintain this idea, similar changes were made in the position-wise feed-forward network.
Results. The proposed method is tested on the aircraft vibration dataset. Two conditions were chosen for testing: limited data and noisy environment. The limited data condition is simulated by selecting a small number of samples into the training set (a maximum of 10 per class). The noisy environment condition is simulated by adding Gaussian noise to the raw signal. According to the obtained results, the proposed method achieves a high average precision metric rate with a small number of parameters. The experiments also show the importance of the proposed modules and changes, confirming the assumptions about the process of feature extraction.
Conclusion. The results of the conducted experiments show that the proposed model can detect faults with almost perfect accuracy, even with a small number of parameters. The proposed lightweight model is robust in limited data conditions and noisy environment conditions. The prospects for further research are the development of fast and accurate neural networks for fault diagnosis and the development of limited data training techniques.
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