Enhancing aero-engine airway fault diagnosis through multimodal deep neural networks

Zhishun Yang; Weibin Lin; Zhenyu Guo; Yongxin Zheng

doi:10.1117/12.3026748

15 March 2024 Enhancing aero-engine airway fault diagnosis through multimodal deep neural networks

Zhishun Yang, Weibin Lin, Zhenyu Guo, Yongxin Zheng

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 130751E (2024) https://doi.org/10.1117/12.3026748
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

This research delves into an innovative approach for diagnosing faults in aero-engine airways using a multimodal deep neural network. The method involves creating a sophisticated neural network model capable of precisely detecting and categorizing various airway faults within the engine. This is achieved by synergistically integrating data from multiple sensory modalities, such as sound and vibration, to enhance the accuracy of fault identification and classification. The study conducted in-depth analysis and optimisation in data preprocessing, feature extraction and model design to improve the performance of the diagnostic model. Experimental results show that the proposed multimodal deep neural network method exhibits high accuracy and reliability in the diagnosis of aero-engine airway faults, and has potential for practical application.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhishun Yang, Weibin Lin, Zhenyu Guo, and Yongxin Zheng "Enhancing aero-engine airway fault diagnosis through multimodal deep neural networks", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 130751E (15 March 2024); https://doi.org/10.1117/12.3026748

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