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

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.