Fault diagnosis technology of aero-engine rotors based on meta-action theory driven by machine learning for reliability improvement

Abstract

The intricate structure of electromechanical products presents significant challenges in fault diagnosis, and conventional methods frequently fail to capture the correlation between time-domain and frequency-domain features of fault vibration signals. Moreover, these methods typically rely on extensive training datasets and demonstrate limited generalization capabilities. To overcome these limitations, this paper introduces a fault analysis framework based on the meta-action unit (MAU) to streamline fault diagnosis processes in electromechanical products. An integrated model comprising Fast Fourier Transform (FFT), Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (Bi-GRU), Transformer and Attention mechanisms, which designated as the FFT-CNN-Bi-GRU-Transformer-Attention model, was developed to enhance the extraction and representation of vibration signal features, thereby improving model robustness and accuracy. The methodology involves several sequential processes. Initially, fault signals were collected using the MAU and transformed from the time-domain to frequency-domain via FFT. Subsequently, a CNN was employed to automatically extract salient features from the frequency-domain signals. Bi-GRU was then applied to process these features in both forward and backward directions, thus enriching the expressiveness of the data representation. To facilitate efficient parallel computation, the Transformer mechanism was incorporated to refine the output from the Bi-GRU, while the Attention mechanism was used to capture intricate fault features and patterns, significantly enhancing the model’s diagnostic performance. The proposed method was validated using an aero-engine rotor unit as a test case, achieving an accuracy of 98.16 %. Comparative analyses with conventional fault diagnosis techniques underscore the clear advantages of the proposed method. This method provides a foundation for accurate fault identification and timely maintenance of aero-engine rotors, as well as other electromechanical products with analogous structural characteristics.