Blade crack detection based on domain adaptation and autoencoder of multidimensional vibro-acoustic feature fusion

Abstract

On the condition of variable speeds, the blade crack signal generated by the single-mode sensor is an inherent risk of being misled by noise. Besides, it is challenging to reach the required integrity of information through feature extraction in single analysis domain, leading to the loss of detection accuracy for blade cracks. Therefore, a blade crack detection method based on domain adaptation and autoencoder (AE) of multidimensional vibro-acoustic feature fusion is proposed to extract more complete fault information from multi-sensor and multi-analysis domains. This method extracts one-dimensional (1D) and two-dimensional (2D) features from the frequency domain and smoothed pseudo-wigner-ville distribution (SPWVD) spectrum of the vibro-acoustic signal. Firstly, the 1D and 2D features are extracted without supervision through Stacked Denoising Autoencoder (SDAE) and Convolution Denoising Autoencoder (CDAE), respectively. Then, the extracted multidimensional features are fused and classified through the fully connected network. Finally, for insufficient diagnosis accuracy and data imbalance under certain working condition models with variable speed and combined with inductive transfer mechanism and domain adaptation, the stable diagnosis model under specific speed is applied for transferring and learning the unsupervised networks of other models compensating the weak generalization ability and robustness of the model. The experimental results show that this method is characterized by higher accuracy and robustness compared with the diagnosis method of single-domain feature for the single-mode sensor. Moreover, the proposed migration mechanism can well solve the data imbalance under different working conditions and improve the generalization ability of the few-shot model.

Keywords

Autoencoder vibro-acoustic fusion domain adaptation blade crack detection subspace learning

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