Time-frequency enhanced characterization method based on asymmetric image reconstruction autoencoder

Abstract

Abstract The vibration signals of mechanical equipment are subject to the influence of complex and variable working conditions, often exhibiting non-smooth and non-linear characteristics. The conventional time-frequency (TF) analysis (TFA) method, which relies on energy concentration, is susceptible to noise and impact, making it challenging to accurately extract fault characteristics. To overcome these limitations, this paper proposes an innovative approach. In this paper, we introduce an asymmetric image reconstruction autoencoder model, which is based on two well-known TFA methods, namely, short-time Fourier transform (STFT) and synchroextracting transform (SET), effectively reducing noise and improving the TF energy concentration process through learning the mapping relationship between STFT and SET. To address the clarity issue in the reconstructed TF images, the paper incorporates a channel attention mechanism known as SE Block into the encoding-decoding structure. Additionally, a skip connection structure is introduced to aid in restoring the structural details of the reconstructed TF images. Moreover, an improved weighted joint loss function is proposed to adaptively enhance various types of TF distribution features. This enhancement ensures that different characteristics of TF distribution are adequately addressed during the reconstruction process. The proposed method is put to the test using both simulated signals and experimental signals from gearbox rolling bearing faults. The results demonstrate that compared to traditional TFA and post-processing methods, the proposed model exhibits superior capabilities in enhancing the TF characterization of multi-source time-varying signals. Furthermore, it demonstrates remarkable robustness to noise and can accurately extract instantaneous frequency. These findings point to the promising potential of this method for mechanical fault identification and diagnosis applications.