Transient Signal Analysis Using Parallel Time-Frequency Manifold Filtering for Bearing Health Diagnosis

• Main Authors: Deqi Zhang, Xiaoxi Ding, Wenbin Huang, Qingbo He
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Sparse representation; time-frequency image; parallel time-frequency manifold filtering; image morphology filtering; fault diagnosis
• Online Access: https://ieeexplore.ieee.org/document/8917988/

It is crucial to identify and extract the weak transient features embedded in the vibration signals for bearing health monitoring and fault diagnosis. However, due to the macro-structural disturbance and background noise interference, it is not easy to mine the transient features, especially at the apparent failure stage. Meanwhile, the actual mechanism of bearing fault detect can be not simply expressed by the formulated theory models without consideration of the actual physical collision process. To overcome these issues, motivated by the merits of time-frequency manifold (TFM), this paper proposes a new transient feature extraction method, called parallel time-frequency manifold (PTFM) filtering, by simultaneously using TFM-based reconstruction with TFM-based filter in parallel for transient feature extraction. First, to improve the computational efficiency of TFM, two-dimensional discrete wavelet transform is employed on the raw time-frequency distribution (TFD) with image compression. TFM learning is later used to mine the principle manifolds from those approximation sub-images. Then, the amplitudes of the raw time-frequency image can be reconstructed by TFM feature bases while the desired location of time-frequency feature can be captured by TFM morphology filter in a process of image morphology. With raw time-frequency phases in a series of inverse processes, the de-noised signal can be finally synthesized from these filtered images. The proposed method accomplishes a natural manifold feature denoising by combining the sparse theory with image morphology, and demonstrates attractive prospects in the following three aspects: signal de-noising with a self-learning mode in the view of image morphology processing combined with sparse theory, fault diagnosis with in-band noise/close interference removal, and machine health monitoring with capability in capturing sensitive failure information. Simulations and experiments confirmed the effectiveness of the proposed PTFM filtering method in noise suppression and feature enhancement, which is valuable for bearing health monitoring and diagnosis applications.