Abstract
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.
Highlights
Bearings have been widely used in rotating machines of modern industry
This paper presents a new signal denoising method called parallel time-frequency manifold (PTFM) filtering by combining time-frequency manifold (TFM) basis reconstruction and TFM morphology filtering to extract transient characteristics embedded in noisy signals for bearing health monitoring and diagnosis
The PTFM filtering introduces a mergence of TFM learning and sparse representation into the image morphology filtering field
Summary
Bearings have been widely used in rotating machines of modern industry. They are damageable parts where. To extract the transient features, many signal processing methods have been proposed in the area of bearing health monitoring and diagnosis [5], [7], [11]–[15], such as orthogonal matching pursuit (OMP) [12], band-pass filtering [13] Another two feature extraction methods, wavelet transform (WT) [14] and time-frequency analysis (TFA) [15], are widely used in signal denoising as famous time-frequency (TF) domain denoising methods, which combine time and frequency information together. Since the TFMs represent the intrinsic transient components with the merit of noise suppression, the PTFM-based signal will have good denoising performance This merit could capture sensitive failure information in monitoring and diagnosis of bearings.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.