Time-frequency synthesis analysis for complex signal of rotating machinery via variational mode manifold reinforcement learning. (April 2020)

• Record Type: Journal Article
• Authors: Li, Quanchang; Ding, Xiaoxi; Wang, Tao; Zhang, Mingkai; Huang, Wenbin; Shao, Yimin
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 234:Number 7(2020)
• Page Start: 1438
• Page End: 1455
• Publication Date: 2020-04
• Subjects: Time-frequency synthesis analysis -- adaptive variational mode decomposition -- manifold reinforcement learning -- short-frequency Fourier transform -- fault diagnosis; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/0954406219897688
• Languages: English
• ISSNs: 0954-4062

Abstract

The transient signal caused by localized fault in rotating machinery always contains complex modulation information with heavy background noise distributed, which brings much difficulties for fault feature identification in the application of rotating machinery fault diagnosis. Focusing on the sensitive feature extraction from these complex signals, this paper proposes a novel variational mode manifold reinforcement learning (VM 2 RL) to adaptively construct time-frequency synthesis analysis for enhancement of transient features. First, a method of adaptive variational mode decomposition (VMD) is employed to divide the raw spectrum of the given signal into several sub-bands with different frequency modulation information. Second, an improved time-frequency manifold (ITFM) learning is introduced to gain the topological manifold structure from those sub-distributions in time-frequency domain. Then, a sound-enhanced signature of transient features on the whole time-frequency plane can be synthesized by combining those sub-TFMs from each modulated segment back to the corresponding frequency band. Finally, the time-frequency envelope spectrum for fault diagnosis is further obtained through statistically evaluating their amplitude distribution. Among them, short-frequency Fourier transform (SFFT) is introduced to transform local frequency bands into a series of TFDs which improves the computational efficiency of TFM learning. In this manner, the desired transient distribution on full time-frequency plane can be automatically reconstructed by VM 2 RL with manifold reinforced in a data-driven way. A simulation study and two experimental signals are both analyzed here, and fast spectral kurtosis and conventional VMD methods are also used to verify its effectiveness. Meanwhile, a quantitative analysis has been provided to further illustrate its superiority in the application of complex signal fault of rotating machinery.