A novel empirical reconstruction Gauss decomposition method and its application in gear fault diagnosis

• Published in: Mechanical systems and signal processing 2024-03, Vol.210, p.111174, Article 111174
• Main Authors: Zheng, Xianbin, Yang, Yu, Hu, Niaoqing, Cheng, Zhe, Cheng, Junsheng
• Format: Article
• Language: English
• Subjects: Empirical reconstruction Gaussian decomposition; Gear fault; Reconstruction decomposition strategy; Spectrum segmentation; Unimodal symmetry hypothesis
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2024.111174

•A new signal decomposition method called Empirical reconstruction Gauss decomposition (ERGD) is proposed.•ERGD proposes a novel spectrum segmentation method based on unimodal symmetry.•ERGD puts forward a new reconstruction decomposition strategy, which effectively eliminates noise in the segments and avoids the oscillations introduced by rectangular filters during the decomposition process.•ERGD is applied to gear fault diagnosis, and a fault diagnosis method based on ERGD is proposed. This paper proposes a novel signal decomposition method, empirical reconstruction Gaussian decomposition (ERGD). ERGD comprises two key steps: spectrum segmentation based on the unimodal symmetry hypothesis and reconstruction decomposition strategy. ERGD puts forward the unimodal symmetry hypothesis along with the corresponding unimodal symmetry estimation method. Building upon this, ERGD presents a novel spectrum segmentation approach that adaptively divides the spectrum into energy-concentrated and unimodal symmetry segments, thereby circumventing issues of over-segmentation. The reconstruction decomposition strategy defines a series of α factor generalized Gaussian filters and then reconstructs them into ideal filter banks in order to accomplish signal decomposition. The ideal filter banks can effectively eliminate noise in the segment. It is worth mentioning that ERGD has the ability to adaptively adjust the overlap and attenuation properties of the ideal filter, which can effectively suppress noise interference. As a new signal decomposition method, ERGD is capable of decomposing signals into a series of Gaussian intrinsic mode functions (GIMFs) possessing orthogonality. Applying ERGD to gear fault diagnosis, both simulation and experimental signal analysis results demonstrate the excellent performance of the proposed method in signal decomposition. It significantly contributes to the attainment of precise gear fault diagnosis and holds practical value.