Cavitation Diagnostics Based on Self-Tuning VMD for Fluid Machinery with Low-SNR Conditions  

作　　者：Hao Liu Zheming Tong Bingyang Shang Shuiguang Tong 

机构地区：[1]State Key Laboratory of Fluid Power and Mechatronic Systems,School of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China

出　　处：《Chinese Journal of Mechanical Engineering》2023年第4期212-226,共15页中国机械工程学报（英文版）

基　　金：Supported by National Natural Science Foundation of China(Grant No.52075481);Zhejiang Provincial Natural Science Foundation of China(Grant No.LD21E050003);Central Government Fund for Regional Science and Technology Development of China(Grant No.2023ZY1033).

摘　　要：Variational mode decomposition(VMD)is a suitable tool for processing cavitation-induced vibration signals and is greatly affected by two parameters:the decomposed number K and penalty factorαunder strong noise interference.To solve this issue,this study proposed self-tuning VMD(SVMD)for cavitation diagnostics in fluid machinery,with a special focus on low signal-to-noise ratio conditions.A two-stage progressive refinement of the coarsely located target penalty factor for SVMD was conducted to narrow down the search space for accelerated decomposition.A hybrid optimized sparrow search algorithm(HOSSA)was developed for optimalαfine-tuning in a refined space based on fault-type-guided objective functions.Based on the submodes obtained using exclusive penalty factors in each iteration,the cavitation-related characteristic frequencies(CCFs)were extracted for diagnostics.The power spectrum correlation coefficient between the SVMD reconstruction and original signals was employed as a stop criterion to determine whether to stop further decomposition.The proposed SVMD overcomes the blindness of setting the mode number K in advance and the drawback of sharing penalty factors for all submodes in fixed-parameter and parameter-optimized VMDs.Comparisons with other existing methods in simulation signal decomposition and in-lab experimental data demonstrated the advantages of the proposed method in accurately extracting CCFs with lower computational cost.SVMD especially enhances the denoising capability of the VMD-based method.

关 键 词：Fluid machinery Self-tuning VMD Cavitation diagnostics Hybrid optimized sparrow search algorithm 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TH17[自动化与计算机技术—控制科学与工程]