Application of Auto-Regulative Sparse Variational Mode Decomposition in Mechanical Fault Diagnosis
نویسندگان
چکیده
The variational mode decomposition (VMD) method has been widely applied in the field of mechanical fault diagnosis as an excellent non-recursive signal processing tool. performance VMD depends on its inherent prior parameters. Searching for key parameters using intelligent optimization algorithms poses challenges internal essence and fitness function selection algorithm. Moreover, computational complexity is high. Meanwhile, such methods are not competitive evaluating orthogonality between intrinsic functions reconstruction error a joint indictor termination decomposition. Therefore, this paper proposes new auto-regulative sparse (ASparse–VMD) to achieve accurate feature extraction. regularization term handles sparsification by constructing L2-norm with damping coefficient ε, number K set adaptively recursive manner ensure appropriateness. penalty parameter α dynamically selected according modes sampling frequency. update step τ algorithm signal-to-noise ratio singleness modal components suppress aliasing. experimental results simulation measured demonstrate effectiveness proposed strategies improving defects VMD. Extensive comparisons state-of-the-art show that more effective practical hybrid extraction faults.
منابع مشابه
Resonance-Based Sparse Signal Decomposition and Its Application in Mechanical Fault Diagnosis: A Review
Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed...
متن کاملMechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier
Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily wit...
متن کاملFault Diagnosis of On-Load Tap-Changer Based on Variational Mode Decomposition and Relevance Vector Machine
Abstract: In order to improve the intelligent diagnosis level of an on-load tap-changer’s (OLTC) mechanical condition, a feature extraction method based on variational mode decomposition (VMD) and weight divergence was proposed. The harmony search (HS) algorithm was used to optimize the parameter selection of the relevance vector machine (RVM). Firstly, the OLTC vibration signal was decomposed ...
متن کاملPlanetary Gearbox Fault diagnosis via Joint Amplitude and Frequency Demodulation Analysis Based on Variational Mode Decomposition
Zhipeng Feng 1 ID , Dong Zhang 1 ID and Ming J. Zuo 2,3,* 1 School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China; [email protected] (Z.F.); [email protected] (D.Z.) 2 School of Mechatronics Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 3 Department of Mechanical Engineering, University of Alb...
متن کاملA Fault Diagnosis Method for Automaton based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition
In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronics
سال: 2023
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics12143081