A VME method based on the convergent tendency of VMD and its application in multi-fault diagnosis of rolling bearings

Li, Cuixing; Liu, Yongqiang; Liao, Yingying; Wang, Jiujian

doi:10.1016/j.measurement.2022.111360

Cited by 20 publications

(16 citation statements)

References 32 publications

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“…In essence, SVMD incorporates the additional constraints R 1 , R 2 , and R 3 on the foundation of VMD, enabling the decomposition of signals by employing a continuous application of variational mode extraction [28,29]. The process is considered complete when either all modes have been extracted or the error between the input signal and the sum total of modes is lower than a certain threshold.…”

Section: Theory Of Svmdmentioning

confidence: 99%

A feature extraction method for rotating machinery fault diagnosis based on a target detection index and successive variational mode decomposition

Cao,

Zhang,

et al. 2023

Meas. Sci. Technol.

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Rotating machinery feature extraction is critical for the subsequent fault diagnosis and ensuring safe and stable operation. However, the commonly used methods often have limitations, such as the extracted features being redundant or insufficient and the method parameters generally being set based on experience. Although some feature extraction methods apply optimization algorithms for parameter setting, their objective functions are often too simple to obtain favorable fault diagnosis results. To address these issues, a target detection index (TDI) is constructed which can consider the discrimination among features more comprehensively and make the extracted features more sensitive. Furthermore, a fault feature extraction method is proposed based on TDI and successive variational mode decomposition (SVMD). Taking TDI as the objective function, genetic algorithm (GA) is used for the feature selection process and SVMD parameter optimization. The obtained features are then fused and visualized using t-distributed stochastic neighbor embedding and are classified using support vector machines. The Case Western Reserve University data and hydropower generating unit data are employed for method verification. When compared with other decomposition algorithms, the proposed method exhibits great ability at extracting highly sensitive features.

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Section: Theory Of Svmdmentioning

confidence: 99%

A feature extraction method for rotating machinery fault diagnosis based on a target detection index and successive variational mode decomposition

Cao,

Zhang,

et al. 2023

Meas. Sci. Technol.

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“…The simulated signal x 2 (t), as shown in equation (10), contains two resonant frequency bands. The signal x 3 (t), as shown in equation (11), includes three resonant frequency bands. The convergent trajectories of their CFs are shown in figures 3 and 4, respectively:…”

Section: Research On the Characteristics Of The U-shaped Convergence ...mentioning

confidence: 99%

“…Meanwhile, there is inevitably noise in the collected signal. How to adaptively identify and extract the resonance frequency band rich in fault information has always been the key to realizing bearing fault detection [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive single-mode variational mode decomposition and its applications in wheelset bearing fault diagnosis

Li¹,

Liu²,

Liao³

et al. 2022

Meas. Sci. Technol.

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In recent years, many studies on variational mode decomposition (VMD) have mainly focused on choosing the number of modes and balancing parameter, while less research focuses on the internal properties of VMD. This paper proposes an adaptive single-mode VMD (ASMVMD) method based on the convergence characteristics of VMD and the adaptivity of particle swarm optimization (PSO). Firstly, we study the convergence characteristics of single-mode VMD and find that the U-shaped convergence region related to fault impact is very wide in the whole frequency domain. Secondly, based on the characteristics of the U-shaped convergence region, a new population position initialization strategy is proposed. Finally, the improved PSO is used to optimize the initial center frequency and balancing parameter of single-mode VMD. The effectiveness of the proposed method is verified by analyzing the simulated signal and wheelset bearing fault signals. Compared with the fast kurtogram and Autogram, it is shown that ASMVMD has a stronger capability of fault feature extraction.

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“…To determine these two parameters adaptively, a VME method based on VMD convergence is recommended for multi-fault diagnostics of rolling bearings. 18…”

Section: Introductionmentioning

confidence: 99%

“…To determine these two parameters adaptively, a VME method based on VMD convergence is recommended for multi-fault diagnostics of rolling bearings. 18 There are many methods proposed, Qin et al 19 proposed a minimum absolute contraction and selection operator (LASSO) regression method VMD decomposition based on the selection of component effective variables. This VMD-builds LASSO models for stock data, the results show that the model has high prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

VMD analysis of pulsating flow pressure signal of oil with different particle concentration

Liu

Cheng

2022

Advances in Mechanical Engineering

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The Variational Mode Decomposition (VMD) method was used to decompose the oil pressure signals containing different particle concentrations; the number of components of VMD decomposition is determined by using the criterion of joint comparison between the slope of the center frequency of each component and the main frequency of the component of VMD decomposition, and a method for determining the main components of the signal is proposed by the average energy value; the Hilbert envelope demodulation analysis was carried out for the maximum energy component of each pressure signal. The results showed that the larger the particle concentration in the oil, the greater the number of main components obtained by VMD decomposition, and the proportion of the main components per unit concentration showed a monotonically decreasing trend. The main frequency of the main component is the fundamental frequency of its signal or the multiplication frequency combined with the rotating shaft frequency; there is an amplitude modulation relationship between any components; with the increase of particle concentration, the fundamental frequency of the pressure signal decreases successively; the maximum energy component of each pressure signal has the characteristics of amplitude modulation and frequency modulation in different degrees.

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A VME method based on the convergent tendency of VMD and its application in multi-fault diagnosis of rolling bearings

Cited by 20 publications

References 32 publications

A feature extraction method for rotating machinery fault diagnosis based on a target detection index and successive variational mode decomposition

A feature extraction method for rotating machinery fault diagnosis based on a target detection index and successive variational mode decomposition

Adaptive single-mode variational mode decomposition and its applications in wheelset bearing fault diagnosis

VMD analysis of pulsating flow pressure signal of oil with different particle concentration

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