Research on Rolling Bearing Fault Diagnosis Method Based on Improved LMD and CMWPE

Song, Enzhe; Gao, Feng; Yao, Chong; Ke, Yun

doi:10.1007/s11668-021-01226-3

Cited by 6 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section applies the BT-EVMD algorithm to a typical analog signal and compares its performance with other decomposition algorithms. The signal is similar to those in references [15,29], but its composition is more complex. It contains high-frequency weak signal with intermittent time, periodic impulse signal and combined components with similar frequency.…”

Section: Experiments Validations 41 Simulation Analysissupporting

confidence: 67%

“…However, there are some problems such as fitting overshoot, endpoint effect and modal aliasing, which seriously restrict its practical application [13]. Local mean decomposition (LMD) [14] adaptively decomposes the non-stationary multi-component signal into the sum of several product functions (PF) with physical meaning of instantaneous frequency [15]. It has the disadvantages of signal mutation and large amount of calculation caused by demodulation [16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Empirical Variational Mode Decomposition Based on Binary Tree Algorithm

Zhou

et al. 2022

Sensors

View full text Add to dashboard Cite

Aiming at non-stationary signals with complex components, the performance of a variational mode decomposition (VMD) algorithm is seriously affected by the key parameters such as the number of modes K, the quadratic penalty parameter α and the update step τ. In order to solve this problem, an adaptive empirical variational mode decomposition (EVMD) method based on a binary tree model is proposed in this paper, which can not only effectively solve the problem of VMD parameter selection, but also effectively reduce the computational complexity of searching the optimal VMD parameters using intelligent optimization algorithm. Firstly, the signal noise ratio (SNR) and refined composite multi-scale dispersion entropy (RCMDE) of the decomposed signal are calculated. The RCMDE is used as the setting basis of the α, and the SNR is used as the parameter value of the τ. Then, the signal is decomposed into two components based on the binary tree mode. Before decomposing, the α and τ need to be reset according to the SNR and MDE of the new signal. Finally, the cycle iteration termination condition composed of the least squares mutual information and reconstruction error of the components determines whether to continue the decomposition. The components with large least squares mutual information (LSMI) are combined, and the LSMI threshold is set as 0.8. The simulation and experimental results indicate that the proposed empirical VMD algorithm can decompose the non-stationary signals adaptively, with lower complexity, which is O(n2), good decomposition effect and strong robustness.

show abstract

Section: Experiments Validations 41 Simulation Analysissupporting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Empirical Variational Mode Decomposition Based on Binary Tree Algorithm

Zhou

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Minghong Han [20] used the combination method of LMD and multi-scale symbolic dynamic information entropy (MSDE) to diagnose and analyze the fault type and degree of rolling bearing and achieved good results. Song Enzhe [21] improved LMD, combined with composite multi-scale weighted permutation entropy (CMWPE) and support vector machine (SVM), and accurately distinguished various fault types of rolling bearings under the same fault degree, with more reliable diagnosis results. Compared with the EMD method, the LMD method can effectively suppress the end effect and solve the problems of under-envelope and over-envelope [22,23].…”

Section: Literature Analysismentioning

confidence: 99%

Fault Diagnosis of Mine Truck Hub Drive System Based on LMD Multi-Component Sample Entropy Fusion and LS-SVM

Xu,

Li,

Zhang

et al. 2023

Actuators

View full text Add to dashboard Cite

As the main transportation equipment in ore mining, the wheel drive system of mining trucks plays a crucial role in the transportation capacity of mining trucks. The internal components of the hub drive system are mainly composed of bearings, gears, etc. The vibration signals caused during operation are nonlinear and nonstationary complex signals, and there may be more than one factor that causes faults, which causes certain difficulties for the fault diagnosis of the hub drive system. A fault diagnosis method based on local mean decomposition (LMD) multi-component sample entropy fusion and LS-SVM is proposed to address this issue. Firstly, the LMD method is used to decompose the vibration signals in different states to obtain a finite number of PF components. Then, based on the typical correlation analysis method, the distribution characteristics and correlation coefficients of vibration signals in the frequency domain under different states are calculated, and effective PF multi-component sample entropy features are constructed. Finally, the LS-SVM multi-fault classifier is used to train and test the extracted multi-component sample entropy features to verify the effectiveness of the method. The experimental results show that, even in small-sample data, the LMD multi-component sample entropy fusion and LS-SVM method can accurately extract fault features of vibration signals and complete classification, achieving fault diagnosis of wheel drive systems.

show abstract