A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine

Lv, Zhongliang; Tang, Baoping; Zhou, Yi; Zhou, Chen

doi:10.1155/2016/3196465

Cited by 43 publications

(25 citation statements)

References 24 publications

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“…The number of decomposition mode number K is determined automatically by the method in Reference [30]. VMD decomposition is performed on the signal, and the correlation coefficient between the last modal component and the decomposition of the original signal is calculated.…”

Section: Simulation Processmentioning

confidence: 99%

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

2020

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In order to improve the accuracy of wind power prediction and ensure the effective utilization of wind energy, a short-term wind power prediction model based on variational mode decomposition (VMD) and an extreme learning machine (ELM) optimized by an improved grey wolf optimization (GWO) algorithm is proposed. The original wind power sequence is decomposed into series of modal components with different center frequencies by the VMD method and some new sequences are obtained by phase space reconstruction (PSR). Then, the ELM model is established for different new time series, and the improved GWO algorithm is used to optimize its parameters. Finally, the output results are weighted and merged as the final predicted value of wind power. The root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed VMD-improved GWO-ELM prediction model in the paper are 5.9113%, 4.6219%, and 13.01% respectively, which are better than these of ELM, back propagation (BP), and the improved GWO-ELM model. The simulation results show that the proposed model has higher prediction accuracy than other models in short-term wind power prediction.

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Section: Simulation Processmentioning

confidence: 99%

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

2020

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“…They proposed a new kernel function based on thin plate spline Littlewood-Paley wavelet kernel function. Lv et al [14] proposed a combined method based on both variational mode decomposition (VMD) and multi-kernel support vector machine (MK-SVM) optimized by an immune genetic algorithm (IGA) for damage detection in mechanical systems. Cha et al [15] proposed a method based on image processing to detect the damages in civil infrastructures.…”

Section: Structural Damage Detectionmentioning

confidence: 99%

Vibration Anatomy and Damage Detection in Power Transmission Towers with Limited Sensors

Mohammadi

Mirtaheri

Salkhordeh

et al. 2020

Sensors

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This study presents a technique to identify the vibration characteristics in power transmission towers and to detect the potential structural damages. This method is based on the curvature of the mode shapes coupled with a continuous wavelet transform. The elaborated numerical method is based on signal processing of the output that resulted from ambient vibration. This technique benefits from a limited number of sensors, which makes it a cost-effective approach compared to others. The optimal spatial location for these sensors is obtained by the minimization of the non-diagonal entries in the modal assurance criterion (MAC) matrix. The Hilbert–Huang transform was also used to identify the dynamic anatomy of the structure. In order to simulate the realistic condition of the measured structural response in the field condition, a 10% noise is added to the response of the numerical model. Four damage scenarios were considered, and the potential damages were identified using wavelet transform on the difference of mode shapes curvature in the intact and damaged towers. Results show a promising accuracy considering the small number of applied sensors. This study proposes a low-cost and feasible technique for structural health monitoring.

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“…Similar to EWT, as the fault diagnosis of machinery is a popular issue of dynamic analysis, a great amount of research regarding application and improvement has been undertaken for VMD in this domain [ 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 ]. After decomposition of signals by using VMD, signal characteristics of fault are obtained by some other methods.…”

Section: Variational Mode Decompositionmentioning

confidence: 99%

A Comparative Study of Four Kinds of Adaptive Decomposition Algorithms and Their Applications

Liu

Luo

Huang

et al. 2018

Sensors

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The adaptive decomposition algorithm is a powerful tool for signal analysis, because it can decompose signals into several narrow-band components, which is advantageous to quantitatively evaluate signal characteristics. In this paper, we present a comparative study of four kinds of adaptive decomposition algorithms, including some algorithms deriving from empirical mode decomposition (EMD), empirical wavelet transform (EWT), variational mode decomposition (VMD) and Vold–Kalman filter order tracking (VKF_OT). Their principles, advantages and disadvantages, and improvements and applications to signal analyses in dynamic analysis of mechanical system and machinery fault diagnosis are showed. Examples are provided to illustrate important influence performance factors and improvements of these algorithms. Finally, we summarize applicable scopes, inapplicable scopes and some further works of these methods in respect of precise filters and rough filters. It is hoped that the paper can provide a valuable reference for application and improvement of these methods in signal processing.

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A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine

Cited by 43 publications

References 24 publications

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

Vibration Anatomy and Damage Detection in Power Transmission Towers with Limited Sensors

A Comparative Study of Four Kinds of Adaptive Decomposition Algorithms and Their Applications

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