Variational mode decomposition based low rank robust kernel extreme learning machine for solar irradiation forecasting

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

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Vibration Tendency Prediction of Hydroelectric Generator Unit Based on Fast Ensemble Empirical Mode Decomposition and Kernel Extreme Learning Machine with Parameters Optimization

2018 11th International Symposium on Computational Intelligence and Design (ISCID) ◽

10.1109/iscid.2018.10167 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yahui Shan ◽

Jianzhong Zhou ◽

Wei Jiang ◽

Jie Liu ◽

Yanhe Xu ◽

...

Keyword(s):

Extreme Learning Machine ◽

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Parameters Optimization ◽

Mode Decomposition ◽

Hydroelectric Generator ◽

Kernel Extreme Learning Machine ◽

Generator Unit ◽

Learning Machine

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Research on Multi-Domain Fault Diagnosis of Gearbox of Wind Turbine Based on Adaptive Variational Mode Decomposition and Extreme Learning Machine Algorithms

Energies ◽

10.3390/en13061375 ◽

2020 ◽

Vol 13 (6) ◽

pp. 1375 ◽

Cited By ~ 1

Author(s):

Hui Li ◽

Bangji Fan ◽

Rong Jia ◽

Fang Zhai ◽

Liang Bai ◽

...

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Grey Wolf Optimizer ◽

Mechanical Equipment ◽

Variational Mode Decomposition ◽

Time Frequency ◽

Mode Decomposition ◽

Fault Features ◽

Frequency Domains ◽

Learning Machine

Since variational mode decomposition (VMD) was proposed, it has been widely used in condition monitoring and fault diagnosis of mechanical equipment. However, the parameters K and α in the VMD algorithm need to be set before decomposition, which causes VMD to be unable to decompose adaptively and obtain the best result for signal decomposition. Therefore, this paper optimizes the VMD algorithm. On this basis, this paper also proposes a method of multi-domain feature extraction of signals and combines an extreme learning machine (ELM) to realize comprehensive and accurate fault diagnosis. First, VMD is optimized according to the improved grey wolf optimizer; second, the feature vectors of the time, frequency, and time-frequency domains are calculated, which are synthesized after dimensionality reduction; ultimately, the synthesized vectors are input into the ELM for training and classification. The experimental results show that the proposed method can decompose the signal adaptively, which produces the best decomposition parameters and results. Moreover, this method can extract the fault features of the signal more completely to realize accurate fault identification.

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Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Complexity ◽

10.1155/2020/6947059 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Hong Yang ◽

Lipeng Gao ◽

Guohui Li

Keyword(s):

Prediction Model ◽

Extreme Learning Machine ◽

Acoustic Signal ◽

Grey Wolf ◽

Underwater Acoustic ◽

Signal Prediction ◽

Mode Decomposition ◽

Kernel Extreme Learning Machine ◽

Learning Machine ◽

Underwater Acoustic Signal

Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an underwater acoustic signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and underwater acoustic signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of underwater acoustic signal series.

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Ultra-short-term wind speed prediction based on variational mode decomposition and optimized extreme learning machine

Wind Engineering ◽

10.1177/0309524x211038547 ◽

2021 ◽

pp. 0309524X2110385

Author(s):

Lian Lian ◽

Kan He

Keyword(s):

Wind Speed ◽

Prediction Model ◽

Extreme Learning Machine ◽

Prediction Accuracy ◽

Good Prediction ◽

Variational Mode Decomposition ◽

Short Term ◽

Mode Decomposition ◽

Whale Optimization ◽

Learning Machine

The main purpose of this paper is to improve the prediction accuracy of ultra-short-term wind speed. It is difficult to predict the ultra-short-term wind speed because of its unstable, non-stationary and non-linear. Aiming at the unstable and non-stationary characteristics of ultra-short-term wind speed, the variational mode decomposition algorithm is introduced to decompose the ultra-short-term wind speed data, and a series of stable and stationary components with different frequencies are obtained. The extreme learning machine with good prediction performance and real-time performance is selected as the prediction model of decomposed components. In order to solve the problem of random setting of input weights and bias of extreme learning machine, whale optimization algorithm is used to optimize extreme learning machine to improve the regression performance. The performance of the developed prediciton model is verified by real ultra-short-term wind speed sample data. Five prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, eight performance indicators, and Pearson’s test correlation coefficient, the results show that the proposed prediction model has high prediction accuracy.

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Iterative variational mode decomposition and extreme learning machine for gearbox diagnosis based on vibration signals

JOURNAL OF MECHANICAL ENGINEERING AND SCIENCES ◽

10.15282/jmes.13.1.2019.10.0380 ◽

2019 ◽

Vol 13 (1) ◽

pp. 4477-4492

Author(s):

M. Firdaus Isham ◽

M. Salman Leong ◽

L. M. Hee ◽

Z. A. B. Ahmad

Keyword(s):

Signal Processing ◽

Fault Diagnosis ◽

Extreme Learning Machine ◽

Processing Method ◽

Mode Number ◽

Variational Mode Decomposition ◽

Vibration Signals ◽

Signal Processing Method ◽

Mode Decomposition ◽

Learning Machine

Vibration-based monitoring and diagnosis provide an excellent and reliable monitoring strategies for maintaining and sustaining a million dollars of industrial assets. The signal processing method is one of the key elements in gearbox fault diagnosis for extracting most useful information from raw vibration signals. Variational mode decomposition (VMD) is one of the recent signal processing methods that helps to solve many limitations in traditional signal processing method. However, pre-determine the input parameters especially the mode number become a challenging task for using this method. Then, this study aims to propose an iterative approach for selecting the mode number for the VMD method by using the normalized mean value (NMV) plot. The NMV value is calculates based on the ratio of a summation of VMD modes and the input signals. The result shows that the proposed iterative VMD approach can select an accurate mode number for the VMD method. Then, the vibration signals decomposed into different VMD modes and used for gearbox fault diagnosis. Statistical features have been extracted from the selected VMD modes and pass into extreme learning machine (ELM) for fault classification. Iterative VMD-ELM provide significance improvement of about 20% higher accuracy in classification result as compared with EMD-ELM. Hence, this research study offers a new mean for gearbox diagnosis strategy.

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