An Improved VMD-Based Denoising Method for Time Domain Load Signal Combining Wavelet with Singular Spectrum Analysis

Fu, Jingjing; Cai, Fuyou; Guo, Yonghao; Liu, Hongda; Niu, Wentie

doi:10.1155/2020/1485937

Cited by 17 publications

(10 citation statements)

References 40 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e method described in this paper uses the amplitude of the frequency response function feature vector. erefore, the increase of this energy component would have a certain impact on load identification [26][27][28]. In summary, within a certain range of motion speed, the load identification method has proved to have a high identification accuracy in this paper.…”

Section: Resultsmentioning

confidence: 70%

Single Pick Cutting Rock Load Identification Based on Improved Regularization Method

Li¹,

Zhai²,

Wang³

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

To explore the relationship between the cutting vibration and the cutting load of a single pick, this paper studied a new method for a single pick cutting rock load identification. This paper improved the low accuracy problem of the regularization method in the inverse process of frequency response function in the traditional load identification method by introducing a filter operator. By combining the inverse pseudoexcitation method and the improved regularization method, the identification of the load dependent on the vibration signal was realized. A single pick cutting rock test equipment was built, which could simulate the actual working conditions of pick cutting rock in the underground or tunnel. By changing cutting speed, cutting angle, cutting spacing, and cutting depth of the single pick, the change trends of real cutting load and identification load were obtained. The load identification method proposed in this paper was consistent with the change trend of the real load under the single pick cutting state. Therefore, the method had good recognition accuracy and the maximum load recognition error was 17.35%. Compared with other traditional load identification methods, the identification error was reduced by a maximum of 1.98%. This method can identify the cutting load of single pick and modify the morbidity problem of frequency response function matrix. The method has a better recognition effect on the cutting load of the pick than the traditional recognition methods. The research could benefit the design of the cutting system and the arrangement of the pick on the coal mine or tunneling machinery.

show abstract

Section: Resultsmentioning

confidence: 70%

Single Pick Cutting Rock Load Identification Based on Improved Regularization Method

Li¹,

Zhai²,

Wang³

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…To validate the applicability of the load identification method, the data in this paper is not Fu et al 2020;Maurya et al 2020). In summary, within a certain range of motion speed, the load identification method in this paper has a high identification accuracy.…”

Section: Resultsmentioning

confidence: 98%

Single Pick Cutting Rock Load Identification Based on Improved Regularization Method

Dong

Zhai

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

To explore the relationship between the cutting vibration and the cutting load of a single pick, this paper studied a new method for a single pick cutting rock load identification. This paper improved the low accuracy problem of the regularization method in the inverse process of frequency response function in the traditional load identification method by introducing a filter operator. By combining the inverse pseudo excitation method and the improved regularization method, the identification of the load dependent on the vibration signal was realized. A single pick cutting rock test equipment was built, which could simulate the actual working conditions of pick cutting rock in underground or tunnel. By changing cutting speed, cutting angle, cutting line spacing and cutting depth of the single pick, the change trends of real cutting load and identification load were obtained. The load identification method proposed in this paper was consistent with the change trend of the real load under the single pick cutting state. Therefore, the method had good recognition accuracy and the maximum load recognition error was 17.35%. Compared with the traditional load identification method, the identification error was reduced by a maximum of 1.98%. This method can identify the cutting load of single pick and modify the morbidity problem of frequency response function matrix. The method has a better recognition effect on the cutting load of the pick than the traditional recognition method. The research could benefit for the design of the cutting system and the arrangement of the pick on the coal mine or tunneling machinery.

show abstract

“…There are many data denoising methods, and many studies have also examined various data denoising methods [48], among which wavelet threshold denoising is widely used [49]. The signal containing noise is decomposed by wavelet.…”

Section: Denoising and Normalization Of Input Datamentioning

confidence: 99%

An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Liu

Feng

et al. 2021

Symmetry

View full text Add to dashboard Cite

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

show abstract

An Improved VMD-Based Denoising Method for Time Domain Load Signal Combining Wavelet with Singular Spectrum Analysis

Cited by 17 publications

References 40 publications

Single Pick Cutting Rock Load Identification Based on Improved Regularization Method

Single Pick Cutting Rock Load Identification Based on Improved Regularization Method

Single Pick Cutting Rock Load Identification Based on Improved Regularization Method

An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Contact Info

Product

Resources

About