A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis

Li, Changpeng; Peng, Tianhao; Zhu, Yanmin

doi:10.3390/s20102949

Cited by 17 publications

(5 citation statements)

References 43 publications

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“…In the traditional algorithm of VMD, the user needs to set the penalty parameter β and the number of the components K before processing the signal because of the theory limitation [ 33 , 34 , 35 ]. The improper selection of the two parameters will result in some unacceptable mode compositions.…”

Section: Related Algorithmmentioning

confidence: 99%

A Fault Diagnosis Scheme Using Hurst Exponent for Metal Particle Faults in GIL/GIS

Duan

Yan

et al. 2022

Sensors

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A diagnosis scheme using the Hurst exponent for metal particle faults in GIL/GIS is proposed to improve the accuracy of classification and identification. First, the diagnosis source signal is the vibration signal generated by the collision of metal particles in the electric field. Then, the signal is processed via variational mode decomposition (VMD) based on particle swarm optimization with adaptive parameter adjustment (APA-PSO). In the end, fault types are classified and identified by an SVM model, whose feature vector is composed of the Hurst exponents of each intrinsic mode function (IMF-H). Extensive experimental data verify the effect of this new scheme. The results exhibit that the classification performance of SVM is significantly improved by the new feature vector. Furthermore, the VMD based on APA-PSO with adaptive parameter adjustment can effectively enhance the decomposition quality.

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Section: Related Algorithmmentioning

confidence: 99%

A Fault Diagnosis Scheme Using Hurst Exponent for Metal Particle Faults in GIL/GIS

Duan

Yan

et al. 2022

Sensors

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“…During the system detection process, the syringe pump pushes the reaction solution into the flow cell for detection, and a light source with a certain wavelength is used to illuminate the liquid column in the flow cell. The transmitted light hits the silicon photocell to generate photocurrent, and the weak photocurrent is amplified by the transimpedance amplifier and converted to I/V signal, and then output to the ADS1115 for acquisition and analog-to-digital conversion [6][7] . Collect the transmitted light of a series of concentration solutions and calculate the absorbance, and calculate the standard working curve through the absorbance and concentration values of different concentration solutions.…”

Section: Overall System Designmentioning

confidence: 99%

Design of Online Detection System of Boiler Water Quality Based on STM32

Wei

Shi

et al. 2022

J. Phys.: Conf. Ser.

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Industrial boilers have strict requirements for water quality. It is necessary to adjust water treatment measures and regular blowdown according to the monitoring of phosphate and soluble silica content in boiler water to avoid boiler scaling, corrosion, and deterioration of steam quality, and prevent major accidents. The traditional boiler water quality detection technology has a strong lag and long detection time, which cannot meet real-time online detection. Aiming at the requirement of detection of phosphate radical and soluble silica content in boiler water quality, a portable and low-cost online detection system of boiler water quality based on STM32 was studied. The pump valve flow path is constructed with a syringe pump equipped with a syringe and a distribution valve, LED and silicon photocell are used for the light source and detector of the system respectively, and the photoelectric detection part is composed of a designed Z-shaped flow cell, which reduces the experimental cost; Design a data acquisition circuit based on STM32 microcontroller and a 16-bit high-precision analogy-to-digital converter, as well as a constant-current drive circuit for driving LED light sources, to improve the stability and high precision of the automatic measuring instrument. The system test results show that the boiler water quality online detection system based on STM32 can accurately detect the phosphate and soluble silica concentrations in boiler water quality.

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“…The effectiveness of VMD decomposition is mainly influenced by two factors, namely, the number of mode components (K) and the number of penalty factors (α) [22]. Similarly, the performance of the LSTNet model is significantly affected by its parameter values [23].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series

Wang,

Ye,

et al. 2023

Sustainability

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Accurately predicting the remaining useful life (RUL) of lithium-ion batteries holds significant importance for their health management. Due to the capacity regeneration phenomenon and random interference during the operation of lithium-ion batteries, a single model may exhibit poor prediction accuracy and generalization performance under a single scale signal. This paper proposes a method for predicting the RUL of lithium-ion batteries. The method is based on the improved sparrow search algorithm (ISSA), which optimizes the variational mode decomposition (VMD) and long- and short-term time-series network (LSTNet). First, this study utilized the ISSA-optimized VMD method to decompose the capacity degradation sequence of lithium-ion batteries, acquiring global degradation trend components and local capacity recovery components, then the ISSA–LSTNet–Attention model and ISSA–LSTNet–Skip model were employed to predict the trend component and capacity recovery component, respectively. Finally, the prediction results of these different models were integrated to accurately estimate the RUL of lithium-ion batteries. The proposed model was tested on two public lithium-ion battery datasets; the results indicate a root mean square error (RMSE) under 2%, a mean absolute error (MAE) under 1.5%, and an absolute correlation coefficient (R2) and Nash–Sutcliffe efficiency index (NSE) both above 92.9%, implying high prediction accuracy and superior performance compared to other models. Moreover, the model significantly reduces the complexity of the series.

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A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis

Cited by 17 publications

References 43 publications

A Fault Diagnosis Scheme Using Hurst Exponent for Metal Particle Faults in GIL/GIS

A Fault Diagnosis Scheme Using Hurst Exponent for Metal Particle Faults in GIL/GIS

Design of Online Detection System of Boiler Water Quality Based on STM32

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series

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