scholarly journals Crow Search Algorithm for MEMS Gyroscope Temperature Drift Signal and Processing for Denoising

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xichen Wang ◽  
Huiliang Cao ◽  
Xiaomin Duan
Keyword(s):  
Search Algorithm ◽  
Noise Signal ◽  
Sample Entropy ◽  
Least Square ◽  
Effect Of Temperature ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Temperature Drift ◽  
Mems Gyroscope ◽  
Mode Decomposition

To solve the problem of micro-electro-mechanical system (MEMS) gyroscope noise, this paper presents a variational mode decomposition (VMD) method based on crow search algorithm. First, the signal was decomposed by variational mode decomposition for optimization of crow search algorithm (CSA-VMD) method. The parameters required by the VMD method (penalty parameter α and decomposition number K) are given by the crow search algorithm, and then the signal is decomposed into the superposition of multiple subsignals, called intrinsic mode functions (IMFs). The sample entropy (SE) corresponding to each IMF is then obtained. By calculating the sample entropy, the noise signal can be divided into pure noise part, mixing part, and temperature drift part. Second, Savitzky–Golay smoothing denoising (SG) is used to filter the mixed noise signal to eliminate the influence of noise. Third, for the filtering of the drift part, the least square support vector machine optimized by the crow search algorithm (CSA-LSSVM) was used to filter, so as to reduce the effect of temperature drift. Finally, the processed signal is reconstructed to achieve the goal of denoising. Through the results, it can be found that the optimized VMD and LSSVM using CSA algorithm can achieve more effective denoising. After using the method proposed in this paper, the angular random walk value is 1.1175    ∗  10−4°/h/√Hz, and the bias stability is 0.0017°/h. Compared with the original signal, the two signals are optimized by 98.1% and 98.2%, respectively. It can be seen from the experimental results that the proposed CSA-VMD method, SG method, and CSA-LSSVM method can effectively eliminate noise effects.

scholarly journals A Novel Shearer Cutting State Recognition Method Based on Improved Variational Mode Decomposition and LSSVM with Acoustic Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Zhongbin Wang ◽  
Bin Liang ◽  
Lei Si ◽  
Kuangwei Tong ◽  
Chao Tan
Keyword(s):  
Search Algorithm ◽  
Optimal Parameter ◽  
Gravitational Search Algorithm ◽  
Principal Component ◽  
Least Square ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Recognition Method ◽  
Mode Decomposition

The recognition of shearer cutting state is the key technology to realize the intelligent control of the shearer, which has become a highly difficult subject concerned by the world. This paper takes the sound signal as analytic objects and proposes a novel recognition method based on the combination of variational mode decomposition (VMD), principal component analysis method (PCA), and least square support vector machine (LSSVM). VMD can decompose a signal into various modes by using calculus of variation and effectively avoid the false component and mode mixing problems. On this basis, an improved gravitational search algorithm (IGSA) is designed by using the position update mechanism of Levy flight strategy to find the optimal parameter combination of VMD. Then, the feature extraction is achieved by calculating the envelope entropy and kurtosis of the decomposed intrinsic mode functions (IMFs). To avoid dimensional disasters and reinforce the classification performance, PCA is introduced to choose useful features, and the LSSVM-based classifier is reasonably constructed. Finally, the experimental results indicate that the proposed method is more feasible and superior in the recognition of shearer cutting states.

scholarly journals Research on Novel Denoising Method of Variational Mode Decomposition in MEMS Gyroscope

2021 ◽  
Vol 21 (1) ◽  
pp. 19-24
Author(s):  
Xiaolei Wang ◽  
Huiliang Cao ◽  
Yuzhao Jiao ◽  
Taishan Lou ◽  
Guoqiang Ding ◽  
...  
Keyword(s):  
Resonant Frequency ◽  
Reduction Method ◽  
Noise Signal ◽  
Sampling Frequency ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Mems Gyroscope ◽  
Frequency Level ◽  
Mode Decomposition ◽  
Four Levels

Abstract The noise signal in the gyroscope is divided into four levels: sampling frequency level, device bandwidth frequency level, resonant frequency level, and carrier frequency level. In this paper, the signal in the dual-mass MEMS gyroscope is analyzed. Based on the variational mode decomposition (VMD) algorithm, a novel dual-mass MEMS gyroscope noise reduction method is proposed. The VMD method with different four-level center frequencies is used to process the original output signal of the MEMS gyroscope, and the results are analyzed by the Allan analysis of variance, which shows that the ARW of the gyroscope is increased from 1.998*10−1°/√h to 1.552*10−4°/√h, BS increased from 2.5261°/h to 0.0093°/h.

scholarly journals Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5297
Author(s):  
Jie Lv ◽  
Wenlei Sun ◽  
Hongwei Wang ◽  
Fan Zhang
Keyword(s):  
Fault Diagnosis ◽  
Search Algorithm ◽  
Decomposition Methods ◽  
Sample Entropy ◽  
Signal Decomposition ◽  
Support Vector ◽  
Optimal Parameters ◽  
Rolling Bearings ◽  
Signal Features ◽  
Mode Decomposition

We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD–RCMDE–SSA–SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

scholarly journals A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm

2018 ◽  
Vol 8 (10) ◽  
pp. 1754 ◽  
Author(s):  
Tongxiang Liu ◽  
Shenzhong Liu ◽  
Jiani Heng ◽  
Yuyang Gao
Keyword(s):  
Wind Speed ◽  
Search Algorithm ◽  
Wind Farms ◽  
Cuckoo Search ◽  
Ensemble Empirical Mode Decomposition ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Wind Speed Forecasting ◽  
Mode Decomposition ◽  
Proposed Model

Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)—whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)—is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants.

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