Query: ceemd

title: The Feature Extraction and Diagnosis of Rolling Bearing Based on CEEMD and LDWPSO PNN
abstract: The vibration signals of rolling bearing are often highly nonstationary and nonlinear, and consequently it is not accurate to extract and identify the characteristics of these signals by the traditional methods. In order to improve the performance on the feature extraction from bearing signals and the accuracy of the diagnosis, it requires effective signal processing and diagnose algorithms. In this paper, a new fault diagnosis algorithm which combines complementary ensemble empirical mode decomposition (CEEMD) probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithm optimized by improved linear decreasing weight (LDW) algorithm is proposed. In this method, firstly the vibration signals are decomposed into a number of Intrinsic Mode Functions (IMFs) by the CEEMD algorithm since it has good adaptive ability to nonstable signals and can effectively extract fault features. Then the improved LDWPSO algorithm is introduced to solve the problem that the selection of smoothing factor in PNN model is arbitrary and uncertain. Finally, train and diagnose the fault types of rolling bearing using the LDWPSO PNN model. The proposed method is verified by the experimental datasets. The results indicate that the method can extract the feature vectors of the vibration signals and distinguish them effectively.

title: Short term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study
abstract: To mitigate solar curtailment caused by large scale development of photovoltaic (PV) power generation, accurate forecasting of PV power generation is important. A hybrid forecasting model was constructed that combines random forest (RF) improved grey ideal value approximation (IGIVA) complementary ensemble empirical mode decomposition (CEEMD) the particle swarm optimization algorithm based on dynamic inertia factor (DIFPSO) and backpropagation neural network (BPNN) called RF CEEMD DIFPSO BPNN. PV power generation is affected by many factors. The RF method is used to calculate the importance degree and rank the factors, then eliminate the less important factors. Then, the importance degree calculated by RF is transferred as the weight values to the IGIVA model to screen the similar days of different weather types to improve the data quality of the training sets. Then, the original power sequence is decomposed into intrinsic mode functions (IMFs) at different frequencies and a residual component by CEEMD to weaken the fluctuation of the original sequence. We empirically analyzed a PV power plant to verify the effectiveness of the hybrid model, which proved that the RF CEEMD DIFPSO BPNN is a promising approach in terms of PV power generation forecasting.

title: CEEMD subset OASVR GRNN for ozone forecasting: Xiamen and Harbin as cases
abstract: Tropospheric ozone is a standard air pollutant, and can adversely affect human respiratory system. Many metropolitan areas around the world struggle to meet ozone standards. Therefore, timely and effective ozone prediction can help regulatory agencies to prevent the harm to human body and environment induced by excessive ozone concentration in advance. For selecting the optimal individual model set in combined forecasting, this research proposes CEEMD Subset OASVR GRNN model, based on complete ensemble empirical mode decomposition (CEEMD) method, support vector regression (SVR) generalized regression neural network (GRNN) and optimization algorithms (OA) to predict the daily average concentration of ozone. Specifically, for the ozone time series, CEEMD is used to decompose the original data into three intrinsic mode functions (IMFS) PSO SVR, PSOGSA SVR, GWO SVR and GRNN are employed to model and predict the IMFS, and the prediction results are randomly combined to establish 100 individual models (appendix Table A1) The selection methods of the individual models include MSE ranking, factor score and unsupervised learning systematic clustering, and the influence of number of individual models on combined forecasting is studied. The ozone series of two very distinct Chinese metropolitan areas, Xiamen and Harbin, are selected as the experimental data. The prediction results show that the systematic clustering method is helpful for effectively improving the prediction accuracy of the combined model.

title: Fault feature extraction of low speed roller bearing based on Teager energy operator and CEEMD
abstract: The fault signals of low speed rolling elements bearing are non stationary and non linear, and consequently it is difficult to extract the fault characteristics by the traditional time and frequency domains analysis methods. Furthermore, the vibration signals suffer from severe signal attenuation and noise corruption during the signal transmission process. In order to effectively enhance and extract the fault characteristics from weak bearing signal, it requires effective signal processing strategies or high sensitive sensors to detect the low energy bearing vibration signals. In this paper, one such signal processing method is proposed to detect fault characteristics combined Teager energy operator and Complementary Ensemble Empirical Mode Decomposition (CEEMD) In this method, firstly Teager energy operator is used to strengthen the signal after wavelet noise reduction since it has good temporal resolution and adaptive ability for signal transient changes, and has unique advantages in detecting signal impact characteristics. Then CEEMD algorithm is carried out to extract bearing fault through Intrinsic Mode Function (IMF) decomposition. The proposed method is validated by a scaling model test rig of a wind turbine. The results validate that the method can effectively extract the fault characteristics of low speed bearings and identify the bearing fault.

title: A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM
abstract: Deep learning is well known for extracting high level abstract features from a large amount of raw data without relying on prior knowledge, which is potentially attractive in forecasting financial time series. Long short term memory (LSTM) networks are deemed as state of the art techniques in sequence learning, which are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We propose a novel methodology of deep learning prediction, and based on this, construct a deep learning hybrid prediction model for stock markets CEEMD PCA LSTM. In this model, complementary ensemble empirical mode decomposition (CEEMD) as a sequence smoothing and decomposition module, can decompose the fluctuations or trends of different scales of time series step by step, generating a series of intrinsic mode functions (IMFs) with different characteristic scales. Then, with retaining the most of information on raw data, PCA reduces dimension of the decomposed IMFs component, eliminating the redundant information and improving prediction response speed. After that, high level abstract features are separately fed into LSTM networks to predict closing price of the next trading day for each component. Finally, synthesizing the predicted values of individual components is utilized to obtain a final predicted value. The empirical results of six representative stock indices from three types of markets indicate that our proposed model outperforms benchmark models in terms of predictive accuracy, i.e. lower test error and higher directional symmetry. Leveraging key research findings, we perform trading simulations to validate that the proposed model outperforms benchmark models in both absolute profitability performance and risk adjusted profitability performance. Furthermore, model robustness test unveils the more stable robustness compared to benchmark models.

title: A Noise Attenuation Method for Weak Seismic Signals Based on Compressed Sensing and CEEMD
abstract: The exploration of deep and subtle oil and gas reservoirs is currently an important means of increasing production in older oil fields. How to effectively identify weak signals with noise is a common problem faced in such reservoirs. Especially for deep seismic reflection data, the application of traditional denoising methods is limited due to the weak energy and small difference frequency band between the effective signals and the noise. A novel method of noise attenuation for weak seismic signals based on compressed sensing and CEEMD (complementary ensemble empirical mode decomposition) was proposed in this work. This method consists of three steps: First, the CEEMD algorithm, a time frequency analysis method, was introduced into the classic CS (compressed sensing) denoising method to overcome the non adaptability of CS. CEEMD decomposed the raw seismic signals into sets of IMFs (finite intrinsic mode functions) The IMFs with noise were reconstructed and denoised by CS. In the second step, an enhancement operator was introduced into the penalty term to ensure that the effective signals could be extracted. Finally, the OMP algorithm is adopted to reconstruct the seismic weak signal to prevent the iterative threshold method from damaging weak effective signals. It was demonstrated through the synthetic seismic record and the field seismic data that (a) the CS method can identify the weak signals submerged in the noise by selecting the basic function that is most similar to the effective signals, but it cannot adaptively suppress the high frequency and high wavenumber noise of the complex seismic records; (b) the proposed method overcome the non adaptability of CS and enhance the edge information described by the curved wave rather than the noises, as a result, the high frequency noise is suppressed while the middle/low frequency noise and weak effective signals are also effectively separated.

title: Research on CEEMD AGA Denoising Method and Its Application in Feed Mixer
abstract: Rotating shaft is the key part of rotating machinery, which directly affects the performance of the whole machine. Field test is an easy and quick way to obtain the load data in engineering practice. However, because of various reasons, the load data are often mixed with many noise components. Based on the autocorrelation function, the CEEMD (complementary ensemble empirical mode decomposition) denoising method is proposed in this paper. The AGA (adaptive genetic algorithm) is adopted to solve parameter optimization problems in CEEMD. A new similarity function is proposed as the fitness function. Lastly, the proposed denoising method is applied to a feed mixer's load which is obtained by field test. The result shows that the CEEMD AGA method has good robustness, noise components of small stress amplitude and large stress mean are removed, and there is a high correlation between the original data and the reconstructed data, which demonstrate that the CEEMD AGA method can reduce the influence of noise components effectively.

title: A Hybrid Landslide Displacement Prediction Method Based on CEEMD and DTW ACO SVR Cases Studied in the Three Gorges Reservoir Area
abstract: Accurately predicting the surface displacement of the landslide is important and necessary. However, most of the existing research has ignored the frequency component of inducing factors and how it affects the landslide deformation. Therefore, a hybrid displacement prediction model based on time series theory and various intelligent algorithms was proposed in this paper to study the effect of frequency components. Firstly, the monitoring displacement of landslide from the Three Gorges Reservoir area (TGRA) was decomposed into the trend and periodic components by complete ensemble empirical mode decomposition (CEEMD) The trend component can be predicted by the least square method. Then, time series of inducing factors like rainfall and reservoir level was reconstructed into high frequency components and low frequency components with CEEMD and t test, respectively. The dominant factors were selected by the method of dynamic time warping (DTW) from the frequency components and other common factors (e.g. current monthly rainfall) Finally, the ant colony optimization based support vector machine regression (ACO SVR) is utilized for prediction purposes in the TGRA. The results demonstrate that after considering the frequency components of landslide induced factors, the accuracy of the displacement prediction model based on ACO SVR is better than that of other models based on SVR and GA SVR.

title: CEEMD assisted kernel support vector machines for bearing diagnosis
abstract: The successful assessment of the health condition in rolling element bearings hinges on the early fault detection of fault of bearing elements. Early research has demonstrated that interactions of various components within the mechanical system and added noise from data acquisition system result in the unclear trend of the vibration signal from bearings. The unclear trend could lead to inaccurate assessment of the machine condition, which could result in profit loss and catastrophic failure. Therefore, obtaining the machine condition under heavy noises in the early degradation stage is critical to ensure smooth operation and maximize productivity. In the past, various signal processing techniques are implemented in bearing diagnosis to improve the signal to noise ratio and monitor frequency bands that are associated with bearing fault. The diagnostic models generally involve parameter tuning and feature selections. The tuning of the parameters of the diagnostic model could be tedious and require understanding of the characteristics of the acquired signal. Besides the generally selected signal features such as root mean square, kurtosis, and skewness, the selection of other features from signals does not provide much insights into the degradation of bearings. Features are mixed in a way that a few of the features could have negative impact on the assessment of degradation. To address the abovementioned issue, we present an innovative diagnosis model using the complementary ensemble empirical mode decomposition (CEEMD) with kernel support vector machines (kernel SVM) to evaluate the health condition of bearings in terms of defect severity. To the best of our knowledge, the combination of the two methods has not been implemented in documented research work. The novelty of this work is the optimization of the CEEMD parameter using the bootstrap resampling method while combining the kernel SVM to characterize the fault size of the bearings. The acquired signal is processed using the complementary ensemble empirical mode decomposition method to remove undesirable noises and extract the signal containing the fault signature. The signals are then examined, segmented, and classified based on the degradation stage of bearings. The kernel SVM is implemented to learn the correct classification and predict the corresponding bearing degradation stage based on the simple root mean square value instead of using various features. The model has a prediction accuracy of about 98% and is easy to implement because of less parameters involved. In comparison with the traditional SVM and kernel SVM, the proposed model significantly improved the accuracy of prediction. Because of its high prediction accuracy and easy implementation, the proposed model can be implemented into machine condition monitoring on CNC production machines and various types of rotating machineries.

title: Short term wind speed prediction based on CEEMD decomposition and optimization BP model
abstract: As one of the important factors of wind power generation, wind speed directly affects the stability and safety of wind power system. Wind speed has the characteristics of non linear and nonstationary series, so how to predict wind speed accurately is an outstanding problem. In this paper, a method based on CEEMD and GA BP is used to forecast the wind speed of wind farm in short term. According to the characteristics that wind speed is a nonlinear and non stationary sequence, firstly, the wind speed sequence is decomposed by CEEMD to obtain a subsequence (IMF) which is more stable than the original sequence. Then, each subsequence of the original sequence is modeled and predicted one by one by using BP model. Finally, the prediction results of all subsequences are added to obtain the final wind speed prediction value. Because the number of hidden nodes and learning rate of BP model will affect the modeling efficiency and prediction accuracy of BP model. GA algorithm is used to optimize these two parameters, and the best parameters are iterated according to the minimum MSE to improve the prediction accuracy. The simulation results show that, compared with the short term wind speed prediction results directly using BP model and CEEMD BP model, the method adopted in this paper has higher prediction accuracy, which is conducive to the safe and stable operation of power system.