scholarly journals Characteristics of temperature evolution from 1960 to 2015 in the Three Rivers’ Headstream Region, Qinghai, China

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiaoqiong Liu ◽  
Yuyang Zhang ◽  
Yansui Liu ◽  
Xinzheng Zhao ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Regional Differences ◽  
Ensemble Empirical Mode Decomposition ◽  
Temperature Series ◽  
Statistical Characteristics ◽  
Temperature Changes ◽  
Mean Temperature ◽  
Mode Decomposition ◽  
Anomaly Analysis ◽  
The 1960S ◽  
Three Rivers

AbstractThe cumulative anomaly analysis, the ensemble empirical mode decomposition (EEMD), the Bernaola Galvan heuristic segmentation algorithm (BGSA), the Le Page test, the moving t test at different sub-series scales, and the quasi-periodic oscillations (QPOs) were used to demonstrate the statistical characteristics of the temperature changes in the study area from 1960 to 2015. The results were as follows: the temperatures varied obviously among subregions and seasons and they generally increased; the climate tendency rates of autumn mean temperatures were higher than those of summer and spring; additionally, the temperatures in the three subregions of the Three Rivers’ Headstream Region (THRHR) were relatively low in the 1960s, especially in the early 1960s, followed by those in the 1970s, and the annual mean temperature has been increasing since the mid-late 1980s, especially in the middle 1990s. The results of EEMD showed that the QPOs of the annual mean temperature series in the study area were mainly quasi-3 years, quasi-5–8 years, quasi-12–15 years, and quasi-35–38 years. The results of the annual mean temperature series mutational sites showed that a significant warming mutation began in approximately 1997; and the mutational sites of seasonal mean temperature series in the three subregions of the THRHR all began in the middle and late 1990s. The prediction result of the temperature series trend based on multiple methods showed that the warming persistence of annual and seasonal mean temperature series would be stronger, and their seasonal and regional differences were obvious.

Identification of spin maneuver aerodynamic nonlinear model by applying ensemble empirical mode decomposition and extended multipoint modeling

2018 ◽  
Vol 233 (5) ◽  
pp. 1865-1878 ◽  
Author(s):  
MA Mokhtari ◽  
M Sabzehparvar
Keyword(s):  
Empirical Mode Decomposition ◽  
Center Of Mass ◽  
Ensemble Empirical Mode Decomposition ◽  
Statistical Characteristics ◽  
Aerodynamic Forces ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Mode Decomposition ◽  
Mixing Problem ◽  
Error Method

Identification of the “aircraft aerodynamic model” in some unusual flight conditions such as spin maneuver provides critical information to the flight controller to retake the “dynamic stability” after it has been disturbed by the systematic, natural or environmental oscillations. Hence, a method for identifying the appropriate aerodynamic model in spin maneuvers is presented in this paper. We present an innovative systematic method for aerodynamic modeling of spin maneuvers, which combines the ensemble empirical mode decomposition technique and extended multipoint modeling approach, using flight data. In ensemble empirical mode decomposition, in addition to having all the empirical mode decomposition features, the original signal is collected with the white noise, and by using its statistical characteristics, the ensemble empirical mode decomposition solves the mode mixing problem. By applying the ensemble empirical mode decomposition to the flight parameter data, their intrinsic mode frequencies are extracted and are used as inputs to the extended multipoint modeling model. The extended multipoint modeling structure includes some parameters describing the distribution of aerodynamic forces and moments along each of the aircraft components. Moreover, this method allows coupling between the forces and moments. Unlike conventional methods, which consider the average forces obtained by plane surfaces relative to the center of mass, in the extended multipoint modeling technique, the force generated by each plane of the aircraft is allowed to appear independently in the motion equations. For identifying the aerodynamic model with extended multipoint modeling structure, the equation error method is used with a maximum likelihood optimizer inside. The obtained algorithm has been applied to two sets of spin maneuver flight data which were recorded in actual spin flight. The results demonstrate that the proposed method is able to reproduce the aerodynamic forces and moments for the second spin flight inputs with high accuracy by using a model which is derived from the first spin data identification.

scholarly journals Ensemble Empirical Mode Decomposition: Image Data Analysis with White-noise Reflection

10.14311/1291 ◽  
2010 ◽  
Vol 50 (6) ◽  
Author(s):  
M. Kopecký
Keyword(s):  
Data Analysis ◽  
White Noise ◽  
Empirical Mode Decomposition ◽  
Image Data ◽  
Original Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Statistical Characteristics ◽  
Time Frequency ◽  
Time Space ◽  
Mode Decomposition

During the last decade, Zhaohua Wu and Norden E. Huang announced a new improvement of the original Empirical Mode Decomposition method (EMD). Ensemble Empirical Mode Decomposition and its abbreviation EEMD represents a major improvement with great versatility and robustness in noisy data filtering. EEMD consists of sifting and making an ensemble of a white noise-added signal, and treats the mean value as the final true result. This is due to the use of a finite, not infinitesimal, amplitude of white noise which forces the ensemble to exhaust all possible solutions in the sifting process. These steps collate signals of different scale in a proper intrinsic mode function (IMF) dictated by the dyadic filter bank. As EEMD is a time–space analysis method, the added white noise is averaged out with a sufficient number of trials. Here, the only persistent part that survives the averaging process is the signal component (original data), which is then treated as the true and more physically meaningful answer. The main purpose of adding white noise was to provide a uniform reference frame in the time–frequency space. The added noise collates the portion of the signal of comparable scale in a single IMF. Image data taken as time series is a non-stationary and nonlinear process to which the new proposed EEMD method can be fitted out. This paper reviews the new approach of using EEMD and demonstrates its use on the example of image data analysis, making use of some advantages of the statistical characteristics of white noise. This approach helps to deal with omnipresent noise.

scholarly journals Analysis method of the cavitation vibration signals in poppet valve based on EEMD

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199811
Author(s):  
Beibei Li ◽  
Qiao Zhao ◽  
Huaiyi Li ◽  
Xiumei Liu ◽  
Jichao Ma ◽  
...  
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Center Frequency ◽  
Analysis Method ◽  
Vibration Signals ◽  
Poppet Valve ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Cavitation Intensity ◽  
Eemd Method ◽  
Peak Value

To study the vibration characteristics of the poppet valve induced by cavitation, the signal analysis method based on the ensemble empirical mode decomposition (EEMD) method was studied experimentally. The component induced by cavitation was separated from the vibration signals through the EEMD method. The results show that the IMF2 component has the largest amplitude and energy of all components. The root mean square (RMS) value, peak value of marginal spectrum, and center frequency of marginal spectrum of the IMF2 component were studied in detail. The RMS value and the peak value of the marginal spectrum decrease with a decrease of cavitation intensity. The center frequency of marginal spectrum is between 12 kHz and 20 kHz, and the center frequency first increases and then decreases with a decrease of cavitation intensity. The change rate of the center frequency also decreases with an increase of inlet pressure.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2599
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Yu Sun ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Pump ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

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.

scholarly journals Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 460-477
Author(s):  
Sajjad Khan ◽  
Shahzad Aslam ◽  
Iqra Mustafa ◽  
Sheraz Aslam
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Electricity Market ◽  
Ensemble Empirical Mode Decomposition ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Mode Decomposition ◽  
Electricity Price Forecasting ◽  
Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

Sign in / Sign up

Export Citation Format

Share Document