A Braking Intention Identification Method Based on Data Mining for Electric Vehicles

Time-frequency analysis is of necessity for wrist pulse signal due to its complexity, among which, empirical mode decomposition (EMD) algorithm and its improved noise-assisted versions (such as ensemble EMD, noise-assisted multivariate EMD (NA-MEMD) and very recently median EMD) are deemed to be the most representative ones. In this study, we provide an in-depth evaluation of these well-established noise-assisted EMD algorithms in computational pulse analysis for the first time. In particular, we compare the performance of the different algorithms systematically and quantitatively based on objective quantitative criteria: number and central frequency of intrinsic mode function (IMF) components, total orthogonality index and mode mixing. Rather than using synthetic signals with visual inspection in most existing literature, the wrist pulse signals used in the evaluation are real recorded samples acquired from both healthy and patient subjects. Through extensive experiments, we found that: 1) Advanced EMD algorithm that has the best performance in other areas may not be the most suitable method for pulse signal analysis, which indicates its high dependence on the type of signal to be analyzed; 2) Adding noise can significantly improve algorithm performance, but tends to produce physiologically not relevant components, which however are usually neglected throughout the intelligent pulse diagnosis literature. Therefore, excluding redundant components and then extracting features is expected to improve performance further. Together, currently NA-MEMD achieves a better performance consistently, potential to become a powerful tool for computational pulse analysis, which in itself have not been applied in wrist pulse analysis before. We believe our works can bring up a new perspective to application of EMD-like algorithms in computational pulse analysis/diagnosis with effective information and guidance. Additionally, considering the similarity between physiological signals, especially such as photoplethysmogram/electrocardiogram, our research can be extended to wearable health monitoring technologies, including smart watches and fitness trackers, and their potential future applications, such as in heart rate estimation and evaluate various cardiovascular-related diseases. The present study underscored the necessity of evaluation noise-assisted EMDs or other adaptive decomposition algorithms based on real recorded signals with more objective measures. Especially, caution the possible redundant components that are introduced.

In the present study, an adaptive Fourier Decomposition Method (FDM) is applied to identify damage at connections. For that purpose, an experimental single-story plane frame model is considered. The beam and columns of the frame are connected by angles and bolts, and the damage is introduced by loosening bolts at different locations. The frame is excited at the right top corner using a hammer, and strain time histories are collected from different points. The FDM and Empirical Mode Decomposition (EMD) algorithm are applied to characterize the damage sensitive features. The results obtained from the FDM are compared with the EMD algorithm, which shows that the performance of FDM is better, which motivates to extend its application further to estimate the amount of bolt loosening in terms of extracted damage sensitive features.

With the problem of magnetic anomaly signal in low SNR, we using empirical mode decomposition (EMD) algorithm and Savitzky-Golay filter to solve weak magnetic anomaly signal in noisy environment detection problem. With the EMD, the weak magnetic anomaly signal can be decomposed into different numbers of sub-signals called intrinsic mode functions (IMFs). The IMFs include signals different frequency component from high to low. Recover the signal with IMFs which correlation coefficient is low with noise signal and smooth the signal with Savitzky-Golay filter. The proposed method is verified by simulation on computer. The present experimental results show that this method is effective to detect the weak magnetic anomaly and recover the original signal.

Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes. The method is fully adaptive and generates a complete set of orthogonal basis functions, called Intrinsic Mode Functions (IMFs), in a purely data-driven manner. Amplitude and frequency of IMFs may vary over time which renders them different from conventional basis systems and ideally suited to study non-linear and non-stationary time series. However, biomedical time series are often recorded over long time periods. This generates the need for efficient EMD algorithms which can analyze the data in real time. No such algorithms yet exist which are robust, efficient and easy to implement. The contribution shortly reviews the technique of EMD and related algorithms and develops an on-line variant, called slidingEMD, which is shown to perform well on large scale biomedical time series recorded during neuromonitoring.

This paper proposed a method for roller bearing fault diagnosis using Empirical Mode Decomposition (EMD) algorithm and decision tree. First, to obtain the Intrinsic Mode Functions (IMFs) of bearing vibration signal processed by EMD and processing the IMFs with autocorrelation for noise elimination, then extract the principal frequency as features in frequency domain. Second, build up decision tree with C4.5 algorithm and forecast samples. Feedback the node attributes information to EMD for reducing the calculation of needless attribute and optimizing signal processing algorithm. The experimental bearing vibration signal in accordance with the following conditions: normal, inner race fault, outer race fault and balls fault. The experiment result illustrated that the correct ratio of diagnosis is high and efficient. The method proposed is high accurate and useful for safe production.

The prediction of mold level is a basic and key problem of continuous casting production control. Many current techniques fail to predict the mold level because of mold level is non-linear, non-stationary and does not have a normal distribution. A hybrid model, based on empirical mode decomposition (EMD) and support vector regression (SVR), is proposed to solve the mold level in this paper. Firstly, the EMD algorithm, with adaptive decomposition, is used to decompose the original mold level signal to many intrinsic mode functions (IMFs). Then, the SVR model optimized by genetic algorithm (GA) is used to predict the IMFs and residual sequences. Finally, the equalization of the predict results is reconstructed to obtain the predict result. Several hybrid predicting methods such as EMD and autoregressive moving average model (ARMA), EMD and SVR, wavelet transform (WT) and ARMA, WT and SVR are discussed and compared in this paper. These methods are applied to mold level prediction, the experimental results show that the proposed hybrid method based on EMD and SVR is a powerful tool for solving complex time series prediction. In view of the excellent generalization ability of the EMD, it is believed that the hybrid algorithm of EMD and SVR is the best model for mold level predict among the six methods, providing a new idea for guiding continuous casting process improvement.

Fault diagnosis of rolling bearings is still a very important and difficult research task on engineering. After analyzing the shortcomings of current bearing fault diagnosis technologies, a new approach based on Empirical Mode Decomposition (EMD) and blind equalization eigenvector algorithm (EVA) for rolling bearings fault diagnosis is proposed. In this approach, the characteristic high-frequency signal with amplitude and channel modulation of a rolling bearing with local damage is first separated from the mechanical vibration signal as an Intrinsic Mode Function (IMF) by using EMD, then the source impact vibration signal yielded by local damage is extracted by means of a EVA model and algorithm. Finally, the presented approach is used to analyze an impacting experiment and two real signals collected from rolling bearings with outer race damage or inner race damage. The results show that the EMD and EVA based approach can effectively detect rolling bearing fault.

Reservoir characterization is one of the most challenging tasks that help in modeling different lithological properties like porosity, permeability and fluid saturation using seismic readings like velocity profile, impedance, etc. Such a model is required for field development, placing new wells and prediction management. Seismic attributes are being progressively utilized for the tasks of model building, exploration and properties estimation from the data. However, these tasks become very complex due to the nonlinear and heterogeneous nature of subsurface properties. In this context, present work proposes a recurrent neural network-based learning framework to classify porosity using seismic attributes as predictor variables. The approach begins by calculating different seismic attributes from the data. From the initially calculated attribute set, features that are to be used for classification are selected by using two different strategies. Firstly, the seismic attributes having good correlation strength with reservoir porosity are extracted. Subsequently, generative topographic map is utilized to select the significant features. The final reduced features set obtained by the integrated result of above two strategies is then fed as an input to the empirical mode decomposition (EMD) algorithm. The denoised features resulting from the EMD algorithm are used to train the classification models. Further, a comparison is carried out between the proposed classification framework $$(EMD+RNN)$$ and other supervised classifiers to show the performance of the proposed framework.

In order to solve the fault location problem of single phase grounding fault in non-solidly earthed distribution network, the constitution, principle and application condition of a new fault locating method are presented in this paper. This method is realized by the distribution automation system (DAS), feeder monitoring unit has been deployed to acquire fault zero sequence current waveform and send the waveform to DAS master station. The locating workstation deployed on master station to get intrinsic mode function (IMF) component from zero sequence current waveform by using empirical mode decomposition (EMD) algorithm, then calculated similarity coefficient of IMF component by analyzing waveform similarity algorithm, finally located fault section according to similarity coefficient. The actual operation results verified that the locating method is reasonable and effective.

In order to accomplish rapid detection of the damage in mooring steel strand of the offshore platform this paper proposes to use the ultrasonic guided wave method for the online damage detection. Considering the non-stationary characteristics of the ultrasonic signal, this paper proposes an Empirical Mode Decomposition} (EMD)-based algorithm to detect and localize the position of steel strand damage. To be specific, the detect signal is firstly decomposed by EMD into multiple Intrinsic Mode Functions (IMFs). Then, in order to find the weak damage signal which is usually buried in noise, a weighted synthesis procedure is adopted, i.e., assigning more weights to the dominant components of IMFs. By doing so, the reconstruction signal is denoised and the damage signal can be much more easily captured. Simulation results show that the proposed method can effectively detect the ultrasonic guided wave signal and realize the accurate localization of the weak damage to the steel strand by comparing with the traditional method of directly selecting several natural IMFs.

Aiming at the problems of low accuracy and low robustness in current prediction methods of rolling bearing performance degradation, this paper propose a performance degradation prediction method based on empirical mode decomposition (EMD) and support vector data description (SVDD). Unlike the features extracted by wavelet analysis, the EMD algorithm has better adaptive capability and can better avoid the interference of high frequency noise. fiber Bragg grating (FBG) are used to extract the bearing vibration signal. The use of FBG sensors can better avoid the interference of electromagnetic noise, so that the vibration signal can be accurately extracted in the industrial strong magnetic environment. The Empirical Mode Decomposition is used to decompose data and extract effective intrinsic mode function (IMF) components. Six features of Intrinsic Mode Function components are extracted to form feature vectors and used as training data. Because the IMF component obtained by EMD decomposition has removed most of the noise signals, the signal features are more significant and thus effectively improve the accuracy of subsequent recognition. This paper use Support Vector Data Description for performance degradation prediction and evaluates the degradation performance of the rolling bearing by calculating the degradation value. Experiments show the proposed method can detect the performance degradation process of the rolling bearing and effectively predict the remaining service life. Due to the high recognition accuracy, the proposed method is able to identify the current state of ball bearings and predict their remaining service life.

Empirical mode decomposition (EMD) is an adaptive and data-driven approach for analyzing multicomponent nonlinear and nonstationary signals. The stop criterion, envelope technique, and mode-mixing problem are the most important topics that need to be addressed in order to improve the EMD algorithm. In this paper, we study the envelope technique and the mode-mixing problem caused by separating multicomponent AM-FM signals with the EMD algorithm. We present a new necessary condition on the envelope that questions the current assumption that the envelope passes through the extreme points of an intrinsic mode function (IMF). Then, we present a solution to the mode-mixing problem that occurs when multicomponent AM-FM signals are separated. We experiment on several signals, including simulated signals and real-life signals, to demonstrate the efficacy of the proposed method in resolving the mode-mixing problem.

A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms

In distributed generation (DG) connected grid system, there is serious harmonic pollution caused by the wide use of inverter units and nonlinear loads. The traditional detection methods (such as Fast Fourier Transform and wavelet analysis) heavily depend on synchronous signal detection or basis functions selection, which don’t satisfy the requirements of the harmonic detection of the micro-grid environment. Therefore, a novel method combining discrete wavelet transform (DWT) with Hilbert–Huang transform (HHT) is proposed to detect and analyze the harmonic. The method uses multi resolution analysis of DWT to process the original signal. Then, a series of intrinsic mode function (IMF) components are obtained from the processed signal by using Empirical Mode Decomposition (EMD) algorithm. Thirdly, the fundamental component and harmonic are extracted from the IMF components. Finally, the frequency and amplitude of the signal are calculated by using the HHT. Simulation results demonstrate that the proposed algorithm has high precision and good real-time characteristics. Furthermore, the proposed algorithm satisfies the requirements of the harmonic detection of the DG connected grid system.

This paper aims to study and evaluate the potential of Empirical Mode Decomposition (EMD) algorithm as an online frequency tracker in the presence of sine noises (harmonics). The EMD algorithm works on the principle of time scale variations between the upper maximum values and lower minimum values of a waveform, and for a long time it has been considered as a good offline analysis approach. This paper studies the online frequency tracking ability of EMD with Hilbert Transform and brings forth the limitation of the Fourier Transform, S Transform and Hilbert Transform as frequency estimators. Here a signal section between two zero crossing is subjected to EMD which segregates the oscillating modes of the signal. Further, fundamental oscillating mode is identified and subjected to Hilbert Transform for frequency estimation. The identification of the fundamental component is done by evaluating a correlation parameter between each of the obtained oscillating components and the original section of the signal. The paper also investigates the effect the sampling frequency has on the efficiency of the algorithm.