Fault Location Detection Using CEEMD and Two-Terminal Traveling Wave Ranging

The article proposes to combine a methods based on wavelet denoising (WDN) and Shannon entropy (DSE) for GNSS station position time series denoising and Complementary Ensemble Empirical Mode Decomposition (CEEMD) for the decomposition of the time series into frequency components, namely intrinsic mode functions (IMFs). First, we used WDN as well as DSE to denoise the GNSS time series. They were then decomposed with CEEMD, which is dedicated to analysing non-stationary time series. The proposed WDN + CEEMD and DSE + CEEMD methods were then employed to analyse several GNSS station position time series in Poland. We used daily time series of position residues for 15 GNSS stations of the EUREF Permanent Network (EPN) classified as the Polish national control network. The station time series were decomposed into IMF frequency components, of which IMF5 and IMF6 represented semi-annual and annual signals. We noted an annual oscillation for all the reference stations in the horizontal and vertical components. A semi-annual oscillation was found for all the stations only in the vertical component. The study confirms that the WDN + CEEMD as well as DSE + CEEMD method is capable of limiting the absorption of some noise by seasonal signals. The values of the spectral indices of the station position time series after subtracting the seasonal signals modelled by the WDN + CEEMD or DSE + CEEMD methods assumed values from the range of the power law noise model. GNSS station position time series analysis with WDN + CEEMD and DSE + CEEMD yielded satisfactory results and can be a good alternative for modelling time-dependent seasonal signals in GNSS time series, particularly the annual and semi-annual signals.

An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis

As a direct machining tool, the tool will inevitably wear out during production and processing. In order to grasp the wear state of cutting tools accurately and realize the accurate diagnosis in the cutting process, the CEMMD-WPT feature extraction method is proposed, which is based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Wavelet Package Transform (WPT). Firstly, the CEEMD is used to decompose the Acoustic Emission (AE) signal that acquired by cutting tool. The AE signal is adaptively decomposed into several Intrinsic Mode Functions (IMFs) among with each IMF contains different time scale characteristic. Then, for less IMFs that still have mode mixing, is corrected with good local processing ability by WPT. The CEEMD-WPT combination algorithm not only can effectively solve the problem of the mode mixing after CEEMD, but also eliminate the influence of frequency mixing and illusive component after WPT treatment. Finally, this work select the first few IMFs component with large energy values, calculate the proportion of the total energy as feature vectors, and input them into the Support Vector Machine (SVM) for training and testing, to establish the tool state recognition system. Compared with CEEMD feature extraction method, the feature extracted by CEEMD-WPT method is more accurate and more representative, which lays a good foundation for later recognition.

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

In the paper, a quantified self-adaptive signal-filtering method called effective intrinsic mode functions (IMFs) selection based on complementary ensemble empirical mode decomposition (CEEMD) is proposed. In the method, a combination of the self-adaptive separation of IMFs and the correlation analysis is presented. Generating IMFs by CEEMD are divided automatically into the noisy domain and the signal domain through the quantified correlation coefficient estimation. In order to choose effective IMFs, low-frequency priority and energy priority are used in the noisy domain and the signal domain respectively. A correlation threshold value is also quantitatively calculated by mutual information (MI) between adjacent IMFs. The threshold can filter out accurately IMFs that contain a lot of noises. The final reconstructed signal will be gotten via the partial reconstruction of effective IMFs selected in different domains. All parameters in the proposed filtering method are self-adaptively obtained without a priori knowledge. This method is a fully data-driven approach. Test results of simulation and real signals show the validity of the proposed method and demonstrate its superior performance compared with the other methods of references. In addition, the application of the proposed method and comparison experiment with CEEMDAN are also investigated. The study is limited to signals that were corrupted by additive white Gaussian noise.

Summary In seismic exploration, the first break picking is a fundamental step for seismic data processing. Various theories and methods have been developed by geophysicists in past years. However, each method has its own inherent limitations and cannot always provide an accurate result. So, new theories and methods should be tried and applied. In this paper, we propose a new method for automatic picking of the first break by the complementary ensemble empirical mode decomposition (CEEMD). CEEMD is an effective analysis technology for non-stationary signal. And the seismic signal is a typical non-stationary signal, so it can be divided into a set of intrinsic mode function (IMF) by CEEMD completely. Then, enhance the characteristics of first break on some IMFs with threshold. Finally, recompose the signal and pick the first break. Taking the huge data into account, we use an optimization algorithm to achieve the adaptive picking for every trace. The synthetic signal and field data examples show that the picking method we proposed can achieve convergent and reliable results automatically.

Modified complementary ensemble empirical mode decomposition and intrinsic mode functions evaluation index for high-speed train gearbox fault diagnosis

BackgroundAuditory steady-state response (ASSR) induced by repetitive auditory stimulus is commonly used for audiometric testing. ASSR can be measured using electro-encephalography (EEG) and magnetoencephalography (MEG), referred to as steady-state auditory evoked potential (SSAEP) and steady-state auditory evoked field (SSAEF), respectively. However, the signal level of SSAEP and SSAEF are weak so that signal processing technique is required to increase its signal-to-noise ratio. In this study, a complementary ensemble empirical mode decomposition (CEEMD)-based approach is proposed in MEG study and the extraction of SSAEF has been demonstrated in normal subjects and tinnitus patients.MethodsThe CEEMD utilizes noise assisted data analysis (NADA) approach by adding positive and negative noise to decompose MEG signals into complementary intrinsic mode functions (IMF). Ten subjects (five normal and five tinnitus patients) were studied. The auditory stimulus was designed as 1 kHz carrier frequency with 37 Hz modulation frequency. Two channels in the vicinities of right and left temporal areas were chosen as channel-of-interests (COI) and decomposed into IMFs. The spatial distribution of each IMF was correlated with a pair of left- and right-hemisphere spatial templates, designed from each subject’s N100m responses in pure-tone auditory stimulation. IMFs with spatial distributions highly correlated with spatial templates were identified using K-means and those SSAEF-related IMFs were used to reconstruct noise-suppressed SSAEFs.ResultsThe current strengths estimated from CEEMD processed SSAEF showed neural activities greater or comparable to those processed by conventional filtering method. Both the normal and tinnitus groups showed the phenomenon of right-hemisphere dominance. The mean current strengths of auditory-induced neural activities in tinnitus group were larger than the normal group.ConclusionsThe present study proposes an effective method for SSAEF extraction. The enhanced SSAEF in tinnitus group echoes the decreased inhibition in tinnitus’s central auditory structures as reported in previous studies.

With the continuous development of the application of satellite positioning and navigation, the demand for high precision positioning is constantly enhanced. The multipath effect is a major error source of the Global Navigation Satellite Systems (GNSS). The measurement errors of the pseudo range and carrier phase caused by the multipath signal affect the positioning accuracy of the receiver seriously. Aiming at multipath suppression, a new method based on the Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Hilbert Transform (HT) is proposed in this paper. In the first step, dynamic real-time multipath model was established. According to the characteristics of the received signal, the dynamic real-time multipath model accords with the two conditions that must be met in the CEEMD algorithm. In the second step, the received signal is decomposed to obtain the Intrinsic Mode Functions (IMF) by CEEMD which has the advantage of improving the prediction accuracy greatly. After that, each IMF is transformed by HT which can automatically complete multiple test of channel delay and calculate the uncertainty of measurement through high speed oscilloscope. In order to determine the exact location of the turning point, the system processes the signal by using the envelope detection method based on Hilbert Transform. Then, the channel delay of the simulator is finally obtained since that the instantaneous frequency, phase and amplitude of each multipath signal can be detected by using the CEEMD-HT algorithm. Finally, the tracking loop is improved by choosing the appropriate IMF as the direct signal. The preliminary simulation results obtained by MATLAB demonstrate that this method can strengthen the performance of GNSS multipath real-time suppression effectively. On the one hand, the new method in this paper can achieve the purpose of real-time suppression of multipath effects. On the other hand, it is capable to raise the channel delay measurement accuracy. Compared with the traditional method, the positioning accuracy is improved by 38.59%.

Dynamic regulation of cerebral circulation involves complex interaction between cardiovascular, respiratory, and autonomic nervous systems. Evaluating cerebral hemodynamics by using traditional statistic- and linear-based methods would underestimate or miss important information. Complementary ensemble empirical mode decomposition (CEEMD) has great capability of adaptive feature extraction from non-linear and non-stationary data without distortion. This study applied CEEMD for assessment of cerebral hemodynamics in response to physiologic challenges including paced 6-cycle breathing, hyperventilation, 7% CO2 breathing and head-up tilting test in twelve healthy subjects. Intrinsic mode functions (IMFs) were extracted from arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) signals, and was quantified by logarithmic averaged period and logarithmic energy density. The IMFs were able to show characteristics of ABP and CBFV waveform morphology in beat-to-beat timescale and in long-term trend scale. The changes in averaged period and energy density derived from IMFs were helpful for qualitative and quantitative assessment of ABP and CBFV responses to physiologic challenges. CEEMD is a promising method for assessing non-stationary components of systemic and cerebral hemodynamics.

The fault features of roller bearing vibration signal are usually immerged in heavy background noise and difficult to be extracted. Especially for the diagnosis of weak fault, it is even harder. To extract the weak fault features of bearings effectively, a new method which used complementary ensemble empirical mode decomposition (CEEMD) and Dempster-Shafer (DS) evidence theory is proposed. Firstly, the noise reduction of original signal based on CEEMD is carried out. The vibration signal of roller bearing is decomposed into a series of intrinsic mode functions (IMFs) by CEEMD. The cross correlation coefficients between the original signal and each IMF are calculated to select effective IMF. Secondly, the data-based fusion of effective IMFs is carried on by DS fusion rules. The fault feature components and interference components in the vibration signal can be considered as two incompatible propositions of DS “recognition framework”. The effective IMFs represent the evidences supporting the fault features. Fusing the effective IMFs to integrate the local incomplete information of single IMF and enhance the weak fault features of bearings. Finally, the fault features can be extracted after Hilbert envelope demodulation. The effectiveness of the presented method is validated by simulation and experimental signal. And the results indicate that the proposed method is available for detecting the bearing faults and able to diagnose the weak fault at an early stage.

This paper proposes a framework combining the complementary ensemble empirical mode decomposition with both the independent component analysis and the non-negative matrix factorization for estimating both the heart rate and the respiratory rate from the photoplethysmography (PPG) signal. After performing the complementary ensemble empirical mode decomposition on the PPG signal, a finite number of intrinsic mode functions are obtained. Then, these intrinsic mode functions are divided into two groups to perform the further analysis via both the independent component analysis and the non-negative matrix factorization. The surrogate cardiac signal related to the heart activity and another surrogate respiratory signal related to the respiratory activity are reconstructed to estimate the heart rate and the respiratory rate, respectively. Finally, different records of signals acquired from the Medical Information Mart for Intensive Care database downloaded from the Physionet Automated Teller Machine (ATM) data bank are employed for demonstrating the outperformance of our proposed method. The results show that our proposed method outperforms both the digital filtering approach and the conventional empirical mode decomposition based methods in terms of reconstructing both the surrogate cardiac signal and the respiratory signal from the PPG signal as well as both achieving the higher accuracy and the higher reliability for estimating both the heart rate and the respiratory rate.

A bearing fault diagnosis method based on complementary ensemble empirical mode decomposition (EEMD) and kernel fuzzy c-means (KFCM) algorithm is proposed to address the difficulties in feature extraction and fault diagnosis of wind turbine gearbox vibration signals. Based on empirical mode decomposition method, complementary ensemble empirical mode decomposition is proposed for the decomposition of gearbox vibration signals, obtaining multiple intrinsic mode functions. By calculating the sample entropy of the intrinsic mode function components as feature vectors, the kernel fuzzy c-means algorithm is used to achieve gearbox fault diagnosis. The experimental results show that the proposed method can effectively identify gearbox faults. In order to verify the progressiveness of the proposed method, the proposed method is compared with other methods. The experimental results show that the proposed method has higher fault diagnosis accuracy, which verifies the progressiveness of the proposed method.

Rotating devices such as gears often generate fault vibration signals during eration. Under the interference of strong external noise, it is often difficult to extract these weak fault feature. To this end, a method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Singular Spectrum Analysis (SSA) is proposed to reduce the noise of the gear signals. Firstly, original vibration signals are decomposed into a series of intrinsic mode functions (IMFs) by CEEMD, and the reconstructed signals are obtained after the some IMFs are removed according to evaluation coefficients. Then, the reconstructed signals are further denoised using SSA. After singular value decomposition, grouping and reconstruction, the denoised time series are obtained. The actual gear vibration signals from the QPZZ-II rotating machinery fault simulation rig are used to verify the effectiveness of the proposed method. The results of comparing with the different denoising methods show that the presented method can effectively depress the strong noise, and extract weak fault feature.

Obtaining the instantaneous frequency of seismic data by Hilbert transform is widely used in reservoir prediction and fluid identification. In order to obtain high accuracy and clear physical significance instantaneous frequency, it is usually required that the analyzed signal is stationary and narrow-band. While practical seismic data is a nonstationary and bandwidth signal, directly applying Hilbert transform to seismic data to obtain instantaneous frequency will lack physical significance or even distort. Complementary Ensemble Empirical Mode Decomposition (CEEMD) can adaptively decompose a complex signal into a limited number of Intrinsic Mode Function (IMF), which is stationary, narrow-band and contains the local characteristics information of the original signal. Therefore, applying Hilbert transform to IMF can obtain high accuracy and clear physical significance instantaneous frequency. The amplitude and frequency attributes of seismic data will change abnormally after the reservoir is oil or gas bearing. Extracting the corresponding abnormal change can be used as an effective indicator to identify hydrocarbon information. In this article, the Hilbert Huang transform based on CEEMD is applied to hydrocarbon detection. First, the seismic data is processed by CEEMD to obtain IMF components. Then the IMF component which can reflect the local anomaly changes caused by hydrocarbon is selected. Finally, the instantaneous frequency of the IMF component related to hydrocarbon is obtained by Hilbert transform, and take it as the sign of hydrocarbon detection. Note: This paper was accepted into the Technical Program but was not presented at IMAGE 2021 in Denver, Colorado.