Teager Energy Research Articles

An improved morphological filtering and feature enhancement method for rolling bearing fault diagnosis

Abstract The defects-induced periodic pulse is one of the important indices for the characterization of bearing failure. To solve the problem that the weak impact features caused by the early fault of the rolling bearing are easily to be interfered with by noise and strong background signal and are difficult to extract, an improved morphological filtering method combined with the Teager energy operator (TEO) is proposed to extract weak shock features. Firstly, according to the correlation between the periodic pulse induced by defects and the Morlet wavelet, the Morlet wavelet is used as the model to construct the structural elements. Then, capturing the Pearson correlation coefficient of the structural elements and the original signal and the signal is filtered by the variable scale morphological filter after threshold screening. Finally, the TEO is used as the post-enhancement link to suppress the noise in the signal after morphological processing and further highlight the fault characteristics. Simulation signals, experimental signals, and field signals verify the effectiveness and robustness of the proposed method.

Fault Feature Extraction of Rolling Bearing Based on Maximum Second-Order Cyclostationarity Blind Deconvolution and CEEMD-Teager

The operation of rolling bearings is directly related to the reliability of the whole rotating machinery. If the rolling bearing failure cannot be diagnosed and repaired in time, it will probably lead to equipment shutdown. The feature extraction process is an important part of bearing fault diagnosis. Some existing feature extraction methods are sensitive to noise and interference, and cannot fully explore and characterize useful information in nonlinear and non-stationary signals in complex scenes, and sometimes even lose important information contained in the data, resulting in low diagnostic accuracy. Therefore, a new method combining Maximum Second-order Cyclostationarity Blind Deconvolution (CYCBD) and Complementary Ensemble Empirical Mode Decomposition (CEEMD) was proposed for feature extraction of rolling bearing faults. Firstly, the cyclic frequency is set according to the failure frequency, and the original signal is filtered by CYCBD. Then, the filtered signal is decomposed into multiple Intrinsic Mode Function (IMF) by CEEMD, and the effective IMF components are selected by kurtosis criterion for reconstruction. Finally, the Teager energy operator is used to enhance the transient impact of the reconstructed signals. The results of simulation and comparison experiments show that the proposed method can extract bearing characteristics in different fault states more effectively, and the accuracy of network model diagnosis is improved compared with the traditional methods.

A composite power quality disturbance detection method based on extremum extension optimized SVMD and Teager Energy Operator

A composite power quality disturbance detection method based on extremum extension optimized SVMD and Teager Energy Operator

Damage-related imbalance identification for UAV composite propeller blades based on bidirectional temporal convolutional network and a flexible sensing system

Abstract Damage to the composite propeller blades could lead to rotational imbalance, which seriously affects the operational safety of unmanned aerial vehicles (UAVs), therefore, a novel method combining the Teager energy operator (TEO) and bidirectional temporal convolutional network (BiTCN) is proposed for detecting, localizing, and quantifying the damage-related imbalance in the blades. A flexible sensing system that contains Micro electro mechanical sensor accelerometers, signal conditioning, and wireless transmission is integrated with the composite propeller for in-situ signal acquisition of the propeller blades. TEO is applied to demodulate and enhance the pulse compositions in vibration signals and singular value decomposition (SVD) is employed to suppress random noise, resulting in denoised Teager energy spectrums for model input. Temporal convolutional network (TCN) has been widely used in sequence signal modeling because the causal dilated convolution could learn the context information of sequence signals while maintaining the advantages of parallel computing. To fully extract the signal features, BiTCN models are established to learn both the forward and backward signal features. Experimental verification results show that the proposed method detects the existence of imbalance with 100% accuracy, and the accuracies of localization and quantization are 99.65% and 98.61%, respectively, which are much higher than those of the models with the original signal as input. In addition, compared with the other four different algorithms, BiTCN is superior in terms of convergence speed and prediction accuracy.

Traveling wave fault location for AC transmission lines: an approach based on the application of EMD and Teager energy operator

Traveling wave fault location for AC transmission lines: an approach based on the application of EMD and Teager energy operator

Rolling bearing fault detection method based on teager energy operator

Abstract This study presents a method for detecting subtle faults in rolling bearings by integrating singular value decomposition (SVD) and local modal decomposition (LMD). Initially, the original vibration signals undergo reconstruction in phase space and subsequent denoising using SVD. Following this, LMD is employed to generate multiple local modal functions (IMFs), with the most sensitive IMF components selected based on kurtosis-mean square deviation criteria. The Teager energy operator (TEO) is then computed for these selected IMFs. Ultimately, Fourier transform analysis of TEO is utilized to identify faults in the inner and outer races of rolling bearings. Test results conducted on publicly available data from Case Western Reserve University validate the accurate identification of inner and outer race faults using this approach.

A Study on Fault Localization Method of Three-Terminal Multi-Section Overhead Line–Cable Hybrid Line Using MEEMD Combined with Teager Energy Operator Algorithm

An improved fault localization method combining total aggregate empirical modal decomposition (MEEMD) and Teager energy operator (TEO) is proposed to address the fault localization issue of three-terminal multi-segment overhead line–cable hybrid transmission lines. This method solves the fault localization problem caused by wave impedance discontinuity in hybrid lines. First, the MEEMD algorithm, which improves modal aliasing, and the Teager energy operator, which reflects transient energy changes, are combined for the accurate detection of faulty traveling wave heads. The fault line section determination condition within the fault branch is used to determine the overhead line section or cable section where the fault is located after determining the faulty branch line. This condition is based on the time difference between the initial traveling wave of the fault arriving at each end measurement point and the T-node. Ultimately, the fault distance is determined using the double-ended traveling wave technique. By combining PSCAD and MATLAB for simulation verification, the accuracy of the ranging method is compared and analyzed, and the results show that the method is able to ensure the accuracy of ranging at different fault locations and transition resistances. Additionally, the localization error is basically kept within 30 m, which significantly improves the accuracy of fault localization.

Iterative Laplace of Gaussian filter and improved Teager energy operator for bearing fault detection in gearboxes

Abstract The presence of cyclic impulses is considered to be a significant indicator of bearing defects. The extraction of fault-related information from gearboxes, however, poses a significant challenge due to the complex working conditions in gear transmission systems. These conditions often lead to fault-related impulse features being masked by inevitable background noise and vibration components. To address this issue, a novel iterative Laplace of Gaussian (LoG) filtering technique is proposed for the fault diagnosis field of bearings in gearboxes. An objective function incorporating Kurtosis statistics is used to iteratively update LoG coefficients instead of relying on fixed coefficients as in the original version, making the iterative LoG filtering technique better suited for handling fault-induced impact signals. Furthermore, an improved Teager energy operator (TEO) is developed to reduce the susceptibility of the original TEO to noise. The improvement involves incorporating a multi-resolution symmetric difference sequence into the discrete expression of TEO. This symmetric difference acts as a translation-invariant, sliding-window filter that, when combined with an energy operator, enables both energy detection and frequency filtering. The robustness against noise and sensitivity to frequency can be further improved by adjusting the multi-resolution parameter. The results of the experiments confirm the effectiveness of the proposed approach in identifying weak bearing fault features for gearboxes. Besides, the proposed method is also compared with the original LoG filter-based method and some competing methods, demonstrating its superior performance.

Research on New Energy Transmission Line Fault Localization Method Based on APSVMD-NTEO

Abstract Given the new energy transmission line fault, traveling wave head detection difficulties, wave speed uncertainty, positioning errors, and other problems, this paper proposes a new Teager energy operator based on adaptive parameter selection variational mode decomposition (APSVMD) and new energy transmission line fault location method (NTEO). The envelope fuzzy entropy is introduced to select the key parameters of SVM, and the sampling range of the Teager energy operator (TEO) is improved to effectively improve the capture accuracy of the wave head. In terms of fault location, by improving the double-ended traveling wave method, the uncertainty of wave speed and the effect of actual line length error is eliminated, and the ranging error is reduced.

Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy

Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decomposition (VMD) or discrete wavelet transform (DWT). A feature is then extracted from each subband using one of the following measures: standard deviation, interquartile range, band power, Teager energy, Katz's and Higuchi's fractal dimensions, Shannon entropy, sure entropy, or threshold entropy. Different machine learning techniques are used to classify the features of MCI cases from those of healthy controls. The classifier's performance is validated using leave-one-subject-out (LOSO) cross-validation (CV). The non-dominated sorting genetic algorithm (NSGA)-II is designed with the aim of minimizing the number of EEG channels (or features) and maximizing classification accuracy. The performance is evaluated using a publicly available online dataset containing EEGs from 19 channels recorded from 24 participants. The results demonstrate a significant improvement in performance when utilizing the NSGA-II algorithm. By selecting only a few appropriate EEG channels, the LOSO CV-based results show a significant improvement compared to using all 19 channels. Additionally, the outcomes indicate that accuracy can be further improved by selecting suitable features from different channels. For instance, by combining VMD and Teager energy, the SVM accuracy obtained using all channels is 74.24%. Interestingly, when only five channels are selected using NSGA-II, the accuracy increases to 91.56%. The accuracy is further improved to 95.28% when using only 8 features selected from 7 channels. This demonstrates that by choosing informative features or channels while excluding noisy or irrelevant information, the impact of noise is reduced, resulting in improved accuracy. These promising findings indicate that, with a limited number of channels and features, accurate diagnosis of MCI is achievable, which opens the door for its application in clinical practice.

A Sleep Apnea Detection Methodology Based on SE-ResNeXt Model Using Single-Lead ECG

Sleep apnea (SA) is considered one of the most dangerous sleep disorders. That happens when a person is sleeping, his or her breathing repeatedly stops and starts. In order to develop therapies and management strategies that will be effective in treating SA, it is critical to precisely diagnose sleep apnea episodes. In this study, the single-lead electrocardiogram (ECG), one of the most physiologically pertinent markers for SA, is analyzed to identify the SA issue. In this paper, a novel signal processing method is proposed, in which noise filtering is added and the detection of R peaks is utilized. Particularly, the Teager Energy Operator (TEO) algorithm is applied to detect R peaks and then obtain the RR intervals and amplitudes. Afterward, the SE-ResNeXt 50 deep learning model, which has never been used in SA detection before, is used as a classifier to perform the objective. The proposed model, which is a variation of ResNet 50, has the ability to use global information to highlight helpful information while allowing for feature recalibration. In order to confirm the proposed method, the benchmark dataset PhysioNet ECG Sleep Apnea v1.0.0 is used. Results are better than current research, with 89.21% accuracy, 90.29% sensitivity, and 87.36% specificity. This is also clear evidence that the ECG signals can be taken advantage of to efficiently detect SA.

Method and system for automated detection of sleep spindles using a single EEG channels based TEO and EMD

As a hallmark of N2 sleep stage, sleep spindle detection based on electroencephalogram (EEG) recordings plays a crucial role in analyzing sleep. Hence, how to effectively automatically detect sleep spindle is crucially important. However, many automatic detection methods assume that the signal is stationary, which is inconsistent with the observation that spindle signals have a significant change in amplitude. Thus, some important non-stationary information and the presented evident dynamic characteristics has been ignored.To overcome the aforementioned shortcoming, we propose a novel methodology based on Teager Energy Operator (TEO) and Empirical Mode Decomposition (EMD) to determine the exact location of sleep spindles using single-channel EEG signals. Because the TEO is sensitive to amplitude variations and energy features, our proposed method is suitable for capturing sleep spindles. We conduct plenty of experiments to evaluate our method. The experiments are conducted to validate the effectiveness of our method. On the DREAMS Sleep Spindles Database, our method achieves 91.83% ± 1.1%, 94.12% ± 3.46%, and 83.38% ± 9.32% for accuracy, specificity, and sensitivity, respectively. Experimental results show that the performance of the proposed methodology presented herein achieves better performance as compared to other methods. Moreover, owing to its usage of a single channel of EEG signal, the proposed method will be suitable for edge-device implementation to expedite sleep disorder diagnosis.

Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks

High-voltage direct current (HVDC) transmission is preferred over high-voltage alternating current (HVAC) for long power lines for asynchronous power grid interconnection and high-level renewable energy integration. The control and protection functions associated with HVDC systems help with fast and secure clearance of faults. The control and protection challenges in the embedded HVDC network are of great concern for the stable and secure operation of an HVDC network. The DC fault current may reach an extremely high level in a rather short period because of the low impedance in a DC system, which is dangerous for converters, and disturbances in the AC network directly influence the performance of the HVDC system. Sometimes, faults on the AC side may lead to disconnection or failure of the DC link, causing reliability problems as well as huge economic losses. AC and DC protection solutions are being developed for HVDC systems to enhance their sustainability and reliability. As such, AC and DC faults should be detected and cleared at a faster rate. Therefore, in this article, the feasibility of the synchro-squeezed transform (SST) is analyzed for detection purposes. For more accurate and faster detection, the signal is first decomposed using the empirical mode decomposition (EMD) technique, and then the SST is applied. A discrete Teager energy (DTE) spectrum is obtained with the processed signal, which works as the detection index. The algorithm shows low sampling frequency requirements, with higher efficiency and reliability for the purpose. PSCAD/EMTDC version 4.6 software and MATLAB 2022a software is used for the modeling and simulation.

Nonlinear Forced Vibration Analysis using ‘Elmer’ FEM Package to Develop Poincaré Map and Correlation Method-based Damage Indicators

With the rapid advancement of sensor technology, structural health monitoring (SHM) is also blossoming with prowess in the field of civil, mechanical and aerospace engineering. It deals with the initiation of damage and its characteristic behavior in a deterministic or predictive way. The time-dependent fluctuation associated with the dynamic motion of structures was addressed with few efficient indicators in the past to identify the presence of damage as well as to localize the faulty area. The formulation of all those indicators is validated on specific structural arrangements with certain assumptions. This limitation is considered in this article to project the global applicability of the method along with the inherent constraints. The damage detection in the time-domain is considered to utilize direct accessibility of responses from the sensors attached to the members. It reduces the computational difficulty in transforming response to another domain and also associated approximations that emerge through numerical calculations. Broadly, two types of methods are discussed in this article along with modelling and analysis procedure; methods developed from cross-correlation of response and graphical method or Poincaré map. The model of cantilever beam and axial bar is developed in FreeCad where damage is induced by reducing either thickness or Young’s modulus of damaged elements to simulate cracked member. Meshing of member is done in Gmsh. To generate response history of state variables a newly developed finite element (FE) analysis software package Elmer is then employed for the dynamic analysis of the member with geometric nonlinearity. At last, application of Teager energy operator (TEO) in noise reduction is explained with a general example.

Life Prediction of Rolling Bearing Based on Optimal Time-Frequency Spectrum and DenseNet-ALSTM.

To address the challenges faced in the prediction of rolling bearing life, where temporal signals are affected by noise, making fault feature extraction difficult and resulting in low prediction accuracy, a method based on optimal time-frequency spectra and the DenseNet-ALSTM network is proposed. Firstly, a signal reconstruction method is introduced to enhance vibration signals. This involves using the CEEMDAN deconvolution method combined with the Teager energy operator for signal reconstruction, aiming to denoise the signals and highlight fault impacts. Subsequently, a method based on the snake optimizer (SO) is proposed to optimize the generalized S-transform (GST) time-frequency spectra of the enhanced signals, obtaining the optimal time-frequency spectra. Finally, all sample data are transformed into the optimal time-frequency spectrum set and input into the DenseNet-ALSTM network for life prediction. The comparison experiment and ablation experiment show that the proposed method has high prediction accuracy and ideal prediction performance. The optimization terms used in different contexts in this paper are due to different optimization methods, specifically the CEEMDAN method.

Detection and localization of fault in DC microgrid using discrete Teager energy and generalized least square method

PurposeThis paper aims to introduce a new fault protection scheme for microgrid DC networks with ring buses.Design/methodology/approachIt is well recognized that the protection scheme in a DC ring bus microgrid becomes very complicated due to the bidirectional power flow. To provide reliable protection, the differential current signal is decomposed into several basic modes using adaptive variational mode decomposition (VMD). In this method, the mode number and the penalty factor are chosen optimally by using arithmetic optimization algorithm, yielding satisfactory decomposition results than the conventional VMD. Weighted Kurtosis index is used as the measurement index to select the sensitive mode, which is used to evaluate the discrete Teager energy (DTE) that indicates the occurrence of DC faults. For localizing cable faults, the current signals from the two ends are used on a sample-to-sample basis to formulate the state space matrix, which is solved by using generalized least squares approach. The proposed protection method is validated in MATLAB/SIMULINK by considering various test cases.FindingsDTE is used to detect pole-pole and pole-ground fault and other disturbances such as high-impedance faults and series arc faults with a reduced detection time (10 ms) compared to some existing techniques.Originality/valueVerification of this method is performed considering various test cases in MATLAB/SIMULINK platform yielding fast detection timings and accurate fault location.

FPGA implementation of an improved envelope detection approach for bearing fault diagnosis

This study presents an enhanced envelope detection technique implemented on a field-programmable gate array (FPGA) to diagnose bearing faults in rotating machinery. Bearing faults frequently result in machinery breakdowns, incurring substantial downtime and maintenance expenses. In our approach, we employ the Teager energy operator (TEO) to extract the vibration signal envelope. Subsequently, we subject the envelope signal to the fast Fourier transform (FFT) to generate the envelope spectrum of the vibration signal. Finally, we further refine the envelope spectrum using TEO for a second time, resulting in a pronounced fault peak that facilitates early fault detection. We evaluate the effectiveness and performance of the proposed method using two distinct types of bearing vibration signals, one being simulated and the other measured. Our findings reveal that the suggested approach outperforms traditional envelope detection methods, leading to a substantially enhanced fault diagnosis capability. For instance, when we assess the characteristic frequency ratio (FCFR) for faults in the inner and outer rings of the bearing using the proposed method, we observe that the FCFR values are significantly elevated, ranging from 160 to 330% higher compared to the analysis performed by the TEO and HT methods. Consequently, this indicates that the proposed approach has the ability to detect faults at an earlier stage than other methods. Furthermore, the FPGA-based implementation makes it suitable for critical industrial applications where rapid fault detection is essential to prevent catastrophic failures.

Quantification of Hypsarrhythmia in Infantile Spasmatic EEG: A Large Cohort Study.

Infantile spasms (IS) is a neurological disorder causing mental and/or developmental retardation in many infants. Hypsarrhythmia is a typical symptom in the electroencephalography (EEG) signals with IS. Long-term EEG/video monitoring is most frequently employed in clinical practice for IS diagnosis, from which manual screening of hypsarrhythmia is time consuming and lack of sufficient reliability. This study aims to identify potential biomarkers for automatic IS diagnosis by quantitative analysis of the EEG signals. A large cohort of 101 IS patients and 155 healthy controls (HC) were involved. Typical hypsarrhythmia and non-hypsarrhythmia EEG signals were annotated, and normal EEG were randomly picked from the HC. Root mean square (RMS), teager energy (TE), mean frequency, sample entropy (SamEn), multi-channel SamEn, multi-scale SamEn, and nonlinear correlation coefficient were computed in each sub-band of the three EEG signals, and then compared using either a one-way ANOVA or a Kruskal-Wallis test (based on their distribution) and the receiver operating characteristic (ROC) curves. The effects of infant age on these features were also investigated. For most of the employed features, significant ( ) differences were observed between hypsarrhythmia EEG and non-hypsarrhythmia EEG or HC, which seem to increase with increased infant age. RMS and TE produce the best classification in the delta and theta bands, while entropy features yields the best performance in the gamma band. Our study suggests RMS and TE (delta and theta bands) and entropy features (gamma band) to be promising biomarkers for automatic detection of hypsarrhythmia in long-term EEG monitoring. The findings of our study indicate the feasibility of automated IS diagnosis using artificial intelligence.

Line fault location of flexible DC distribution network based on adaptive noise empirical mode decomposition

A fault location method for flexible DC distribution network lines is proposed to address the issues of insufficient decomposition in fault signal extraction and the complexity of traveling wave head detection. The method combines adaptive noise empirical mode decomposition (CEEMDAN) and Teager energy operator (TEO). Firstly, CEEMDAN is used to adaptively denoise the line mode signal and obtain the optimal modal component to overcome modal aliasing and insufficient decomposition in signal separation. Then, the reach time of the fault traveling wave is determined by using TEO. Ultimately, the fault location is accurately determined by employing both ends of the traveling wave ranging technique. Through the combination of software PSCAD and MATLAB, the simulation experiment of the line fault is carried out. This means that the method can quickly and precisely identify the location of faults in a system.

A Novel Fault Identification and Localization Scheme for Bipolar DC Microgrid

Traditional fault detection and location schemes become ineffective for detecting and locating the fault in DC microgrid systems due to integrating various types of power electronic-based DC loads and generators. To resolve this issue, advanced, intelligent, specialized fault detection and location schemes are necessary. This article suggests a novel fault detection scheme based on the difference in the teager energy available in the DC current wave at sending and receiving ends of lines. After the detection of the fault, the location is calculated by estimating the resistance of the cable up to the fault point as well as the total resistance of the cable, with the help of least square technique. The proposed scheme decides fault is internal if the estimated fault location is less than 1 p.u. Otherwise, the proposed scheme decides fault is external. A DC microgrid with different types of generating units and loads is simulated using MATLAB/SIMULINK to evaluate the developed algorithm. Internal and external faults, pole-to-ground and pole-to-pole faults with changing fault resistance and fault location are some of the fault scenarios that have been simulated. The obtained simulation results prove that the suggested algorithm can discriminate between internal and external faults and locate the fault. The proposed technique is also examined on a DC microgrid hardware testbed and results show the efficiency of the suggested approach.