Teager-Kaiser Energy Operator Research Articles

Retraction Note: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Retraction Note: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Faulty line detection for cross‐line same‐phase successive ground faults in distribution network based on transient characteristics

AbstractCurrently, theoretical analysis of single‐phase grounding (SPG) faults in low‐current grounding systems has reached a high level of refinement, and the corresponding detection methods have matured. However, there is a significant lack of research on successive grounding (SG) faults, particularly in the area of detecting cross‐line same‐phase successive grounding (CSSG) faults in arc suppression coil grounding systems. To address these issues, a novel faulty line detection method based on transient characteristics of CSSG is proposed. Firstly, a transient equivalent circuit is established, and the characteristics of transient zero‐sequence current (ZSC) are theoretically analysed. Secondly, variational mode decomposition (VMD) is employed to extract low and high‐frequency information from intrinsic mode functions (IMFs). Subsequently, a fault initiation criterion based on kurtosis theory and the Teager‐Kaiser energy operator (TKEO) is proposed, which can accurately detect the occurrence time of secondary faults. Finally, a method for faulty line detection based on cross‐correlation distance (CCD) and transient energy (TE) is proposed. The results of PSCAD simulation under different conditions and field test affirm the accuracy of the analysis and the effectiveness of the proposed method.

Research on the Application of Variational Mode Decomposition Optimized by Snake Optimization Algorithm in Rolling Bearing Fault Diagnosis

The rolling bearing is one of the commonly used mechanical components in rotating machinery, and its health directly affects the normal operation of equipment. However, the fault signal of rolling bearing is susceptible to noise interference, which makes it difficult to extract the fault characteristics of the rolling bearing and thus affects the accuracy of the diagnosis results. To address this problem, this paper proposes a method by using a snake optimization algorithm to optimize variational mode decomposition (SOA-VMD) and applies it to the extraction of the fault feature of rolling bearing. First, the minimum Shannon entropy to kurtosis ratio (EKR) is used as the fitness function of SOA to search for the best parameter combination of VMD. Second, the optimized VMD is used to decompose the vibration signal of rolling bearing to obtain K intrinsic mode functions (IMFs). Then, the IMF with the most fault information is selected for reconstruction through EKR. The Teager–Kaiser energy operator (TKEO) spectrum analysis is performed on the reconstructed signal. Finally, this method is used to analyze the simulation signal and rolling bearing vibration signal and compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithms to verify the feasibility and effectiveness of the SOA-VMD method.

Fault diagnosis of reciprocating compressor using Teager-Kaiser energy operator and envelope spectral feature extraction

This paper proposed and implemented the Teager-Kaiser energy operator (TKEO) and envelope spectral analysis techniques for the fault detection of discharge valves of a reciprocating compressor. Based on the extraction of fault features, the instantaneous frequency and amplitude of the signals due to the discharged valve based on energy identification can be effectively characterized by the TKEO that was used to identify the characteristic fault signals accurately. The synthesized signal is processed by envelope spectral analysis and TKEO, which can extract the characteristic signal and eliminate the noise. The experimental design is verified experimentally through different reciprocating compressor gas valve conditions. The simulation results verify the feasibility of the proposed method. The experimental verification is carried out through the measurement signals of the six-cylinder reciprocating compressor under different valve operating conditions. TKEO can remove background noise to obtain reciprocating compressor fault feature signals. Feature extraction is based on TKEO and envelope spectra for fault detection of reciprocating compressors. It is expected to reduce the errors produced by traditional manual fault diagnosis methods and improve the accuracy and efficiency of fault diagnosis. The research results of vibration fault feature extraction using TKEO can be used as the basis for fault diagnosis of the reciprocating compressor system.

Fault Feature Extraction Method of Rolling Bearing Based on IAFD and TKEO

The study of bearing fault feature extraction using adaptive Fourier decomposition (AFD) holds significant practical importance. However, AFD is constrained by its reliance on prior knowledge for determining decomposition levels, which can result in either underdecomposition or overdecomposition based on a single indicator. Consequently, an improved adaptive Fourier decomposition (IAFD) is proposed. First, a combined weight index called SP is constructed, and the whale optimization algorithm is employed to optimize the SP weight parameter. Second, the IAFD decomposition levels can be adaptively determined using the optimized SP. Finally, a feature extraction method-based IAFD and Teager–Kaiser energy operator is applied in rolling bearing fault diagnosis. Case studies on the Case Western Reserve University and self-made KUST-SY datasets validate the effectiveness of the proposed method.

Comparison of sEMG Onset Detection Methods for Occupational Exoskeletons on Extensive Close-to-Application Data.

The design of human-machine interfaces of occupational exoskeletons is essential for their successful application, but at the same time demanding. In terms of information gain, biosensoric methods such as surface electromyography (sEMG) can help to achieve intuitive control of the device, for example by reduction of the inherent time latencies of a conventional, non-biosensoric, control scheme. To assess the reliability of sEMG onset detection under close to real-life circumstances, shoulder sEMG of 55 healthy test subjects was recorded during seated free arm lifting movements based on assembly tasks. Known algorithms for sEMG onset detection are reviewed and evaluated regarding application demands. A constant false alarm rate (CFAR) double-threshold detection algorithm was implemented and tested with different features. Feature selection was done by evaluation of signal-to-noise-ratio (SNR), onset sensitivity and precision, as well as timing error and deviation. Results of visual signal inspection by sEMG experts and kinematic signals were used as references. Overall, a CFAR algorithm with Teager-Kaiser-Energy-Operator (TKEO) as feature showed the best results with feature SNR = 14.48 dB, 91% sensitivity, 93% precision. In average, sEMG analysis hinted towards impending movements 215 ms before measurable kinematic changes.

The LPR Instantaneous Centroid Frequency Attribute Based on the 1D Higher-Order Differential Energy Operator

In ground-penetrating radar (GPR) or lunar-penetrating radar (LPR) interpretation, instantaneous attributes (e.g., instantaneous energy and instantaneous frequency) are often utilized for attribute analysis, and they can also be integrated into a new attribute, i.e., the instantaneous centroid frequency. Traditionally, the estimation of instantaneous attributes calls for complex trace analysis or energy operator schemes (e.g., the Teager–Kaiser energy operator, TKEO). In this work, we introduce the 1D higher-order differential energy operator (1D-HODEO) to track instantaneous attributes with better localization. In collocation with the mode decomposition algorithms, the 1D-HODEO performs along each A-scan on the decomposed mode slices to form the final profile of instantaneous centroid frequency by using the piece-wise correlation coefficients. Both a numerical model for simulating two-layer lunar regolith and the LPR Yutu-2 data show that the proposed instantaneous centroid frequency profile on the 1D-HODEO has better resolution, in comparison with that of TKEO and the traditional time-varying centroid frequency. In this work, we present a new approach for extracting instantaneous centroid frequency attributes which provides more comprehensive information in lunar stratigraphic interpretation and LPR attribute analysis.

A life prediction method of rolling bearing based on signal reconstruction and fusion dual channel network

In addressing the problem of low prediction accuracy in the remaining useful life (RUL) prediction of rolling bearings, caused by noise interference and insufficient extraction of sensitive features by deep learning models, this paper presents a life prediction method based on signal reconstruction and dual-channel network fusion. First, addressing the issue of extracting weak features from rolling bearing vibration signals, an optimized combination of variational mode decomposition (VMD) and Teager–Kaiser energy operator (TKEO) for signal reconstruction is proposed. TKEO is used to track the energy of high-frequency impulses in the original signal. The sparrow search algorithm is employed for optimizing VMD to perform high-frequency filtering, resulting in an optimized impulse energy signal. This signal is then multiplied with the original signal to enhance weak impulse features under noisy background. Next, a dual-channel network model for RUL prediction is constructed based on temporal convolutional network (TCN) and convolutional neural networks (CNNs). The one-dimensional time-series signal obtained after reconstruction and denoising serves as the input for the TCN network channel. Simultaneously, the signal obtained after reconstruction and denoising undergoes synchrosqueezed wavelet transforms to generate a two-dimensional time–frequency representation as input for the CNN network channel. This setup allows TCN and CNN to respectively extract temporal features from the vibration signal and time–frequency features from the spectrogram. By incorporating self-attention mechanisms, internal correlations between different features are fully explored, thereby enhancing prediction accuracy.

Fault Section Recognition within Duration of Power Swing Using Teager–Kaiser Energy Operator and Sequence Components

Relaying operation is not reliable during power swing due to the presence of additional lower-frequency components of electrical signals. Relay operation is prohibited during Power swing blocking (PSB) operation to restrict the malfunctioning of the distance relay. During such periods, fault section identification (FSI) schemes need to work for safeguarding the electrical power system from disturbances. Fault detection and fault direction estimation are part of FSI. This article proposes a protection algorithm within the period of power swing where a Teager–Kaiser energy operator (TKEO) of superimposed negative sequence admittance (SNSA) is utilized for fault detection and an integrated scheme for determining their direction. In this integrated direction estimation approach, the direction of fault is recognized by calculating the phases of negative sequence admittance (NSA) and positive sequence admittance (PSA). The proposed technique is verified and tested on a WSCC 9-bus, 400 kV, 50 Hz system for different balanced and unbalanced fault cases with different fault conditions. Even, in some scenarios like CT saturation and single pole tripping (SPT), the proposed approach performs accurately. The results are simulated using EMTDC/PSCAD software.

A nonlinear feature extraction approach for speech emotion recognition using VMD and TKEO

Speech emotion recognition (SER) is still a challenging research area in human–computer interaction-based systems. This paper proposed a nonlinear feature extraction technique to improve the classification performance of the SER system. The proposed method explores variational mode decomposition (VMD) with the Teager-Kaiser energy operator (TKEO) for the SER. First, VMD decomposes a speech signal into modes, and then the nonlinear TKEO operator is applied to each mode to obtain a time series. The VMD-TKEO preprocessed signal is used to extract the global features based on Energy, Pitch frequency and Mel frequency cepstral coefficients. The features are statistically examined using the Kruskal-Wallis test. The resultant feature set is examined over the support vector machine and its variants for emotion classification. The Ryerson Audio-Visual database is used for the experiment, and different emotion classification problems are formulated. Finally, the accuracy of the proposed SER architecture is quantitatively analyzed, which outperforms the other existing architectures.

Bearing fault classification using TKEO statistical features and artificial intelligence

The study introduces a novel approach to classify faulty bearings using a combination of the Teager-Kaiser Energy Operator (TKEO) and Artificial Intelligence. The TKEO signal is used for statistical feature extraction to distinguish between healthy and abnormal bearings and two datasets were used to evaluate the proposed method. Total 11 statistical features were extracted from the raw and processed signals using the TKEO operator. The obtained feature set was used as input for various machine learning algorithms, and their performance was compared. Additionally, statistical features were calculated using the Hilbert Transform and compared to the proposed method. The study found that when the TKEO features were used as input for the classifier in the acoustic signal, the CART model achieved the highest accuracy of 99.62% compared to the raw and Hilbert transform signal features. In the case of vibration signals, the TKEO signal feature outperformed the raw signal feature with 100% accuracy for all artificial intelligence models. The proposed methodology revealed that using TKEO signal features as input significantly enhanced the classification accuracy.

An enhanced cyclostationary method and its application on the incipient fault diagnosis of induction motors

The cyclostationary analysis techniques have been extensively explored for the purpose of fault detection in rotating machinery. However, there are still huge challenges because of both limited detection frequency range and low fault identification accuracy. This paper proposes an improved cyclostationary method to enhance incipient fault features. Firstly, the continuous wavelet transform is used to accurately locate important frequency bands, and the fault modulation mechanism or fast kurtogram can be adopted to design the optimal wavelet transform scale factor. Secondly, the Teager-Kaiser energy operator is improved to be used in the frequency domain for the weak fault feature enhancement. Finally, fault features are presented in the cyclic frequency domain through spectral coherence and enhanced envelope spectrum. The proposed method is verified through both numerical simulation and experiments, including incipient half-broken rotor bar, and rolling bearing outer race faults in induction motors.

Ambient oscillatory mode assessment in power system using an advanced signal processing method

Estimation of electromechanical mode properties is crucial in modern power systems for providing the operators with an adequate indication of the stress in the system. Measurement-based approaches use signal processing algorithms for mode identification and parameter estimation. This paper presents a novel framework for the assessment of low-frequency oscillation modes using real-world synchrophasor data with minimum computational effort. A nonstationary approach known as Time-Varying Filter based Empirical Mode Decomposition (TVF-EMD) technique is used to identify the dominant low-frequency modes present in the ambient PMU data. The combination of TVF-EMD with Teager Kaiser Energy Operator (TKEO) precisely estimates the instantaneous mode parameters, such as frequency, amplitude, and damping ratio. The efficacy of the proposed approach is demonstrated by applying it in a synthetic signal, simulated data of a standard IEEE test system, and in real-world PMU data of the Indian power grid. The proposed method is compared with the existing methodologies and the observations reveal that the proposed method has robust performance in estimating the instantaneous mode features in the power system with less computational complexities.

Model-free inertia estimation in bulk power grids through O-splines

This paper proposes a novel methodology to precisely estimate the generation units’ inertia constant by using the wide-area monitoring systems (WAMS) information of bulk power grids. The method accurately estimates the rate of change of frequency (RoCoF) and the amplitude of active power deviations through the multivariate O-splines-based differentiators of the discrete-time Taylor–Fourier transform (DTTFT). The proposal utilises active power and frequency measurements that are synchronously collected from WAMS after load changes or generation trips. In the first stage, the Teager–Kaiser energy operator (TKEO) detects the right disturbance instant. Then, the first-order and zero-order O-splines differentiators are applied to the frequency and active power deviation measurements to simultaneously estimate the RoCoF and the amplitude of active power deviations in a sole stage. Thus, the inertia constants of all generation units derived from the second Newton law are estimated using all time-synchronised signals and processing in an exclusive step by the O-splines of the DTTFT in a short time. The attained results and their comparison with a state-of-the-art technique confirm the effectiveness and performance of the proposed strategy for inertia estimates, where simulated signals of two well-known benchmark power grids are tested.

A Novel High Impedance Fault Detection Strategy for Microgrid Based on Differential Energy Signal of Current Signatures and Entropy Estimation

The non-linearly deterministic fault current characteristics with low current magnitude of high impedance fault (HIF) have thrown an inevitable challenge to the microgrid protection system. Moreover, differentiating HIF from non-fault events is also a challenging task under different operating scenarios of the microgrid. In order to address this, a simple and fast protection algorithm, based on Empirical Mode Decomposition (EMD) in conjugation with Teager-Kaiser Energy Operator (TKEO) is proposed in this article. This scheme process the current signals retrieved from both ends of the line through EMD, and subsequently, the current signal energy is extracted by TKEO. The incidence of a fault is affirmed by measuring the energy difference signal and estimation of entropy. By employing the second layer algorithm using entropy analysis, the non-fault disturbances, such as capacitor and load switching, temporary overloading, and DG outage, have been effectively distinguished from HIFs. The proposed technique is tested for grid-tied and autonomous microgrid operation modes with several HIF and non-fault cases. It is inferred that the proposed technique can detect and differentiate HIFs from other system perturbations seamlessly in less time. Compared to other methods, this scheme has procured excellent accuracy and reliability, and is also not affected by the dynamic current behavior of the microgrid. The PSCAD-EMTDC software is used to build and simulate the test system, and the MATLAB programming interface is utilized to develop and test the suggested method.

Fault Detection in DC Microgrid: A Transient Monitoring Function-Based Method

This paper develops a transient monitoring function (TMF) based fault detection method for DC microgrids (DCMGs). In this method, the measured currents from one-end of the poles are firstly transformed into modal components. Then, the modal current is estimated using TMF concept. Afterward, the residual signal is defined as the absolute value of the difference between the modal and estimated currents. This signal has negligible fluctuations during the normal operation, while it experiences significant variations after fault occurrence. This feature is selected to reflect the fault incidence. To do so, the teager-kaiser energy operator (TKEO) is applied to the residual signal to amplify the changes, followed by a fault detection index (FDI). Comparing FDI with a threshold can reveal the fault. Furthermore, FDI can discriminate the faulty conditions in different operation modes of DCMG with a wide variation of fault conditions. To assess the developed scheme performance, a DCMG with different kinds of sources and loads is simulated in MATLAB/Simulink. The results confirm the quickness and the high accuracy of the proposed technique. Moreover, the method is validated in an experimental laboratory small-scale test bench. Finally, to demonstrate the superiority of the method, a comparative study with recent methods is presented.

Novel Approach for Synchronous Generator Protection Using New Differential Component

A new stator fault detection and classification scheme for synchronous generators (SGs), based on an amplified discrete Teager-kaiser energy operator (ADTKEO) of a new differential component ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DC_SI</i> ), is proposed. Detection of an internal fault is based on the fulfillment of the ADTKEO ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ξ</i> ) with respect to the pre-determined threshold along with the checking of second-harmonic content in the current transformer (CT) secondary current. Afterward, the classification of internal faults is performed based on ‘ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ξ</i> ’. The performance of the proposed scheme is tested on various fault cases obtained from a simulation network developed in a real-time digital simulator and the developed laboratory prototype of the SG. The results indicate that the proposed scheme not only provides 100% winding coverage against phase-to-ground faults but is also capable of detecting other phase faults within a quarter cycle for low-resistance grounded SGs. At the same time, it stays extremely stable for external faults, even considering errors in CT along with its saturation (without compromising speed/sensitivity). The proposed scheme is independent of the sampling frequency, loading condition, decaying dc component present in the acquired signals, and rating of the SG.

A robust HD-sEMG sensor suitable for convenient acquisition of muscle activity in clinical post-stroke dysphagia

Objective. A flexible high-density surface electromyography (HD-sEMG) sensor combined with an adaptive algorithm was used to collect and analyze the swallowing activities of patients with Post-stroke dysphagia. Approach. The electrode frame, modified electrode, and bonded substrate of the sensor were fabricated using a flexible printed circuit process, controlled drop coating, and molding, respectively. The adaptation algorithm was achieved by using Laplace and Teager-Kaiser energy operators to extract active segments, a cross-correlation coefficient matrix (CCCM) to evaluate synergy, and multi-frame real-time dynamic root mean square (RMS) to visualize spatiotemporal information to screen lesions and level of dysphagia. Finally, support vector machines (SVM) were adopted to explore the classification accuracy of sex, age, and lesion location with small sample sizes. Main results. The sensor not only has a basic low contact impedance (0.262 kΩ) and high signal-to-noise ratio (37.284 ± 1.088 dB) but also achieves other characteristics suitable for clinical applications, such as flexibility (747.67 kPa) and durability (1000 times) balance, simple operation (including initial, repeated, and replacement use), and low cost ($ 15.2). The three conclusions are as follows. CCCM can be used as a criterion for judging the unbalanced muscle region of the patient’s neck and can accurately locate unbalanced muscles. The RMS cloud map provides the time consumption, swallowing times, and unbalanced areas. When the lesion location involves the left and right hemispheres simultaneously, it can be used as an evidence of relatively severely unbalanced areas. The classification accuracy of SVM in terms of sex, age, and lesion location was as high as 100%. Significance. The HD-sEMG sensor in this study and the adaptation algorithm will contribute to the establishment of a larger-scale database in the future to establish more detailed and accurate quantitative standards, which will be the basis for developing more optimized screening mechanisms and rehabilitation assessment methods.

A Novel Combination Method of a Convolutional Neural Network and Energy Operators for the Detection of Change-Points in Electromyographic Signals

Currently, neural network algorithms based on time-domain features are used for change-point detection problems, and they have proven to be effective. However, due to the instability of human biosignals, establishing a training dataset with labels is difficult. For supervised learning methods, wherein parameters are updated on a small sample set through a feed-forward mechanism, it is difficult to ascertain the degree to which the performance of the trained neural network corresponds to the overfitting of the dataset upon which the network was trained. To this end, this paper attempted to directly replace the parameters in the convolutional neural network that need to be updated by training. A method based on the combination of the Teager–Kaiser energy operator (TKEO) and the convolutional neural network is proposed. We tested the proposed method on simulated EMG data with different signal-to-noise ratios and real data with labels, respectively. Compared with multiple detection methods, the proposed method had significant advantages in terms of reliability, accuracy, and computational speed. Furthermore, the proposed method does not require any prior knowledge about the signal, lending itself to be flexible and adaptable to any application. It may be a promising alternative to solving change-point detection problems.

A novel protection strategy for microgrid based on estimated differential energy of fault currents

• This article has presented a protection algorithm based on energy measurement of the current signal using TKEO. • TKEO shows that the energy of a signal can be realized in terms of its instantaneous amplitude and frequency. • The non-zero value of the energy difference index triggers the fault detection algorithm. • This class of operators is an efficient tool for analysing amplitude-modulated-frequency-modulated signals due to its simplicity and excellent time-resolution capability. • This approach can efficiently detect both low resistance and high impedance faults, with excellent distinguishable capability between faults with other power system disturbances. The significant benefits of distributed generation (DG) integration in the emerging microgrid are compromised due to substantial challenges associated with the protection. Due to DG penetration, fault current becomes dynamic, leading to make conventional protection methods vulnerable. In order to address this serious issue, a simple and fast protection algorithm, based on Teager-Kaiser Energy Operator (TKEO) is proposed in this paper. This scheme extracts the energy of current signal, retrieved from both ends of the line using TKEO. For fault detection, energy difference of the current signals is used. In order to validate the effectiveness of this proposed scheme, 33 bus test microgrid system operating in both grid-connected and autonomous mode is presented. For evaluating the performance of proposed scheme, high impedance fault cases are also examined. The distinctiveness of the proposed algorithm is that it is principally relying on the energy difference of current signals, therefore it can subdue the difficulties associated with dynamic fault current. Moreover, it does not suffer from computational complexity; thus, inherently hastening the detection process. The test system is designed and simulated in PSCAD-EMTDC software , while MATLAB programming interface is used for developing and testing the proposed algorithm.