Search Results (19)

To address the challenges posed by weak early fault signal features, strong noise interference, low diagnostic accuracy, poor reliability when using single information sources, and the limited availability of high-quality samples in practical applications for permanent magnet synchronous motor (PMSM) bearings, this paper proposes an early bearing fault diagnosis method based on Hippopotamus Optimization Variational Mode Decomposition (HO-VMD) and weighted evidence fusion of current–vibration signals. The HO algorithm is employed to optimize the parameters of VMD for adaptive modal decomposition of current and vibration signals, resulting in the generation of intrinsic mode functions (IMFs). These IMFs are then selected and reconstructed based on their kurtosis to suppress noise and harmonic interference. Subsequently, the reconstructed signals are demodulated using the Teager–Kaiser Energy Operator (TKEO), and both time-domain and energy spectrum features are extracted. The reliability of these features is utilized to adaptively weight the basic probability assignment (BPA) functions. Finally, a weighted modified Dempster–Shafer evidence theory (WMDST) is applied to fuse multi-source feature information, enabling an accurate assessment of the PMSM bearing health status. The experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio (SNR) and enables precise diagnosis of early bearing faults even in scenarios with limited sample sizes. Full article

Rolling bearing fault diagnosis in electric hoists faces significant challenges due to heavy noise and complex vibration interferences, which obscure fault signatures and hinder conventional demodulation methods. While existing techniques like the Teager–Kaiser energy operator (TKEO) and its variants (e.g., HO-AEO, SD-AEO) offer filterless demodulation, their susceptibility to noise and dependency on preprocessing limit diagnostic accuracy. This study proposes a Multi-resolution Higher-order Symmetric Analytic Energy Operator (MHSAEO) to address these limitations. The MHSAEO integrates three innovations: (1) dynamic non-adjacent sampling to suppress stochastic errors, (2) AM-FM dual demodulation via symmetric energy orthogonality, and (3) adaptive spectral mining for full-band feature extraction. Experimental validation on a 10-ton electric hoist bearing system demonstrates that the MHSAEO achieves signal-to-noise ratio improvements (SNRIs) of −3.83 dB (outer race faults) and −2.12 dB (inner race faults), successfully identifying the characteristic fault frequencies of both inner (145.9 Hz) and outer races in electric hoist bearings with 2nd–5th harmonics. Compared to traditional methods, the MHSAEO reduces computational time by 30.1 × (0.0328 s vs. 0.9872 s) without requiring preprocessing. The results confirm its superior anti-interference capability and real-time performance over the TKEO, HO-AEO, and hybrid denoising–TKEO approaches. Full article

Medium-voltage cables in hydropower plants are typically arranged in multi-circuit configurations to ensure reliability, yet their exposure to harsh operational conditions accelerates insulation degradation and increases partial discharge risks. Traditional fault localization methods, such as the traveling wave method using wavelet transform to process fault signals, suffer from wavefront distortion due to inter-line reflections and noise interference in multi-circuit systems, because wavelet-based techniques are limited by preset basis functions and environmental noise. To address these challenges, a fault localization method for multi-circuit parallel cables based on the Teager–Kaiser Energy Operator (TKEO) is proposed in this paper. First, the fault signal is decoupled using Clarke transformation to suppress common-mode interference, obtaining the α component. Subsequently, the α component is subjected to wavelet transform to obtain the high-frequency components, which are then optimized using the TKEO. The TKEO is applied to optimize the wavelet-transformed signal, enhancing transient energy variations to precisely identify the arrival time of the fault wavefront at measurement points, thereby enabling accurate fault localization. The results of the four types of fault experiments indicate that the use of the TKEO to optimize the wavelet transform of the traveling wave method improved the accuracy of fault localization. Full article

Predictive maintenance is essential for improving the efficiency of equipment and reducing downtime in industrial operations. This study investigates the application of machine learning in predictive maintenance, specifically emphasizing data preprocessing and classification techniques using the Teager–Kaiser Energy Operator (TKEO) method, which captures dynamic variation in signals. The effectiveness of TKEO was compared against conventional methods, using the Case Western Reserve University (CWRU) dataset, with vibration data collected from bearings operating under different load conditions. Different data segmentation lengths (2400 and 12,000 samples) were evaluated to assess the impact of segment size on classification accuracy. The study also investigated the effects of various feature selection strategies by comparing four- and six-feature combinations. Advanced classifiers, including support vector machines and random forests, demonstrated that TKEO effectively improved model accuracy in the capture of fault-related signal dynamics. These findings offer new insights to support reliable predictive maintenance in industrial settings and provide a new perspective for future research into active vibration control, where vibration signal analysis, feature extraction, and mathematical modeling play key roles in optimizing control algorithms and enhancing the efficiency of adaptive control systems. Full article

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. Full article

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. Full article

Neuro mechanical time delay is inevitable in the sensorimotor control of the body due to sensory, transmission, signal processing and muscle activation delays. In essence, time delay reduces stabilization efficiency, leading to system instability (e.g., falls). For this reason, estimation of time delay in patients such as people living with spinal cord injury (SCI) can help therapists and biomechanics to design more appropriate exercise or assistive technologies in the rehabilitation procedure. In this study, we aim to estimate the muscle onset activation in SCI people by four strategies on EMG data. Seven complete SCI individuals participated in this study, and they maintained their stability during seated balance after a mechanical perturbation exerting at the level of the third thoracic vertebra between the scapulas. EMG activity of eight upper limb muscles were recorded during the stability. Two strategies based on the simple filtering (first strategy) approach and TKEO technique (second strategy) in the time domain and two other approaches of cepstral analysis (third strategy) and power spectrum (fourth strategy) in the time–frequency domain were performed in order to estimate the muscle onset. The results demonstrated that the TKEO technique could efficiently remove the electrocardiogram (ECG) and motion artifacts compared with the simple classical filtering approach. However, the first and second strategies failed to find muscle onset in several trials, which shows the weakness of these two strategies. The time–frequency techniques (cepstral analysis and power spectrum) estimated longer activation onset compared with the other two strategies in the time domain, which we associate with lower-frequency movement in the maintaining of sitting stability. In addition, no correlation was found for the muscle activation sequence nor for the estimated delay value, which is most likely caused by motion redundancy and different stabilization strategies in each participant. The estimated time delay can be used in developing a sensory motor control model of the body. It not only can help therapists and biomechanics to understand the underlying mechanisms of body, but also can be useful in developing assistive technologies based on their stability mechanism. Full article

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. Full article

In environment sound classification, log Mel band energies (MBEs) are considered as the most successful and commonly used features for classification. The underlying algorithm, fast Fourier transform (FFT), is valid under certain restrictions. In this study, we address these limitations of Fourier transform and propose a new method to extract log Mel band energies using amplitude modulation and frequency modulation. We present a comparative study between traditionally used log Mel band energy features extracted by Fourier transform and log Mel band energy features extracted by our new approach. This approach is based on extracting log Mel band energies from estimation of instantaneous frequency (IF) and instantaneous amplitude (IA), which are used to construct a spectrogram. The estimation of IA and IF is made by associating empirical mode decomposition (EMD) with the Teager–Kaiser energy operator (TKEO) and the discrete energy separation algorithm. Later, Mel filter bank is applied to the estimated spectrogram to generate EMD-TKEO-based MBEs, or simply, EMD-MBEs. In addition, we employ the EMD method to remove signal trends from the original signal and generate another type of MBE, called S-MBEs, using FFT and a Mel filter bank. Four different datasets were utilised and convolutional neural networks (CNN) were trained using features extracted from Fourier transform-based MBEs (FFT-MBEs), EMD-MBEs, and S-MBEs. In addition, CNNs were trained with an aggregation of all three feature extraction techniques and a combination of FFT-MBEs and EMD-MBEs. Individually, FFT-MBEs achieved higher accuracy compared to EMD-MBEs and S-MBEs. In general, the system trained with the combination of all three features performed slightly better compared to the system trained with the three features separately. Full article

The GPR signals appear nonlinear and nonstationary during propagation. To evaluate the nonstationarity, the empirical mode decomposition (EMD) and its modifications have been proposed to localize the variations of energy and frequency components over time. Among the EMD−based algorithms, the variational mode decomposition (VMD) is one of the representative methods. It eliminates the drawbacks of EMD, to some extent, but is still executed in one dimension. In this work, the multivariate variational mode decomposition (MVMD) algorithm is introduced for decomposing the GPR B-scans into several IMF-slices in two dimensions, which inherits the advantages of the VMD and considers the stratigraphic constraints. Then, by applying the Teager–Kaiser energy operator (TKEO) within each IMF-slice, a novel energy attribute is formed and termed as the “TKEO-slices”. The proposed TKEO-slices can localize the energy attribute of geophysical information of different scales with good stratigraphic continuity. The proposed scheme is evaluated by the synthetic benchmark, model data, and field data. Compared with the VMD−based scheme and the classic instantaneous amplitude, the proposed TKEO-slices show better resolution and lateral continuity. Full article

Rolling bearings are key components that support the rotation of motor shafts, operating with a quite high failure rate among all the motor components. Early bearing fault diagnosis has great significance to the operation security of motors. The main contribution of this paper is to illustrate Gaussian white noise in bearing vibration signals seriously masks the weak fault characteristics in the diagnosis based on the Teager–Kaiser energy operator envelope, and to propose improved TKEO taking both accuracy and calculation speed into account. Improved TKEO can attenuate noise in consideration of computational efficiency while preserving information about the possible fault. The proposed method can be characterized as follows: a series of band-pass filters were set up to extract several component signals from the original vibration signals; then a denoised target signal including fault information was reconstructed by weighted summation of these component signals; finally, the Fourier spectrum of TKEO energy of the resulting target signal was used for bearing fault diagnosis. The improved TKEO was applied to a vibration signal dataset of run-to-failure rolling bearings and compared with two advanced diagnosis methods. The experimental results verify the effectiveness and superiority of the proposed method in early bearing fault detection. Full article

This research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process consists of four parts: time divisions of data, Teager–Kaiser energy operator (TKEO), the conventional EMG feature extraction (i.e., the mean absolute value (MAV), zero crossings (ZC), slope-sign changes (SSC), and waveform length (WL)), and eight major machine learning models (i.e., decision tree (medium), decision tree (fine), k-Nearest Neighbor (KNN) (weighted KNN, KNN (fine), Support Vector Machine (SVM) (cubic and fine Gaussian SVM), Ensemble (bagged trees and subspace KNN). Then, we compare and investigate 48 classification models (i.e., 47 models are proposed, and 1 model is the conventional) based on five healthy subjects. The results showed that all the classification models achieved accuracies ranging between 74–98%, and the processing speed is below 40 ms and indicated acceptable controller delay for robotic arm control. Moreover, we confirmed that the classification model with no time division, with TKEO, and with ensemble (subspace KNN) had the best performance in accuracy rates at 96.67, recall rates at 99.66, and precision rates at 96.99. In short, the combination of the proposed TKEO and ensemble (subspace KNN) plays an important role to achieve the EMG classification. Full article

As a vital component widely used in the industrial production field, rolling bearings work under complicated working conditions and are prone to failure, which will affect the normal operation of the whole mechanical system. Therefore, it is essential to conduct a health assessment of the rolling bearing. In recent years, Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is applied to the fault feature extraction for rolling bearings. However, the algorithm still has the following problems: (1) The selection of fault period T depends on prior knowledge. (2) The accuracy of signal denoising is affected by filter length L. To solve the limitations, an improved MOMEDA (IMOMEDA) method is proposed in this paper. Firstly, the envelope harmonic-to-noise ratio (EHNR) spectrum is adopted to estimate the fault period of MOMEDA. Then, the improved grid search method with EHNR spectral entropy as the objective function is constructed to calculate the optimal filter length used in the MOMEDA. Finally, a feature extraction method based on the improved MOMEDA (IMOMEDA) and Teager-Kaiser energy operator (TKEO) is applied in the field of rolling bearing fault diagnosis. The effectiveness and generalization performance of the proposed method is verified through comparison experiment with three data sets. Full article

The determination of ultrasonic echo signal onset time is the core of performing the time difference method to calculate wind speed. However, in practical cases, background noise makes precise determination extremely difficult. This paper carries out research on the accurate determination of onset time, exploring the advantages of an improved method based on the combination of Hilbert-Huang Transform (HHT) and high-order statistics (kurtosis). Performing Hilbert-Huang Transform to the received wave is aimed at determining a rough arrival time, around which a fixed size of data is extracted as initial sample to avoid a false pick. Then the fourth-order kurtosis of a smaller sample, extracted successively by a moving window from the initial sample, is calculated. The minimum point corresponds to the initial onset time. This approach was tested on a real ultrasonic echo signal dataset, acquired in a wind tunnel with an ultrasonic anemometer. The proposed method showed satisfying results in both ideal cases and low signal-to-noise ratio (SNR) environment, compared with traditional onset time determination approaches, including Akaike Information Criterion (AIC-picker), Short-term Average over Long-term Average (STA/LTA), and Teager-Kaiser energy operator (TKEO). The experimental results acquired by the HHT-kurtosis method demonstrated that the proposed method possesses a high accuracy. Full article

Neuromuscular electrical stimulation (NMES) is useful for muscle strengthening and for motor restoration of stroke patients. Using a portable ultrasound instrument, we developed an M-mode imaging protocol to visualize contractions elicited by NMES in the quadriceps muscle group. To quantify muscle activation, we performed digital image processing based on the Teager–Kaiser energy operator. The proposed method was applied for 35 voluntary patients (18 women and 17 men), of 63.8 ± 14.1 years and body mass index (BMI) 30.2 ± 6.70 kg/m² (mean ± standard deviation). Biphasic, rectangular electric pulses of 350 µs duration were applied at two frequencies (60 Hz and 120 Hz), and ultrasound was used to assess the sensory threshold (ST) and motor threshold (MT) amplitude of the NMES signal. The MT was 23.4 ± 4.94 mA, whereas the MT to ST ratio was 2.69 ± 0.57. Linear regression analysis revealed that MT correlates poorly with body mass index (R² = 0.004) or with the thickness of the subcutaneous adipose tissue layer that covers the treated muscle (R² = 0.013). Our work suggests that ultrasound is suitable to visualize neuromuscular reactivity during electrotherapy. The proposed method can be used in the clinic, enabling the physiotherapist to establish personalized treatment parameters. Full article