Harmonic Interference Research Articles

Rolling bearing weak fault detection using transient structure‐optimal VMD and adaptive group sparse coding

AbstractRolling bearings are essential parts in machine equipment and detecting damage in the early stage is crucial for ensuring the safe production and machine life. However, it is difficult to extract weak fault features under strong background noise, discrete harmonic frequency interference and non‐stationary service conditions. This investigation proposes a hybrid fault diagnosis approach utilizing transient structure‐optimal variational mode decomposition (TS‐OVMD) and adaptive group sparse coding (AGSC) for addressing the formidable problem. According to the singular value structure between transient signal and the interference signal, this work investigates the singular value shrinkage (SVS) technique to adaptively obtain the independent components number. Then, we present a transient structure measure (TSM) to adaptively optimize the balance factor. This measure index systematically quantifies the typical characteristics of the bearing fault signal, which can maximize the fault information representation and effectively reduces the useful information loss caused by improper selection of VMD parameters. Finally, a sparse coding model called AGSC is furthermore designed to enhance the fault impulses readability and suppress residual noise based on the sparsity within group property and the TSM. The proposed approach is verified using experimental data and is found to be superiority comparison with the state‐of‐the‐art method.

The Influence of Harmonic Interference from High-Voltage Power Lines on the Noise Immunity of the Receiver of the Continuous Automatic Locomotive Signaling Channel

The Influence of Harmonic Interference from High-Voltage Power Lines on the Noise Immunity of the Receiver of the Continuous Automatic Locomotive Signaling Channel

Investigation of Harmonic Response in Non-Premixed Swirling Combustion to Low-Frequency Acoustic Excitations

The propagation mechanism of flow disturbance under acoustic excitations plays a crucial role in thermoacoustic instability, especially when considering the effect of non-premixed combustion on heat release due to reactant mixing and diffusion. This relationship leads to a complex coupling between the spatial distribution of the equivalence ratio and the propagation mechanism of flow disturbance. In the present study, the response of a methane-air non-premixed swirling flame to low-frequency acoustic excitations was investigated experimentally. By applying Proper Orthogonal Decomposition (POD) analysis to CH* chemiluminescence images, the harmonic flame response was revealed. Large Eddy Simulation (LES) was utilized to analyze the correlation between the vortex motion within the shear layers and the harmonic response under non-reacting conditions at excitation frequencies of 20 Hz, 50 Hz, and 150 Hz. The results showed that the harmonic flame response was mainly due to the harmonic velocity pulsations within the shear layers. The acoustically induced vortices within the shear layer exhibited motion patterns susceptible to harmonic interference, with spatial distribution characteristics closely related to the oscillation modes of the non-premixed combustion.

MAE-Based Self-Supervised Pretraining Algorithm for Heart Rate Estimation of Radar Signals.

Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods still require longer observation times. Most deep learning methods related to heart rate estimation still need to collect more heart rate marker data for training. To address the above problems, this paper introduces a radar signal-based heart rate estimation network named the "masked phase autoencoders with a vision transformer network" (MVN). This network is grounded on masked autoencoders (MAEs) for self-supervised pretraining and a vision transformer (ViT) for transfer learning. During the phase preprocessing stage, phase differencing and interpolation smoothing are performed on the input phase signal. In the self-supervised pretraining step, masked self-supervised training is performed on the phase signal using the MAE network. In the transfer learning stage, the encoder segment of the MAE network is integrated with the ViT network to enable transfer learning using labeled heart rate data. The innovative MVN offers a dual advantage-it not only reduces the cost associated with heart rate data acquisition but also adeptly addresses the issue of respiratory harmonic interference, which is an improvement over conventional signal processing methods. The experimental results show that the process in this paper improves the accuracy of heart rate estimation while reducing the requisite observation time.

One-Step Discrete Fourier Transform-Based Sinusoid Frequency Estimation under Full-Bandwidth Quasi-Harmonic Interference

The estimation of the frequency of sinusoids has been the object of intense research for more than 40 years. Its importance in classical fields such as telecommunications, instrumentation, and medicine has been extended to numerous specific signal processing applications involving, for example, speech, audio, and music processing. In many cases, these applications run in real-time and, thus, require accurate, fast, and low-complexity algorithms. Taking the normalized Cramér–Rao lower bound as a reference, this paper evaluates the relative performance of nine non-iterative discrete Fourier transform-based individual sinusoid frequency estimators when the target sinusoid is affected by full-bandwidth quasi-harmonic interference, in addition to stationary noise. Three levels of the quasi-harmonic interference severity are considered: no harmonic interference, mild harmonic interference, and strong harmonic interference. Moreover, the harmonic interference is amplitude-modulated and frequency-modulated reflecting real-world conditions, e.g., in singing and musical chords. Results are presented for when the Signal-to-Noise Ratio varies between −10 dB and 70 dB, and they reveal that the relative performance of different frequency estimators depends on the SNR and on the selectivity and leakage of the window that is used, but also changes drastically as a function of the severity of the quasi-harmonic interference. In particular, when this interference is strong, the performance curves of the majority of the tested frequency estimators collapse to a few trends around and above 0.4% of the DFT bin width.

Minimum structure of high-order harmonic spectrum from molecular multi-orbital effects involving inner-shell orbitals.

The spectral features of high-order harmonic spectra can provide rich information for probing the structure and dynamics of molecules in intense laser fields. We theoretically study the high harmonic spectrum with the laser polarization direction perpendicular to the N2O molecule and find a minimum structure in the plateau region of the harmonic spectrum. Through analyzing the time-dependent survival probability of different electronic orbitals and the time-dependent wave packet evolution, it is found that this minimum position is caused by the harmonic interference of HOMO a, HOMO-1, and HOMO-3 a orbitals. Moreover, this interference minimum is discovered over a wide frequency range of 0.087a.u. to 0.093a.u., as well as a range of driving laser intensities with peak amplitudes between 0.056 a.u. and 0.059 a.u.. This study sheds light on the multi-electron effects and ultrafast dynamics of inner-shell electrons in intense laser pulses, which are crucial for understanding and controlling chemical reactions in molecules.

Noise Characteristics and Denoising Methods of Long-Offset Transient Electromagnetic Method

The advantages of the long-offset transient electromagnetic method include deep detection and sensitive response to resistivity anomalies. It is widely used in underground mineral resources exploration, fluid identification in petroleum reservoirs, hydraulic fracturing, and dynamic residual oil and gas monitoring. After the primary field signal is turned off, grounded electrodes or coils are used to observe the secondary eddy field. The secondary field signal decays quickly and has a large dynamic range and a wide frequency band but is easily affected by various natural and human electromagnetic interferences. Therefore, noise reduction and distortion correction are important issues in the processing of transient electromagnetic data. This paper proposes a systematic noise interference suppression process. Multi-period and positive–negative bipolar signal stackings were used to remove random noise and suppress DC offset signals. Then, a time-domain inverse digital recursive method was applied to remove characteristic frequency signals, e.g., power frequency signals and their harmonic interference. A standard noise-free signal was constructed through forward modeling simulation and verified by adding different types of noise. Finally, high-quality transient electromagnetic secondary field attenuation signals were obtained through overlapping windowing technology. We applied this algorithm to obtain electromagnetic data from dynamic monitoring of hydraulic fracturing in Fuling shale gas and from a copper–iron metal mine in Daye City, demonstrating its effectiveness.

An enhanced K-SVD denoising algorithm based on adaptive soft-threshold shrinkage for fault detection of wind turbine rolling bearing

Due to nonstationary operating conditions of wind turbines and surrounding harsh working environments, the impulse features induced by bearing faults are always overwhelmed by heavy noise, which brings challenges to accurately detect rolling bearing faults. Sparse representation exhibits excellent performance in nonstationary signal analysis, but it is closely bound up with the degree of similarity between the atoms in a dictionary and signals. Therefore, this paper investigates an enhanced K-SVD denoising method based on adaptive soft-threshold shrinkage to achieve high-precision extraction of impulse signals, and applies it to fault detection of generator bearing of wind turbines. An adaptive sparse coding shrinkage soft-threshold denoising is first proposed to remove noise and harmonic interference in the residual term of dictionary updating, so that the updated atoms show obvious impact characteristics. Furthermore, a soft-threshold shrinkage function with adaptive threshold is designed to further suppress clutter in atoms of the learned dictionary, so as to obtain an optimized dictionary for recovering impulse signals. Two actual engineering cases are selected for analysis, and the envelope spectrum correlation kurtosis corresponding to the results obtained by the proposed method is significantly higher than that of other comparison methods, thus verifying its superiority in detecting rolling bearing faults.

A novel time delay‐based phase‐locked loop with improved anti‐harmonic interference performance for grid synchronization

SummaryThe time delay unit‐based PLLs (TD‐PLLs) are widely used in grid synchronization in single‐phase power systems. However, the TD‐PLL is vulnerable to frequency variations and harmonic disturbances, leading to grid synchronization failures. Analysis shows that the performance degradation is due to the inability to obtain the variation of the input signal frequency. This paper proposes a new time delay‐based phase‐locked loop called immune to double frequency component TD‐PLL (IDFC‐TD‐PLL) based on a feedback structure to accurately capture the variation of input signal frequency, which can improve the dynamic performance and the performance against harmonic interference. Simulations and experiments have evaluated the performance of the proposed phase‐locked loop, demonstrating its superior accuracy and dynamic performance compared to the phase‐locked loops referred in the paper.

Sustainable irrigation of pipeline fluid flow rate regulation based on traveling wave magnetic field

To adjust the flow rate of liquid in the pipeline in real-time in the process of crop drip irrigation, to achieve sustainable agricultural irrigation. This paper designs and develops a kind of liquid speed-regulating device for drip irrigation based on traveling wave magnetic field. This paper aims to use the electromagnetic driving force generated by the traveling wave magnetic field to realize the soft control of liquid flow rate in drip irrigation pipelines. First of all, the structure of the combined traveling wave magnetic field generator was designed. COMSOL physical field simulation software was used to build the traveling wave magnetic field simulation model, and the magnetic field distribution diagram and the variation curve of the magnetic field intensity were generated in the time domain. Then, through the analysis of the simulation model, the harmonic interference of a single row and combined traveling-wave magnetic field generator is compared, and the optimal spacing between the traveling-wave magnetic field generator units is determined. Finally, the water-fertilizer fusion liquid used in the process of agricultural irrigation was selected and the liquid speed regulation experiment was carried out. The experimental results show that the combined liquid speed regulating device can effectively reduce the number of traveling wave field harmonics, compared with the single wave field generator at the same power, the magnetic field intensity is increased by 33.9 %. When the excitation current is 2 A and the initial flow rate is 0.5 m/s, the device can increase the flow rate of water-fertilizer fusion liquid for agricultural drip irrigation by 2.27 % and decrease by 2.13 %, which can meet the real-time and effective speed regulation requirements of water-fertilizer fusion liquid in the process of agricultural drip irrigation.

Boost power factor correction converter with adaptive harmonic compensation control

AbstractIn low‐voltage distribution systems, the increased penetration of power electronic devices leads to the decentralization of harmonic sources, which puts the sensitive loads or communication systems in the risk status. The conventional methods of point‐to‐point harmonic control are not applicable. In order to solve this problem without increasing additional investments, this paper focuses on mitigating the harmonic interference through the power factor correction (PFC) converter. Hence, a critical conduction mode (CRM) single‐phase boost PFC converter with adaptive harmonic compensation (AHC) control is proposed to reduce the harmonic interference at user side. Firstly, the feed‐forward control loop of a PFC converter is decoupled into the fundamental branch and harmonic branch to trace the harmonic interference and generate the compensation current. Then the compensation gain is designed by the compensation loop gain and compensation capacity of PFC converter. Meanwhile, the characteristics of the PFC converter are analyzed to study the effect of the AHC control on the active power transmission and output load, which can provide important support for the proposed method in application. Finally, a 160‐W experimental prototype is built to verify the effectiveness of the proposed AHC control.

Low-Complexity Harmonic Cancellation Scheme Based on Digital Multitone Mixing for Short-Wave Power Amplifier

In short-wave (SW) communication system, the harmonics of the power amplifier (PA), which may fall within the transmission range, are the principal consideration of harmonic interference. In the process of harmonic cancellation (HC), the number of sampling points always needs to be large enough when capturing multiple harmonics. It directly leads to the increase of modeling complexity. In order to solve this problem, this letter proposes a low-complexity HC scheme based on digital multitone mixing (DMTM), which uses multitone signals to mix harmonic signals into a narrow range for processing. The experimental results show that the multiple harmonics can be canceled to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> 69 dBc in the proposed HC scheme. Also, the complexity is only 3.3% and 1.7% of the full-band HC scheme when the carrier frequencies are 5 and 8 MHz.

A Novel Method for Bearing Fault Diagnosis under Variable Speed Based on Envelope Spectrum Fault Characteristic Frequency Band Identification.

Rolling element bearing (REB) vibration signals under variable speed (VS) have non-stationary characteristics. Order tracking (OT) and time-frequency analysis (TFA) are two widely used methods for REB fault diagnosis under VS. However, the effect of OT methods is affected by resampling errors and close-order harmonic interference, while the accuracy of TFA methods is mainly limited by time-frequency resolution and ridge extraction algorithms. To address this issue, a novel method based on envelope spectrum fault characteristic frequency band identification (FCFBI) is proposed. Firstly, the characteristics of the bearing fault vibration signal's envelope spectrum under VS are analyzed in detail and the fault characteristic frequency band (FCFB) is introduced as a new and effective representation of faults. Then, fault templates based on FCFB are constructed as reference for fault identification. Finally, based on the calculation of the correlation coefficients between the envelope spectrum and fault templates in the extended FCFB, the bearing fault can be diagnosed automatically according to the preset correlation coefficient criterion. Two bearing VS experiments indicate that the proposed method can achieve satisfactory diagnostic accuracy. The comparison of OT and TFA methods further demonstrates the comprehensive superiority of the proposed method in the overall consideration of accuracy, diagnostic time, tachometer dependency, and automatic degree.

Optimal control method of induction motor torque ripple based on harmonic component

AbstractThe torque ripple of an induction motor has strong harmonic interference, which causes stator current distortion. Stability control is always a difficult problem in this field. An optimal control method for the torque ripple of induction motors based on harmonic components is proposed. To address the instability and significant energy loss caused by torque ripple in induction motors, an optimization control method for torque ripple in induction motors based on harmonic components is studied and designed. On the basis of the induction motor model, according to the characteristics and generation principle of torque ripple, combined with controllable and uncontrollable parts of harmonic torque, the stator current harmonics are used to suppress torque harmonics and minimize torque ripple. For the purpose of to cancel the harmonic component of the output voltage, achieve digital output voltage filtering, reduce the harmonic component of the stator current, and achieve optimal control of the torque ripple of the induction motor, the resonant digital filter is simultaneously deployed at the controller's output. The test results showed that the maximum torque ripple value was 2 Nm and the minimum torque ripple value was 0.1 Nm, which can restrain the distortion of stator current to the greatest extent and reduce the harmonic content of torque. In summary, the optimization control method proposed in the study can effectively suppress harmonic interference, which has important practical implications for future harmonic research.

Waveform-to-Waveform End-to-End Learning Framework in a Seamless Fiber-Terahertz Integrated Communication System

Seamless fiber-terahertz integrated communication has emerged as a promising technology in the special field of 6G, including mobile fronthaul and wireless bridges. Electronic terahertz communication systems have the advantages of high-level integration, small form factors, and potentially low costs, but their drawbacks are their small bandwidths and high harmonic interference levels. In this paper, an end-to-end learning-based waveform-to-waveform automatic equalization framework (W2WAEF) is proposed to overcome the above shortcomings in a seamless fiber-terahertz integrated communication system. An attention-based three-tributary heterogeneous neural network (ATTH) is designed to simulate the channel model of a seamless fiber-wireless communication system, taking fiber optics, optical-to-electrical conversion, and electrogenerated terahertz waves into account. With the proposed method, a data rate of 80.78 Gbps is experimentally demonstrated for discrete multitone modulation (DMT) signals over 5 km of fiber and 1 m of 209-GHz terahertz signals under a 20% soft decision-forward error correction (SD-FEC) threshold of 2.4 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> . Compared with an approach without preprocessing, a receiver sensitivity gain exceeding 1.3 dB is successfully achieved at a data rate of 60 Gbps. The proposed method is a promising scheme for meeting the high speed and low-cost demands of future seamless fiber-terahertz integrated communication systems.

An Optimization of Least-Square Harmonic Phasor Estimators in Presence of Multi-Interference and Harmonic Frequency Variance

The wide application of power electronic devices brings an increasing amount of undesired harmonic and interharmonic tones, and accurate harmonic phasor estimation under a complex signal input is an important task for smart grid applications. In this paper, an optimization of least-square dynamic harmonic phasor estimators, considering multi-interference and harmonic frequency variance, is proposed. A comprehensive error index (CEI) composed of the fundamental-leakage-led harmonic amplitude estimation error, harmonic mutual interference, out-of-band interference, and harmonic frequency deviation is employed. The largest CEI part of least-square algorithms using three different signal decomposition models is analyzed for the first time, and variables to reduce this error component are then introduced using singular value decomposition. With the CEI and defined variables, a minimum-error estimation of harmonic phasors under various interference and harmonic frequency change is discussed. Numerical tests are performed, and the test results show that after the proposed optimization is applied to least-square algorithms, the harmonic phasor estimation errors are considerably reduced, especially for low-order harmonics. We also show the possibility of choosing desired optimal phasor filter design by balancing the measurement accuracy and data latency. For example, when the window length is set to three nominal cycles, the proposed optimization can yield both good accuracy and fast measurement speed for estimating harmonic phasors under multi-interference and harmonic frequency variance.

Improved Single-phase PLL Based on All-Pass Filter

The detection accuracy of a conventional phase-locked loop for a single phase is affected by the interference of the DC component and harmonic component when the grid has a lot of problems such as harmonic interference and DC offset, and It will be unable to accurately track the grid voltage frequency and phase. The traditional APF-PLL is improved to address this problem. An improved phase-locking algorithm is proposed, combining a CSOGI and a FLL with the conventional single-phase phase-locked loop. The method incorporates CSOGI into the APF-PLL pre-stage to reduce interference from DC and harmonic components, while FLL achieves the frequency tracking of the input signal and improves the adaptivity and dynamic response speed of the phase-locked loop. Through simulation verification, the proposed new phase-locked loop can improve the frequency tracking capability, dynamic performance, and precision of the locking phase of the PLL.

Adaptive Nonlinear Least Squares Framework for Contactless Vital Sign Monitoring

The respiratory and heart rates are critical physiological parameters, and conventional contact-based monitoring techniques may cause discomfort and epidermal damage, being therefore inadequate for long-term monitoring. Despite recent advances, accurate contactless vital sign monitoring is still challenging in practical scenarios, especially in relation to heart rate estimation. In this work, we propose a comprehensive framework for vital sign processing in frequency-modulated continuous-wave radar systems and evaluate its performance with real data imitating common working conditions in an office environment. First, to improve the signal-to-noise ratio before estimation, we propose a novel slow-time phase correlation processing, which allows early integration of the vital sign energy at nearby range bins. Subsequently, we present an adaptive nonlinear least squares framework that explores the harmonic structure existing in the recovered displacement signal. An additional Kalman filter stage is designed to select among multiple estimates from different search regions, thus conferring adaptivity and robustness against harmonic interference and noise. This approach largely provides estimates within the predefined error intervals, being capable of tracking the true breathing and heart rate values even during continuous small body movements. The final accuracy and root mean square error values have shown enhanced estimation, outperforming conventional spectral estimation and other recently proposed methods in almost all scenarios.

Relay Protection Setting Calculation of Power Transformer Based on ETAP

The conventional relay protection setting calculation method considers the internal interference of the transformer and obtains the setting value quickly, which leads to large harmonic interference of the transformer and affects the secondary setting calculation results. Therefore, the setting calculation method of the power transformer relay protection based on the Electrical Transient Analysis Program (ETAP) is designed. The harmonic transfer characteristics of the power transformer are extracted, and the harmonic interference of the power transformer is proposed. Then the ETAP software is adopted to establish the calculation principle of the power transformer relay protection setting and improve the calculation accuracy of the secondary setting. Through comparative experiments, it is verified that the second fixed value of the algorithm is close to the actual fixed value and can be applied to real life.

Multi-scale dual domain network for nonlinear magnetization signal filtering in magnetic particle imaging

Magnetic particle imaging (MPI) realizes functional imaging by generating nonlinear response signals in the magnetic field through magnetic nanoparticles. MPI is a highly sensitive tracer-based imaging technology, which opens new possibilities for promising biomedical applications. However, in practice, the background signals, including random thermal noise from MPI receive chains and the harmonic interference caused by nonlinear components under a high-frequency excitation field, restrict the MPI performance. In this study, we propose a learning-based method by training a multi-scale dual-domain network to effectively filter the MPI signals. In the model, a multi-channel filtering module was designed to suppress the noise-related features in time and frequency domains. We constructed four different MPI signal datasets including simulated and measured noises acquired from a homemade MPI system to verify our method. The signal filtering tests were performed on synthetic and measured data. The experimental results indicated that our method can achieve the best performance among state-of-art signal filtering methods. Especially, on the dataset containing measured noise, our proposal improved the signal-to-noise ratio from 6.88 dB to 29.11 dB. Moreover, the percentage root means square difference was reduced from 51.26 to 3.96 and the root mean square error was reduced from 65.07×10-4 to 5.29×10-4.