Frequency Domain Research Articles

Hot rolled steel surface defect detection and classification using an automatic ensemble approach

Abstract This study introduces an ensemble-based Deep Neural Network (DNN) model for detecting defects on steel surfaces. The method suggested in this study classifies steel surface conditions into six possible fault categories, namely, crazing, inclusion, rolled in, pitted surface, scratches, and patches. The images undergo preprocessing and extraction of features in spatial and frequency domains using image segmentation techniques such as grey level difference method (GLDM), fast Fourier Transform (FFT), grey level co-occurrence matrix (GLCM), texture analysis and discrete wavelet transform (DWT). The ensembling of image features into a fused feature pool is carried out after the preprocessing of input images that are provided as input to a light-weight neural network model for training and testing. The performance of the model is comprehensively evaluated via an ablation study both before and after ensembling. In addition, the model capability is effectively analyzed using receiver operating characteristics (ROC) curve, confusion matrix from which classification accuracy of the model could be obtained and other parameters including precision and f1-score. It was observed that the proposed deep learning network presents phenomenally high accuracy of 99.72% for detection and classification of steel surface faults. This result was found to be superior when compared with the performance of the same neural network over each feature type individually. This study also compares the classification results of the model built based on the ensembled feature set with the results of various other classification approaches available in literature. The ensemble-based model could potentially be integrated into existing inspection systems for real-time, efficient and robust condition monitoring of steel surfaces.

Amplification in parametrically-driven resonators near instability based on Floquet theory and Green’s functions

Abstract Here we use Floquet theory to calculate the response of
parametrically-driven time-periodic systems near the onset of parametric
instability to an added external ac signal or white noise.
We provide new estimates, based on the Green's function method,
for the response of the system in the frequency domain.
Furthermore, we present novel expressions for the power and
noise spectral densities.
We validate our theoretical results by comparing our predictions for the
specific cases of a single degree of freedom parametric amplifier and of the
parametric amplifier coupled to a harmonic resonator with the
numerical integration results and with analytical approximate results obtained via
the averaging method up to second order.

Deep Learning-Based Multi-Feature Fusion for Communication and Radar Signal Sensing

Recent years witness the rapid development of communication and radar technologies, and many transmitters are equipped with both communication and radar functionalities. To keep track of the working state of a target dual-functional transmitter, it is crucial to sense the modulation mode of the emitted signals. In this paper, we propose a deep learning-based intelligent modulation sensing technique for dual-functional transmitters. Different from existing modulation sensing methods, which usually focus on communication signals, we take both communication and radar signals into consideration. Typically, the dominant features of communication signals lie in the time domain, while those of radar signals lie in both time and frequency domains. To enhance the sensing accuracy, we first exploit real and complex value convolution operations to extract both time-domain and frequency-domain features of emitted signals from the target transmitter. Then, we fuse the extracted features by assigning proper weights with the attention mechanism. Simulation results reveal that the proposed technique can improve the sensing accuracy by up to 4% on average compared with benchmarks.

Novel two-step kissing balloon inflation technique after bifurcation stenting under 3D-optical frequency domain imaging guidance.

Novel two-step kissing balloon inflation technique after bifurcation stenting under 3D-optical frequency domain imaging guidance.

A methodological integration of fisher score technique with intelligent machine learning methods for ball bearing fault investigation

This paper presents an efficient approach to detect, diagnose and estimate the severity of failures in various components of bearings in induction motors using vibration signature analysis. This automated method integrates the Fisher Score feature selection method and an efficient hyperparameter tuning model with machine learning models, including Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Decision Tree (DT), to accurately classify defects in bearings. This approach ensures accurate classification of bearing defects within less computation time. This work is carried out with vibration signals, recorded from a laboratory experimental setup on Machine Fault Simulator (MFS), focusing on ball bearing with defects in inner race, outer race and ball itself. Time and Frequency domain analysis are employed to compute the features for fault investigation in ball bearings using machine learning models. The computed results demonstrate that the proposed feature selection method with hyperparameter tuning achieved remarkable maximum accuracy among X, Y and XY combinations of datasets, with 97% in DT, 94% in SVM and 95.23% in k-NN models during the frequency domain analysis. Notably, these model accuracies improved to 99.04% in DT, 98% in SVM and 98% in k-NN during further analysis with Fisher Score technique. Consequently, the testing loss using this hyperparameter tuning function remains very low. Overall, this paper compares the results of time and frequency domain analysis and introduces a promising approach for both efficient and accurate fault detection and severity estimation in bearings of induction motors, potentially reducing the need for extensive manpower and sensor usage.

Exploring Motion Stability of a Novel Semi-Submersible Platform for Offshore Wind Turbines

The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems.

Predictors of late lumen enlargement after drug-coated balloon angioplasty for de novo coronary lesions.

Late lumen enlargement (LLE) - a positive remodelling phenomenon - after drug-coated balloon (DCB) angioplasty for stable coronary disease contributes to a lower restenosis rate. However, lesion characteristics promoting LLE remain unclear. This study aimed to investigate predictive lesion characteristics for LLE using serial optical frequency domain imaging (OFDI) following DCB angioplasty for de novo coronary artery lesions. This retrospective, single-centre observational study included patients with angina pectoris who underwent paclitaxel-coated balloon angioplasty without stenting under OFDI guidance as well as follow-up OFDI. OFDI endpoints were lumen volume, plaque phenotype, and procedure-associated dissection. LLE was defined as a ≥10% increase in the lumen volume of the treated lesion at follow-up. Between August 2016 and December 2019, among patients with successful DCB angioplasty, 108 lesions (83 patients) had available follow-up imaging after a median of 6.1 months. LLE was detected in 44 (40.7%) lesions. Fibrous/fibrocalcific and layered plaques had significantly larger lumen volumes at follow-up than immediately after the index procedure, whereas lipid plaques exhibited no significant difference. Medial dissection with an arc >90° revealed an increased lumen volume. Multivariate analysis showed that layered plaques (odds ratio [OR] 8.73, 95% confidence interval [CI]: 1.92-39.7; p=0.005) and medial dissection with an arc >90° (OR 4.65, 95% CI: 1.63-13.3; p=0.004) were independent LLE predictors. Layered plaques and extensive medial dissection after DCB angioplasty were associated with higher LLE occurrence in de novo coronary lesions. These findings may be clinically applicable to DCB therapeutic strategies based on plaque features.

Conceptual design of a high reactive-power ferroelectric fast reactive tuner

We present a novel design of a ferroelectric fast reactive tuner (FE-FRT) capable of modulating mega-VAR reactive power on a submicrosecond timescale. The high reactive power capability of our design extends the range of applications of reactive tuners to numerous applications. We present a detailed analytical model of the performance of a megawatt-class reactive power device and benchmark it against finite-element method eigenmode and frequency domain electromagnetic simulations. We introduce new features, including an annulus design for the ferroelectric capacitors and capacitive window coupling to the cavity. We consider thermal design issues and nonlinear effects in the ferroelectric. The model covers several configurations, allowing control of the frequency of superconducting and normal-conducting cavities in a variety of applications and frequencies. We calculate that the FE-FRT designed should be capable of handling around 0.45 MVAR of reactive power with around 3 kW of resistive losses, providing a frequency tuning range of 8 kHz in an example of 400 MHz cavity geometry. Published by the American Physical Society 2024

A short time series rolling bearing fault diagnosis method based on FMTF-CNN

Abstract The signal characteristics are extracted directly from the convolutional level when the Convolutional Neural Network (CNN) is used as a fault diagnosis method in most instances. Some scholars have proposed to use the Markov Transition Field (MTF) to extract the signal characteristics before convolution. However, for time-domain signals, the fault characteristics extraction is difficult, and the training process of the neural network parameters is slow. Therefore, in this paper, based on Fast Fourier Transform (FFT) and MTF algorithm, the Fourier Markov Transition Field (FMTF) characteristics extraction method in frequency domain is proposed, and a large number of experiments are carried on the public bearing data set of Jiangnan University. The final verification shows that the FMTF-CNN method has higher accuracy, faster training process and more accurate characteristics extraction than the traditional MTF-CNN diagnosis method.

Transmissibility-based operational modal analysis: A unified scheme and uncertainty quantification

The transmissibility-based Operational Modal Analysis (OMA) technique has attracted substantial research interest over the past two decades due to its capability to extract structural modal parameters in the presence of non-white excitation. The transmissibility function used in OMA can be categorized as Response Transmissibility (RT) and Power Spectral Density Transmissibility (PSDT). Modal parameters can be identified by combining RTs from multiple load cases or PSDTs from multiple transfer outputs. Nevertheless, the mathematical formulations and implementation procedures of the various RT-based and PSDT-based methods exhibit significant differences. Furthermore, the uncertainty associated with modal parameter estimates is rarely assessed in these methods. This work aims to propose a scheme that unifies various transmissibility-based OMA algorithms through a consistent parametric formulation. Within this scheme, a universal parametric model and a standardized implementation procedure are developed to accommodate two types of transmissibility in both time and frequency domains and six OMA algorithms; additionally, a generalized framework for quantifying the uncertainties associated with modal parameter estimates is provided. The proposed scheme is finally validated using a planar truss structure.

Changes in fatigue symptoms following an exercise-based rehabilitation programme for patients with Long-COVID

BackgroundThere is evidence to support COVID-19 rehabilitation programmes improving persistent COVID-19 symptoms; however, there is concern that therapies that include an exercise component may increase fatigue and post-exertional symptom exacerbation (PESE).ObjectivesTo determine the effect of a 6 week COVID-19 rehabilitation programme on fatigue and PESE in individuals with on-going COVID-19 symptoms.MethodsAfter a routine medical assessment, individuals with persistent COVID-19 symptoms were enrolled on a 6-week COVID-19 specific rehabilitation programme. The programme included symptom-titrated exercise, education, and self-management advice. Fatigue was assessed pre- and post-programme using the Functional Assessment Chronic Illness Therapy-Fatigue questionnaire (FACIT). Exercise capacity (Incremental and Endurance Shuttle Walking Test; ISWT/ESWT) and PESE (DePaul Symptom Questionnaire; DSQ) were also assessed pre- and post-programme. Composite scores were calculated for the frequency and severity domains of the DSQ.Results148 patients (median [IQR] age 59[49–72] years, 82 (60%) female, 81(54%) hospitalised) completed the COVID-19 rehabilitation programme. FACIT score reduced pre- to post-programme by a mean[CI] change of −5[−7, −4];p<0.01. Exercise capacity increased by 82[65, 99] m for the ISWT and 398[333, 462]seconds for the ESWT (n=148). PESE was assessed in 44 patients. The DSQ frequency and severity composite score improved by 20[13, 28] and 19[13, 26] points, respectively (p<0.01, n=44).ConclusionThis data demonstrates the potential benefits of a COVID-19 rehabilitation programme on improving fatigue, exercise capacity, and symptom exacerbation, in those with persistent COVID-19 symptoms.

Microstrip Quasi-Elliptic Absorptive Bandpass Filter with Ultra-Wide Reflectionless Range and Compact Size

Absorptive bandpass filters (ABPFs) are highly attractive in modern microwave communication systems due to their ability to internally absorb the harmful stopband RF-power reflections. This paper reports an approach to designing quasi-elliptic ABPFs with ultra-wide reflectionless range, enhanced selectivity, and compact size. The method is realized based on a fourth-order quasi-elliptic absorptive lowpass filter (ALPF) prototype with a simplified structure. This ALPF prototype exhibits both good impedance-matching over the whole normalized frequency domain and an adjustable transmission zero close to the passband. By applying an equivalent impedance transformer model, a coupled-line-based ABPF scheme is devised from the ALPF prototype, which eliminates conventional dispersive transmission line inverters, resulting in an ultra-wide reflectionless range and a compact size. Closed-form equations are derived to support the filter synthesis. A 2.45 GHz microstrip ABPF with 30% fractional bandwidth is designed for verification. The measured minimum in-band insertion loss is 0.83 dB and the reflectionless range of return loss better than 10 dB is from DC to 12.88 GHz. Both the upper and lower stopband suppression exceed 20 dB, with the upper stopband extending up to 6.80 GHz. The upper and lower out-of-band roll-off rates are 93.9 and 121.4 dB/GHz, respectively. The overall circuit size is 0.12 λg2.

Using frequency domain analysis to elucidate travel time reliability along congested freeway corridors

Travel time reliability (TTR) is an essential measure of service for traffic performance management, especially for congested freeway corridors. This paper proposes a systematic analytical framework to analyze the predictable and unpredictable variations in route TTR in corridor networks. More specifically, the predictable variation in route TTR is estimated with a deterministic fluid-based polynomial arrival queue (PAQ) model, while the unpredictable variation in route TTR is analyzed through the residual induced by the PAQ estimation model. Based on the output of the PAQ estimation model, a frequency-domain approach is proposed to decompose the observed time-domain travel time into underlying unobserved factors in different temporal resolutions. With the discrete-time Fourier transform method and the Butterworth low-pass filtering technique, it is capable of analytically uncovering different temporal scales of predictable variations in TTR, including the trend of day-to-day variations, the dynamics of within-day variations, as well as the stochasticity of within-period variations. Connecting with the unpredictable variations represented by the residual due to the PAQ approximation model, we can comprehensively elucidate TTR for congested freeway corridors. Case studies are conducted to show the effectiveness of the proposed frequency-domain approach in elucidating and measuring different contributing elements in TTR, and more specifically, the four components, i.e., the trend of day-to-day variations, the dynamics of within-day variations, the stochasticity of within-period variations, and the residual induced by the PAQ estimation model, account for 64.9 %, 20.2 %, 1.2 %, 13.7 % of variations in the route travel time, respectively, based on a case study with the PeMS dataset.

Identification of optimal accelerometer placement on trains for railway switch wear monitoring via multibody simulation

Accelerometers play a crucial role in the railway industry, especially in track monitoring. Traditionally, they are placed on the railway tracks or often on bridges to monitor the health and condition of the infrastructure. Recently, there has been an increased focus on using regular trains to monitor the condition of railway infrastructure. Often, the sensors are placed based on certain assumptions without much scientific evidence or support. This paper utilizes the multibody simulation software GENSYS to identify the optimal placement of accelerometers on a passenger train for monitoring railway switch wear. Switch wear profiles were generated systematically and used as input for the simulations, studying acceleration at a total of 93 locations distributed among the wheelsets, bogies, and carbody. Based on both time and frequency domain analyses, optimal sensor locations were identified, generally close to the first bogie or wheelset at the leading carbody. Accelerations generated by the wheelset passing the switch can also be captured in the carbody, but it is important to note that these are several orders lower in magnitude compared to the acceleration on the wheelset. If accelerometers are to be placed in the carbody, correct sensitivity must be chosen, and a high-pass filter should be applied to capture the acceleration signals associated with switch wear. The study confirms that there is a direct correlation between the depth of switch wear and the magnitude of the acceleration. It remains effective even under various curve radii and train speeds.

Frequency Domain Analysis and Determination for Spectrum Characteristics of Laser With External Phase Modulation

Frequency Domain Analysis and Determination for Spectrum Characteristics of Laser With External Phase Modulation

High-resolution frequency domain decomposition for modal analysis of bridges using train-induced free-vibrations

Modal parameters are structural inherent characteristics that can be applied for revealing performance of railway bridges. Free vibration signals generated by a passage of train are commonly utilized to estimate the modal parameters of railway bridges due to their higher signal-to-noise ratios compared to random vibrations caused by ambient loads. However, since free vibration signals rapidly decay over time, the available free-vibration data is typically short-time. When using the fast Fourier transform-based spectral estimation method for modal identification from short-time vibration data, a phenomenon known as spectral leakage occurs, leading to miss-identification of some structural modes. In this study, the classical frequency domain decomposition (FDD) is improved for modal identification of railway bridges, in which the higher resolution auto-power spectral density (PSD) and cross-PSD functions are calculated through the autoregressive (AR) model-based method. The AR model-based method improves both the smoothness and resolution of the PSD functions compared to the fast Fourier transform technique. These AR model-based PSD functions are then employed in the FDD process to facilitate frequency and mode shape identification while avoiding spurious noise modes. The proposed eigenvalue fitting technique is subsequently utilized to estimate damping ratios. Numerical simulation data as well as vibration data from an actual bridge are analyzed to validate the proposed method, with a comparison made to the Welch’s PSD-based method. The results demonstrate that the modified FDD approach enables more effective identification of structural modes, even in the presence of closely-spaced modes.

Exploring the Impact of Campus Landscape Visual Elements Combination on Short-Term Stress Relief among College Students: A Case from China

Although the effect of campus landscape space on stress relief among college students has been confirmed, few existing studies have considered the impact on stress recovery from the perspective of factor combination, and the key visual elements and the most effective combination of visual elements to relieve stress are still unclear. This study attempts to conduct a natural experiment within Chinese campuses, measuring physiological indicators of stress such as heart rate (HR), frequency domain index of heart rate variability (LF/HF), skin conductance level (SCL), skin temperature (SKT), and respiratory rate (RESP) using physiological instruments. It explored the effects of visual elements and their combinations in campus landscape spaces on short-term stress relief among college students through semantic segmentation, multifactorial analysis of variance, and post hoc multiple comparison methods. Research results demonstrate that the presence of water elements in the field of vision can effectively improve the stress relief effects of landscape spaces. Reasonable combinations of natural landscape elements and artificial landscape elements in the design can also effectively promote stress relief among students. Building facade area and sky area, water area and sky area, and plant species and pavement area are three combinations of factors with the strongest interactive effects. “Natural water scenery” and “exquisite artificial” are two campus landscape design patterns most conducive to short-term stress relief.

Chatter Stability of Orthogonal Turn-Milling Process in Frequency and Discrete-Time Domains

Abstract As the industry seeks better quality and efficiency, multitasking machine tools are becoming increasingly popular owing to their ability to create complex parts in one setup. Turn-milling, a type of multi-axis machining, combines milling and turning processes to remove material through simultaneous rotations of the cutter and workpiece with the translational feed of the tool. While turn-milling can be advantageous for large parts made of hard-to-cut materials, it also offers challenges in terms of surface form errors and process stability. Because tool eccentricity and workpiece rotation lead to more complexity in process mechanics and dynamics, traditional milling stability models cannot predict the stability of turn-milling processes. This study presents a mathematical model based on process mechanics and dynamics by incorporating the unique characteristics of the orthogonal turn-milling process to avoid self-excited chatter vibrations. A novel approach was employed to model time-varying delays considering the simultaneous rotation of the tool and workpiece. Stability analysis of the system was performed in both the semi-discrete time and frequency domains. The effects of eccentricity and workpiece speed on stability diagrams were demonstrated and validated through experiments. The results show that the tool eccentricity and workpiece speed alter the engagement geometry and delay in the regeneration mechanism, respectively, leading to significant stability diagram alterations. The proposed approach offers a comprehensive framework for the stability of orthogonal turn-milling and guidance for the selection of process conditions to achieve stable cuts with enhanced productivity.

Evaluation of the single-frequency variometric approach based on low-cost GNSS observations and different satellite combinations for detecting short-term dynamic behaviors

Abstract This study presents the capability of the single-frequency (SF) variometric approach (VA) technique with low-cost GNSS observations to detect short-term dynamic behaviors. Harmonic oscillations with amplitudes between 5 and 20 mm and frequencies between 0.3 and 5.0 Hz were generated employing a single-axis shake table to investigate the performance of the SF-VA technique in the Structural Health Monitoring (SHM) system. Besides, a Mw 6.9 Kobe, 1995 earthquake simulation was generated using the shake table to analyze the SF-VA performance for the Earthquake Early Warning (EEW) system. A low-cost u-blox ZED-F9P GNSS receiver and ANN-MB-00 patch antenna were used to collect GNSS observations at a 20 Hz sampling rate during the experiments. The observations were processed using the MATLAB-based open-source PPPH-VA software in real-time mode, considering eight different satellite combinations. The capability of the SF-VA technique to detect horizontal dynamic behaviors in real-time mode was investigated in the frequency and time domains, accepting the displacements from the Linear Variable Differential Transformer (LVDT) sensor as a reference. The results in the frequency domain demonstrate that the SF-VA technique with low-cost GNSS observations can successfully detect the peak frequency value of short-term harmonic oscillations up to 5 Hz. Moreover, time domain findings emphasize that the short-time dynamic oscillations can be determined with the SF-VA technique with an accuracy ranging from 0.8 to 6.4 mm. Earthquake simulation experiment results demonstrate that the strong ground motions caused by mega earthquakes can be determined at mm-level by the SF-VA method. The results of both experiments show that multi-GNSS observations contribute to the SF-VA technique considerably. Overall, the findings reveal that the SHM and EEW systems can be operated with low-cost GNSS receivers, and the natural frequency of the man-made structures and accurate displacement values of seismic waveforms can be determined in real-time with the SF-VA technique.

Surface Wear Monitoring System of Industrial Transformer Tap-Changer Contacts by Using Voice Signal

Surface wear of the tap-changer contacts of industrial transformers (due to frequent switching times) easily leads to operation failure of industrial transformers, which affects the safety and stability of the transmission network. In this paper, an intelligent voice signal monitoring system was proposed for the abnormal condition (surface wear) of tap-changer contacts. This monitoring system was composed of a voice signal acquisition system, voice analysis system and voice processing system. First, the voice signal of the tap-changer contacts was collected, and the collected voice signal was analyzed in the time domain and the frequency domain. Secondly, the characteristic curve of the voice signal was proposed, and the voice curve was compared with that of the normal operation state. In this case, the running state and surface wear abnormal situation of the tap changer could be monitored and determined, and the cause of the abnormal state could also be further analyzed. This method solved the surface wear problem of the tap changer in industrial transformers, which could be not monitored effectively in real time. This method improved the operational reliability of industrial transformers and had high economic and social benefits.