Frequency Domain Data Research Articles

Optical Characterization of Parasitic Motion in a Long-Stroke Shaker

Abstract We present an optical scheme to simultaneously characterize the cross-axis and rota- tional parasitic motions of a long-stroke shaker. By leveraging the geometric properties of a corner cube retroreflector mounted on the shaker load table, we independently sample pitch, yaw, and horizontal and vertical displacements with sampling rates above 10 kHz. We have applied our optical apparatus to a 400-mm-stroke shaker operated from 0.1 Hz to 100 Hz and present two forms of analysis: (A) Frequency-domain data for the four measured parasitic degrees of freedom, with the resulting implications for accelerometer calibration uncertainties, and (B) extracted trajectories of the shaker table, allowing visualization of the bowed linear guide below 0.5 Hz and higher harmonics and hysteresis above 10 Hz. Our findings demonstrate our optical measurement scheme to be an effective tool for the characterization of parasitic motion for long-stroke shakers. As an example, we determine the "gravity error" in accelerometer calibration with our shaker to be (1.3 ± 0.1) % times the acceleration amplitude at 0.1 Hz, providing a correction to reduce uncertainty from this effect by an order of magnitude.

Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG.

We aim to assess the severity of spatial neglect through detailing patients' field of view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading to inattention to the contralesional space. Commonly used Neglect detection methods like the Behavioral Inattention Test - Conventional (BIT-C) lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale (CBS) provides valuable clinical information, it does not detail the specific field of view affected in neglect patients. Building on our previously developed EEG-based Brain-Computer Interface (BCI) system, AREEN (AR-guided EEG-based Neglect Detection, Assessment, and Rehabilitation System), we aim to map neglect severity across a patient's field of view. We have demonstrated that AREEN can assess neglect severity in a patient-agnostic manner. However, its effectiveness in patient-specific scenarios, which is crucial for creating a generalizable plug-and-play system, remains unexplored. This paper introduces a novel EEG-based combined Spatio-Temporal Network (ESTNet) that processes both time and frequency domain data to capture essential frequency band information associated with spatial neglect. We also propose a field of view correction system using Bayesian fusion, leveraging AREEN's recorded response times for enhanced accuracy by addressing noisy labels within the dataset. Extensive testing of ESTNet on our proprietary dataset has demonstrated its superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, and 86.36% specificity. Additionally, we provide saliency maps to enhance model explainability and establish clinical correlations. These findings underscore ESTNet's potential combined with Bayesian fusion-based FOV correction as an effective tool for generalized neglect assessment in clinical setting.

Current signal analysis using SW-GAT networks for fault diagnosis of electromechanical drive systems under extreme data imbalance

Abstract In complex industrial environments, ensuring the safe operation and effective maintenance of electromechanical equipment is of paramount importance. Intelligent fault diagnosis based on deep learning is currently the most popular data-driven method. However, conventional intelligent fault diagnosis techniques face several challenges: (1) Most diagnostic models rely heavily on analyzing vibration signals. However, vibration sensors are difficult to deploy in space-constrained environments, and vibration signals are frequently contaminated by strong noise. (2) The prevalence of class imbalance between normal and fault data in equipment condition monitoring can lead to model over-reliance on information from a few classes. (3) Traditional diagnostic models presuppose data independence, neglecting the coupling relationships between data. To address the aforementioned issue, this paper proposes a Self-Weighted Graph Attention Networks (SW-GAT) based on motor stator current signal analysis, aimed at solving the fault diagnosis problem of critical transmission components in electromechanical systems under severely imbalanced data scenarios. Firstly, the raw current data is preprocessed using Stacked Auto-Encoders (SAE), and then the decoded current frequency-domain data is utilized to construct graphical data, thereby enhancing the non-common features and weak fault information in the current signals. Secondly, by introducing the graph pooling attention mechanism into GAT, the model can more effectively focus on useful fault feature information within the graph data. Finally, a novel interclass adjustment loss function is designed to adaptively adjust and balance class weights, enabling the model to pay greater attention to minority class samples and thereby improving the recognition accuracy for minority class faults. Validating the proposed method on two cases and comparing it with other advanced approaches, our method achieved the highest accuracy among the compared methods.

Hilbert-Huang Transform and machine learning based electromechanical analysis of induction machine under power quality disturbances

Monitoring and predicting Power Quality (PQ) is crucial for quickly minimizing risk, protecting induction machines (IMs), and increasing productivity. This paper proposes an electromechanical analytical framework for IM working with various PQ disturbances based on machine learning (ML). This work collects the motor characteristics and PQ data, motor vibration signals, and other sensor data to identify the failures. The complicated electromechanical data is broken down into intrinsic mode functions (IMFs) using the Hilbert-Huang Transform (HHT), which reveals the modes present in the signals. This frequency-domain data is retrieved, paired with time-domain characteristics, and used as input features for ML algorithms. Support Vector Machines (SVMs) trained on labeled datasets in which features extracted from PQ disturbances are used to categorize the disturbances into several groups. The PQ disturbances caused by voltage sags, swells, harmonics, and transients can all be efficiently classified using SVM classifiers, allowing for real-time determination of the kind of disturbance influencing the IM. The accuracy for the SVM in the proposed scheme is 97.2 %. The SVM method is trained with PQ data and classification results using MATLAB classifier and Python software.

Vibration Analysis of a Centrifugal Pump with Healthy and Defective Impellers and Fault Detection Using Multi-Layer Perceptron

Centrifugal pumps (CPs) are widely utilized in many different industries, and their operations are maintained by their reliable performance. CPs’ most common faults can be categorized as mechanical or flow-related faults: the first ones are often associated with damage at the impeller, while the second ones are associated with cavitation. It is possible to use computational algorithms to monitor both failures in CPs. In this study, two different problems in pumps, the defective impeller and cavitation, have been considered. When a CP is working in a faulty condition, it generates vibrations that can be measured using piezoelectric sensors. Collected data can be analyzed to extract time- and frequency-domain data. Interpreting the time-domain data showed that distinguishing the type of defect is not possible. However, indicators like kurtosis, skewness, mean, and variance can be used as input for the multi-layer perceptron (MLP) algorithm to classify pump faults. This study presents a detailed discussion of the vibration-based method outcomes, emphasizing the benefits and drawbacks of the multi-layer perceptron method. The results show that the suggested algorithm can identify the occurrence of different faults and quantify their severity during pump operation in real time.

Novel approaches to assessing brake squeal propensity: comparative evaluation of two index formulations

The persistence of brake squeal as a significant NVH issue within automotive brake systems necessitates the development of robust methods for assessing squeal propensity. This study introduces two novel squeal index formulations for assessment purposes and evaluates their performance against the formulation defined by the SAE J2521 standard. Experimental data essential for formulating the squeal index are collected on a controlled mass-sliding belt experiment. Experiments are performed at three different spring stiffness levels, and squeal index formulations are derived based on time and frequency domain data. The first formulation places particular emphasis on the time domain, explicitly considering the duration of individual squeal events. Conversely, the second formulation is based on the frequency response characteristics of the system, which considers the number of super-harmonic peaks in frequency spectra. To evaluate the performance and discriminatory capabilities of the newly developed squeal index formulations, extensive comparisons are made against the Squeal Index based on the test procedure described in SAE J2521. In conclusion, through meticulous analysis and interpretation of the experimental results, it is discerned that the squeal index formulation defined by time domain exhibits superior efficacy in discerning the nuanced effects of varying design parameters on the occurrence of squeal-like behavior.

DC Bus Capacitance Identification Method Based on Frequency Domain Data Drive for EV Charging System

DC Bus Capacitance Identification Method Based on Frequency Domain Data Drive for EV Charging System

Real-time lithology identification from drilling data with self & cross attention model and wavelet transform

Accurate lithology identification during drilling operations is pivotal for optimizing drilling efficiency and informed decision-making. Traditional methods, such as mud logging, Logging While Drilling (LWD), and Measurement While Drilling (MWD), often face challenges such as delays and inaccuracies, lacking consistent utilization of real-time engineering data across all drilling sites. Consequently, these methods frequently overlook the complex interactions of influencing factors, resulting in underutilized geological information that could significantly enhance operational outcomes. This paper introduces the SelfAttention-CrossWT (SACWT) model, an innovative approach that integrates self-attention and cross-attention mechanisms with Wavelet Transform (WT) to utilize real-time drilling parameters, including Weight on Bit (WOB) and Rate of Penetration (ROP). The SACWT model effectively harnesses both temporal and frequency-domain characteristics of drilling data, facilitating comprehensive extraction of latent patterns and dynamics, which substantially improves lithology identification accuracy. The output of the model is the real-time lithology classification at the drill bit's depth. The performance of the SACWT model is compared with Long Short-Term Memory (LSTM) networks and a Self-Attention (SA) model that does not utilize frequency-domain data. When applied to a dataset from the Volve oil field, the SACWT model achieved identification accuracies of 87% and 91% in two separate test wells, outperforming traditional methods. These results underscore the model's efficacy and the significant potential of utilizing real-time drilling data for enhanced lithological characterization. This breakthrough offers a robust framework for future exploration and development operations, providing a more precise, efficient, and cost-effective approach to geological analysis.

Anomaly Detection Method for Industrial Control System Operation Data Based on Time-Frequency Fusion Feature Attention Encoding.

Anomaly detection in industrial control system (ICS) data is one of the key technologies for ensuring the security monitoring of ICSs. ICS data are characterized as complex, multi-dimensional, and long-sequence time-series data that embody ICS business logic. Due to its complex and varying periodic characteristics, as well as the presence of long-distance and misaligned temporal associations among features, current anomaly detection methods in ICS are insufficient for feature extraction. This paper proposes an anomaly detection method named TFANet, based on time-frequency fusion feature attention encoding. Considering that periodic variations are more concentrated in the frequency domain, this method first transforms the time-domain data into the frequency domain, obtaining both amplitude and phase data. Then, these data, together with the original time-series data, are used to extract features from two perspectives: long-term temporal changes and long-distance associations. Finally, the six features learned from both the time and frequency domains are fused, and the feature weights are calculated using an attention mechanism to complete the anomaly classification. In multi-classification tasks on three ICS datasets, the proposed method outperforms three popular time-series models-iTransformer, Crossformer, and TimesNet-across five metrics: accuracy, precision, recall, F1 score, and AUC-ROC, with average improvements of approximately 19%, 37%, 31%, 35%, and 22%, respectively.

Field measurements of wind load effects in a photovoltaic single-axis tracker mounting rail

The demand for solar photovoltaic power installations has resulted in a highly competitive industry. Equipment suppliers are under pressure to reduce design margins for projects to be feasible. Single-axis trackers are widely used to increase yield and reduce the levelised cost of energy compared to fixed-tilt installations. Load coefficients on tracker arrays are usually quantified using model-scale studies conducted in atmospheric boundary layer wind tunnels. These dynamically sensitive structures have been known to suffer wind damage despite the use of modern design methods. Ongoing research into the support structure design is deemed important to ensure long-term reliability. The current study presents full-scale field data acquired over 109 days on an experimental one-in-portrait single-axis tracker facility located in the Western Cape, South Africa. The purpose of these measurements was to quantify wind load effects in two instrumented PV module mounting rails. Frequency domain data showed modal participation of both the torsional and bending modes of the torque tube. The effect of the bending mode was more pronounced for the rail furthest from a pile. Load coefficients calculated using experimental data were generally lower than those presented in ASCE 7-22. Fluctuating stresses at critical locations led to negligible fatigue damage.

Dynamic modelling and experimental validation of a dynamic track stabiliser vehicle–track spatially coupling dynamics system

A dynamic track stabiliser vehicle is an unconventional vehicle for maintaining the quality of ballasted tracks, and its dynamic performance has a significant impact on its working efficiency. For the first time, this paper proposes a comprehensive dynamic track stabiliser vehicle–track coupled vertical and lateral multi-body dynamics model based on classical vehicle–track coupling dynamics theory. In this model, detailed attention is given to the vertical, lateral, roll, yaw, and pitch motions of the components, including the vehicle body, bogies, wheelsets and stabilisers. The Shen–Hedrick–Elkins theory is employed to describe non-linear wheel–rail contact relationships between the bogie wheelsets and the rails, and three various methodologies are adopted to address complicated wheel–rail interaction problems between the stabiliser wheelsets and the rails. This comprehensive multi-body dynamics model enables a detailed investigation of the dynamic performance of the system under complex excitations, such as the lateral excitation of the stabilisers and vertical downward pressure exerted by hydraulic cylinders, especially the dynamic response of the stabilisers in the vibration environment of the complete vehicle. The dynamics model was fully validated by comparing its results with time- and frequency-domain data obtained from field tests. The results indicate that the model is capable of being used for analysis of the dynamic performance of dynamic track stabiliser vehicles.

Abnormal Pavement Condition Detection with Vehicle Posture Data Considering Speed Variations.

Pavement condition monitoring is an important task in road asset management and efficient abnormal pavement condition detection is critical for timely conservation management decisions. The present work introduces a mobile pavement condition monitoring approach utilizing low-cost sensor technology and machine-learning-based methodologies. Specifically, an on-board unit (OBU) embedded with an inertial measurement unit (IMU) and global positioning system (GPS) is applied to collect vehicle posture data in real time. Through a comprehensive analysis of both time domain and frequency domain data features for both normal and abnormal pavement conditions, feature engineering is conducted to identify how the most important features affect abnormal pavement condition recognition. Six machine learning models are then developed to identify different types of pavement conditions. The performance of different algorithms and the significance of different features are then analyzed. Moreover, the influence of vehicle speed on pavement condition assessment is further examined and classification models for different speed intervals are developed. The results indicate that the random forest (RF) model that considers vehicle speed achieves the best performance in pavement condition monitoring. The outcomes of the present work would contribute to cost-effective pavement condition monitoring and provide an important reference for pavement maintenance sectors.

Understanding the Fourier Transform

This presentation will focus on understanding the assumptions of the Fourier transform and how these assumptions affect the frequency domain data. We will discuss the inputs the software requires to process the Fourier transform and the outputs created from the transform. Also, we highlight why the frequency domain output of different software packages might not match.

AI in ECG: Validating an ambulatory semiology labeller and predictor

ObjectivesEarly prediction of epileptic seizures can help reduce morbidity and mortality. In this work, we explore using electrocardiographic (ECG) signal as input to a seizure prediction system and note that the performance can be improved by using selected signal processing techniques. MethodsWe used frequency domain analysis with a deep neural network backend for all our experiments in this work. We further analysed the effect of the proposed system for different seizure semiologies and prediction horizons. We explored refining the signal using signal processing to enhance the system's performance. ResultsOur final system using the Temple University Hospital’s Seizure (TUHSZ) corpus gave an overall prediction accuracy of 84.02 %, sensitivity of 87.59 %, specificity of 81.9 %, and an area under the receiver operating characteristic curve (AUROC) of 0.9112. Notably, these results surpassed the state-of-the-art outcomes reported using the TUHSZ database; all findings are statistically significant. We also validated our study using the Siena scalp EEG database. Using the frequency domain data, our baseline system gave a performance of 75.17 %, 79.17 %, 70.04 % and 0.82 for prediction accuracy, sensitivity, specificity and AUROC, respectively. After selecting the optimal frequency band of 0.8–15 Hz, we obtained a performance of 80.49 %, 89.51 %, 75.23 % and 0.89 for prediction accuracy, sensitivity, specificity and AUROC, respectively which is an improvement of 5.32 %, 10.34 %, 5.19 % and 0.08 for prediction accuracy, sensitivity, specificity and AUROC, respectively. ConclusionsThe seizure information in ECG is concentrated in a narrow frequency band. Identifying and selecting that band can help improve the performance of seizure detection and prediction. SignificanceEEG is susceptible to artefacts and is not preferred in a low-cost ambulatory device. ECG can be used in wearable devices (like chest bands) and is feasible for developing a low-cost ambulatory device for seizure prediction. Early seizure prediction can provide patients and clinicians with the required alert to take necessary precautions and prevent a fatality, significantly improving the patient’s quality of life.

STFT-TCAN: A TCN-attention based multivariate time series anomaly detection architecture with time-frequency analysis for cyber-industrial systems

Networks and industrial systems play a pivotal role in modern society, and their security has garnered increasing attention. Anomalies within industrial equipment may propagate through fault transmission, leading to a cascade of failures. Additionally, cyberattacks on equipment can result in significant losses. Therefore, in the realm of industrial and cyberspace domains, an effective multivariate time series anomaly detection system for monitoring equipment is instrumental in ensuring the healthy operation of the machinery. Nevertheless, detecting anomalies in numerous time series remains challenging, stemming from the absence of anomaly labels and the complexity of the data patterns. Existing algorithms predominantly concentrate on modeling within the time domain, falling short in fully leveraging the informative features present in frequency domain data, resulting in diminished detection performance. This paper introduces STFT-TCAN, a model for anomaly detection in time series that seamlessly integrates information from both time and frequency domains for extracting data features. Sliding windows and the Short Time Fourier Transform (STFT) are utilized to construct a frequency matrix, effectively amalgamating the characteristics of both time and frequency domains within the time series. Furthermore, the model employs Temporal Convolutional Networks (TCN) and Transformer attention mechanisms (which combined to form the TCAN module) to capture the features of multivariate time series, thereby resulting in heightened detection accuracy. The proposed model undergoes validation on six publicly available datasets, showcasing the superior performance of the STFT-TCAN model in comparison to current baseline methods. It adeptly extracts features from both frequency and time domains in sequential data, thereby achieving state-of-the-art performance in tasks related to anomaly detection in multivariate time series.

Chaotic Brillouin optical correlation domain analysis based on Simplex pulse coding

The signal-to-noise ratio (SNR) is a crucial metric for the evaluation of a stimulated Brillouin scattering (SBS) based sensing system. We propose and experimentally demonstrate chaotic Brillouin optical correlation domain analysis (BOCDA) using the Simplex coding technique for frequency-domain data processing to enhance the SNR. The pulse depletion in the chaotic coded-BOCDA is theoretically and experimentally analyzed, where the impact of cascaded SBS can be eliminated and then the anti-distortion capability is significantly improved. Compared to the single-pulse scheme, the signal-to-background noise ratio of the Brillouin gain spectrum is improved by 2.35 dB, and the data acquisition processing efficiency is improved by four times with the 15-bit Simplex pulse coding technique.

Modified error-in-constitutive-relation (MECR) framework for the characterization of linear viscoelastic solids

We develop an error-in-constitutive-relation (ECR) approach toward the full-field characterization of linear viscoelastic solids described within the framework of standard generalized materials. To this end, we formulate the viscoelastic behavior in terms of the (Helmholtz) free energy potential and a dissipation potential. Assuming the availability of full-field interior kinematic data, the constitutive mismatch between the kinematic quantities (strains and internal thermodynamic variables) and their “stress” counterparts (Cauchy stress tensor and that of thermodynamic tensions), commonly referred to as the ECR functional, is established with the aid of Legendre–Fenchel gap functionals linking the thermodynamic potentials to their energetic conjugates. We then proceed by introducing the modified ECR (MECR) functional as a linear combination between its ECR parent and the kinematic data misfit, computed for a trial set of constitutive parameters. The affiliated stationarity conditions then yield two coupled evolution problems, namely (i) the forward evolution problem for the (trial) displacement field driven by the constitutive mismatch, and (ii) the backward evolution problem for the adjoint field driven by the data mismatch. This allows us to establish compact expressions for the MECR functional and its gradient with respect to the viscoelastic constitutive parameters. For generality, the formulation is established assuming both time-domain (i.e. transient) and frequency-domain data. We illustrate the developments in a two-dimensional setting by pursuing the multi-frequency MECR reconstruction of (i) piecewise-homogeneous standard linear solid, and (b) smoothly-varying Jeffreys viscoelastic material.

Frequency Domain Template Subtraction Approach to Attenuate Maternal Electrocardiogram in Fetal Electrocardiogram.

We develop a frequency domain template subtraction approach to attenuate the maternal ECG in the abdominal ECG measured from pregnant women. The proposed approach was tested on five public fetal ECG datasets simultaneously measured with ECG from the fetal scalp. The method's performance was compared with the template subtraction approach in the time domain using the accuracy and association metrics. The accuracy was calculated by counting the number of fetal complexes in the processed data that coincided with the fetal complexes in the scalp fetal ECG. The association is quantified as the coherence between the processed data and the gold standard. The maximum coherence values calculated for each approach were compared using the paired t-test. Our results showed no difference in the accuracy between the frequency and time domain approach (p = 0.733). However, the association was higher between the frequency domain data and the gold standard compared to the template subtraction data and the gold standard (p = 0.049), indicating that the frequency domain approach yielded a signal that resembled that of the scalp ECG compared to the time domain approach.

Biomechanical Modelling of Porcine Kidney.

In this study, the viscoelastic properties of porcine kidney in the upper, middle and lower poles were investigated using oscillatory shear tests. The viscoelastic properties were extracted in the form of the storage modulus and loss modulus in the frequency and time domain. Measurements were taken as a function of frequency from 0.1 Hz to 6.5 Hz at a shear strain amplitude of 0.01 and as function of strain amplitude from 0.001 to 0.1 at a frequency of 1 Hz. Measurements were also taken in the time domain in response to a step shear strain. Both the frequency and time domain data were fitted to a conventional Standard Linear Solid (SLS) model and a semi-fractional Kelvin-Voigt (SFKV) model with a comparable number of parameters. The SFKV model fitted the frequency and time domain data with a correlation coefficient of 0.99. Although the SLS model well fitted the time domain data and the storage modulus data in the frequency domain, it was not able to capture the variation in loss modulus with frequency with a correlation coefficient of 0.53. A five parameter Maxwell-Wiechert model was able to capture the frequency dependence in storage modulus and loss modulus better than the SLS model with a correlation of 0.85.

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

AbstractFault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method.