Time Domain Analysis Of Signals Research Articles

A robust, deep learning-based analysis of time-domain signals for NMR spectroscopy

When analyzing the Free Induction Decay (FID) signal produced by nuclear magnetic resonance (NMR) spectroscopy, Fourier transforms (FT) are used to decompose time-domain signals arising from nuclear interactions. This transformation enables the extraction of frequency-domain information, allowing for the recognition of patterns within the generated NMR spectra. Most modern NMR processing software applies FT to generate the final spectra. Researchers process FID using various techniques, such as phase correction, windowing, and FT, to enhance the interpretation of the obtained spectra. This processing step requires careful consideration of the characteristics of the original data and can also be influenced by the researchers' experience, often making it time-consuming to produce reliable results. However, recent advancements in artificial intelligence, particularly deep learning, have demonstrated superior pattern recognition capabilities compared to humans in complex scenarios. These developments have been successfully applied to various aspects of NMR spectroscopy. In this study, we demonstrate that neural networks can replace FT in NMR spectroscopy, enabling robust and rapid prediction of spectra and peak lists from FID signals. Our results confirm that deep learning can efficiently process NMR data to generate final spectra. As a proof of concept, we present the resulting spectra, along with peak lists predicted by supplying only FID input to the deep learning algorithm. The generated peak lists can be considered as spectra with infinite resolution.

Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors

Trains used in urban mass passenger transit often have side entrance doors through which passengers can rapidly enter and exit the train. These doors are typically electrically powered and automated. Many incidents have occurred in which a passenger is trapped and injured while passing through the doors as they are closing. Existing solutions rely on sensitive-edge sensors and current signal peak detection in the time domain to detect door obstructions. However, these methods have notable limitations: sensors are expensive, and sensor failure can result in safety risks, while time-domain signal analysis is prone to noise, potentially leading to false peak detection. The proposed efficient and cost-effective method enhances safety by implementing the torque control of a DC motor which limits the door closing force to prevent potential injuries. In addition, it reduces reliance on traditional edge sensors, which are prone to failure and may result in undetected obstructions. By using a robust time–frequency domain approach, the system ensures more accurate detection, minimizing potential injury risks. An obstruction of the door causes a corresponding change in the motor current. These changes can be detected by using the discrete wavelet transform to decompose the current signal. The norm and peak of the current are used as obstacle detection features, and appropriate threshold values are obtained from a simulation. The simulation results were validated through an experiment. The proposed novel system effectively detects forces between 100 and 200 N (indicating the presence of an object) within 0.3 s and complies with EN14752 safety standard. It can also differentiate between soft and hard objects trapped in train doors.

Carbody hunting behaviour of high speed vehicles in low effective conicity of wheel-rail contact

In the past, bogie hunting instability garnered more attention due to its potential for causing serious safety issues. However, with the rise in high-speed vehicle speeds and operational mileage, the concerns arising from carbody hunting can no longer be overlooked. This article investigates the causes and solutions for low-frequency carbody swaying under low effective conicity in wheel-rail contact, employing a combination of experimental and numerical techniques. Initially, the study involves an in-depth analysis of time-domain signals from lateral and vertical accelerations of the carbody and bogie frame during a high-speed train field-test. The carbody vibration mode resulting from carbody hunting displays a swaying motion at a frequency of 1.5 Hz. Subsequently, a dynamic system model of the high-speed vehicle was established and the suspension mode of the model were verified. The frequency of the carbody upper sway motion is determined to be 1.39 Hz. Low effective conicity is induced when grinding wheel match with new rail or new wheel match with grinding rail. Under this low effective conicity, the hunting frequency of the vehicle is around 1.5 Hz, close to the frequency of the carbody upper sway motion. The coincidence of the hunting frequency and the upper sway motion frequency leads to the low-frequency swaying of the carbody. At last, the effects of wheel profiles, rail profiles and suspension parameters on the carbody hunting have been studied. The study demonstrates that addressing low-frequency carbody swaying can be achieved through targeted rail grinding. This process involves modifying the rail profile while ensuring careful grinding of the rail inner corner. Furthermore, it has been confirmed in the article that replacing suitable yaw dampers is also effective.

Improving diagnostic precision in amyloid brain PET imaging through data-driven motion correction

BackgroundHead motion during brain positron emission tomography (PET)/computed tomography (CT) imaging degrades image quality, resulting in reduced reading accuracy. We evaluated the performance of a head motion correction algorithm using 18F-flutemetamol (FMM) brain PET/CT images.MethodsFMM brain PET/CT images were retrospectively included, and PET images were reconstructed using a motion correction algorithm: (1) motion estimation through 3D time-domain signal analysis, signal smoothing, and calculation of motion-free intervals using a Merging Adjacent Clustering method; (2) estimation of 3D motion transformations using the Summing Tree Structural algorithm; and (3) calculation of the final motion-corrected images using the 3D motion transformations during the iterative reconstruction process. All conventional and motion-corrected PET images were visually reviewed by two readers. Image quality was evaluated using a 3-point scale, and the presence of amyloid deposition was interpreted as negative, positive, or equivocal. For quantitative analysis, we calculated the uptake ratio (UR) of 5 specific brain regions, with the cerebellar cortex as a reference region. The results of the conventional and motion-corrected PET images were statistically compared.ResultsIn total, 108 sets of FMM brain PET images from 108 patients (34 men and 74 women; median age, 78 years) were included. After motion correction, image quality significantly improved (p < 0.001), and there were no images of poor quality. In the visual analysis of amyloid deposition, higher interobserver agreements were observed in motion-corrected PET images for all specific regions. In the quantitative analysis, the UR difference between the conventional and motion-corrected PET images was significantly higher in the group with head motion than in the group without head motion (p = 0.016).ConclusionsThe motion correction algorithm provided better image quality and higher interobserver agreement. Therefore, we suggest that this algorithm be adopted as a routine post-processing protocol in amyloid brain PET/CT imaging and applied to brain PET scans with other radiotracers.

Nonlinear Time-domain signal and noise analysis of an active CRLH transmission line for millimeter-wave frequencies

In this paper, time domain signal and noise behavior of an active CRLH transmission line was investigated in the nonlinear regime at millimeter wave frequencies. In this structure, the transistor is considered as three coupled transmission lines and all three drain,gate and source lines are CRLH transmission lines. The left-handed element values for the active CRLH transmission line are calculated in such a way that the phase cancellation effect and dispersion of the CRLH transmission line are reduced. In order to model this active CRLH transmission line, with the assumption of wave propagation in the form of Quasi-TEM, the distributed modeling method is used to extract stochastic telegraphic equation (STE) in the non-linear regime. To model noise sources in the non-linear regime, the Wiener process and modulation functions have been used to model white noise in the nonlinear regime and in the time domain. By applying the finite integration method to stochastic telegraphic equation, the behavior of the signal and noise of the active structure CRLH has been compared with a line of active structure RH and the obtain results of the numerical method have been verified with the results of the Agilent ADS software.

Few-Shot Fault Diagnosis Based on an Attention-Weighted Relation Network.

As energy conversion systems continue to grow in complexity, pneumatic control valves may exhibit unexpected anomalies or trigger system shutdowns, leading to a decrease in system reliability. Consequently, the analysis of time-domain signals and the utilization of artificial intelligence, including deep learning methods, have emerged as pivotal approaches for addressing these challenges. Although deep learning is widely used for pneumatic valve fault diagnosis, the success of most deep learning methods depends on a large amount of labeled training data, which is often difficult to obtain. To address this problem, a novel fault diagnosis method based on the attention-weighted relation network (AWRN) is proposed to achieve fault detection and classification with small sample data. In the proposed method, fault diagnosis is performed through the relation network in few-shot learning, and in order to enhance the representativeness of feature extraction, the attention-weighted mechanism is introduced into the relation network. Finally, in order to verify the effectiveness of the method, a DA valve fault dataset is constructed, and experimental validation is performed on this dataset and another benchmark PU rolling bearing fault dataset. The results show that the accuracy of the network on DA is 99.15%, and the average accuracy on PU is 98.37%. Compared with the state-of-the-art diagnosis methods, the proposed method achieves higher accuracy while significantly reducing the amount of training data.

Diagnostic Accuracy of an Algorithm for Discriminating Presumed Solid and Gaseous Microembolic Signals During Transcranial Doppler Examinations.

The aim of the work described here was to assess the diagnostic accuracy of a new algorithm (SGA-a) for time-domain analysis of transcranial Doppler audio signals to discriminate presumed solid and gaseous microembolic signals and artifacts (SGAs). SGA-a was validated by human experts in an artifact cohort of 20 patients subjected to a 1-h transcranial Doppler exam before cardiac surgery (cohort 1). Emboli were validated in a cohort of 10 patients after aortic valve replacement in a 4-h monitoring period (cohort 2). The SGA misclassification rate was estimated by testing SGA-a on artifact-free test files of solid and gaseous emboli. In cohort 1 (n=24,429), artifacts were classified with an accuracy of 94.5%. In cohort 2 (n=12,328), the accuracy in discriminating solid/gaseous emboli from artifacts was 85.6%. The 95% limits of agreement for, respectively, the numbers of presumed solids and gaseous emboli, artifacts and microembolic signals of undetermined origin were [-10, 10], [-14, 7] and [-9, 16], and the intra-class correction coefficients were 0.99, 0.99 and 0.99, respectively. The rate of misclassification of solid test files was 2%, and the rate of misclassification of gaseous test files was 12%. SGA-a can detect presumed solid and gaseous microembolic signals and differentiate them from artifacts. SGA-a could be of value when both solid and gaseous emboli may jeopardize brain function such as seen during cardiac valve and/or aortic arch replacement procedures.

Prediction of the belt drive contamination status based on vibration analysis and artificial neural network

Belt drive contamination is considered one of the most common failure modes that could be developed in the belts due to harsh operation conditions, high humidity, and sunlight exposure, reducing the belt’s performance. If the belt failure has not been detected early, a sudden shutdown may happen, producing safety and economic consequences. However, most maintenance personnel use their senses of sight, hearing, smell, and touch to identify the cause of the problem while diagnosing a belt drive condition. Hence, this research involves developing an intelligent contamination status detection system based on vibration signal analysis for a pulley-belt rotating system. Time-domain signal analysis was employed to extract some suggestive features such as the root mean square, kurtosis, and skewness from the vibration data. An artificial neural network (ANN) model was built to detect the simulated different operating conditions. The vibration data was gathered with the help of two MEMS accelerometers (ADXL335) interfaced with an NI USB-6009 data acquisition device. A signal capture, analysis, and feature extraction system was developed using Matlab Simulink. The simulated operating conditions include clean, wet, and powder-contaminated belts. The results showed that the designed system could identify the pulley-belt operation conditions with 100% overall accuracy.

Time-Frequency Analysis of Two-Dimensional Electron Spin Resonance Signals.

Two-dimensional electron spin resonance (2D ESR) spectroscopy is a unique experimental technique for probing protein structure and dynamics, including processes that occur at the microsecond time scale. While it provides significant resolution enhancement over the one-dimensional experimental setup, spectral broadening and noise make extraction of spectral information highly challenging. Traditionally, two-dimensional Fourier transform (2D FT) is applied for the analysis of 2D ESR signals, although its efficiency is limited to stationary signals. In addition, it often fails to resolve overlapping peaks in 2D ESR. In this work, we propose a time-frequency analysis of 2D time-domain signals, which identifies all frequency peaks by decoupling a signal into its distinct constituent components via projection on the time-frequency plane. The method utilizes 2D undecimated discrete wavelet transform (2D UDWT) as an intermediate step in the analysis, followed by signal reconstruction and 2D FT. We have applied the method to a simulated 2D double quantum coherence (DQC) signal for validation and a set of experimental 2D ESR signals, demonstrating its efficiency in resolving overlapping peaks in the frequency domain, while displaying frequency evolution with time in case of non-stationary data.

Design and Modelling of Graphene-Based Flexible 5G Antenna for Next-Generation Wearable Head Imaging Systems.

Arguably, 5G and next-generation technology with its key features (specifically, supporting high data rates and high mobility platforms) make it valuable for coping with the emerging needs of medical healthcare. A 5G-enabled portable device receives the sensitive detection signals from the head imaging system and transmits them over the 5G network for real-time monitoring, analysis, and storage purposes. In terms of material, graphene-based flexible electronics have become very popular for wearable and healthcare devices due to their exceptional mechanical strength, thermal stability, high electrical conductivity, and biocompatibility. A graphene-based flexible antenna for data communication from wearable head imaging devices over a 5G network was designed and modelled. The antenna operated at the 34.5 GHz range and was designed using an 18 µm thin graphene film for the conductive radiative patch and ground with electric conductivity of 3.5 × 105 S/m. The radiative patch was designed in a fractal fashion to provide sufficient antenna flexibility for wearable uses. The patch was designed over a 1.5 mm thick flexible polyamide substrate that made the design suitable for wearable applications. This paper presented the 3D modelling and analysis of the 5G flexible antenna for communication in a digital care-home model. The analyses were carried out based on the antenna's reflection coefficient, gain, radiation pattern, and power balance. The time-domain signal analysis was carried out between the two antennas to mimic real-time communication in wearable devices.

Gear Crack Detection Based on Vibration Analysis Techniques and Statistical Process Control Charts (SPCC)

Vibration condition monitoring is a non-devastating technique that can be performed to detect tooth cracks propagating in gear systems. This paper proposes to apply a new methodology using time-domain analysis, frequency-domain analysis, and statistical process control charts (SPCC) for gear crack detection of a 10 DOF dynamic model of spiral bevel gear system (SBGS). The gear mesh stiffness effect used in the model has been studied analytically for different levels of crack faults. Adding Gaussian white noise is discussed as the first step to simulating the initial modeling signals of real-world conditions. Second, time-domain signal analysis was performed to identify periodic vibration pulses as failure components and calculate the statistical standard deviation (STD) feature as a fault-sensitive feature. Third, a fast Fourier transform (FFT) to time signals of the variable gear mesh stiffness was applied to determine the gear mesh frequency and sidebands to detect tooth cracks. Fourth, the SPCC was designed using the Shewhart X-bar chart and an exponentially weighted moving average (EWMA) chart based on the STD feature of the healthy gears. Finally, in the testing stage, the control charts are carried out with simulation signals under faulty conditions to detect the different levels of cracks. The results showed that the EWMA chart outperformed the time domain analysis, frequency domain analysis, and Shewhart X-bar chart in detecting all levels of cracks at an early stage.

Time-domain signal analysis of dielectric response of nonlinear SDBD thruster in near space

The nonlinear time domain response signals of surface dielectric barrier discharge (SDBD) are the basis for studying the energy loss of SDBD thruster. This paper is based on the construction of a double layer lumped parameter circuit model in solid dielectric and discharge plasma regions; based on virtual instrument technology, a test device for AC dielectric characteristics of a nonlinear SDBD thruster was built to obtain the time domain signals of AC voltage and response current. According to the characteristic spectral curve of the voltage and the response current signal in the time domain, the response current is decomposed into resistive current (conduction current) and capacitive current (relaxation current) by an appropriate algorithm. The experimental results show that the response current in the discharge plasma region is mainly the capacitive current, and the peak capacitive current is about 2.9 times of the peak resistance current; Relaxation planned loss is the main form of energy loss in the discharge plasma region. Due to space charge limiting current effect and space charge saturation effect, the static and dynamic resistance of discharge plasma region have platform area and negative resistance characteristics respectively; Both static and dynamic capacitance have negative capacitor characteristics.

Asymmetric double-pulse interferometric FROG for visible-wavelength time-domain spectroscopy.

To extend the detection range of time-domain spectroscopy into the challenging visible frequencies, we propose an interferometry-type frequency-resolved optical gating (FROG). Our numerical simulation shows that, when operating in a double-pulse scheme, a unique phase-locking mechanism can be activated and preserves both zero- and first-order phases (φ0, φ1)-indispensable for phase-sensitive spectroscopic study-that are otherwise inaccessible to standard FROG measurement. Followed by time-domain signal reconstruction and analysis protocol, we show that time-domain spectroscopy with sub-cycle temporal resolution is enabled and well suits the need of a ultrafast-compatible and ambiguity-free method for complex dielectric function measurement at visible wavelengths.

Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis

Rotating machine health monitoring is critical for system safety, cost savings, and increased reliability. The need for a simple and accurate fault diagnosis method has led to the development of various monitoring techniques. They incorporate vibration, motor’s current signature, and acoustic emission signals analysis in condition monitoring. So, based on using vibration signal analysis, a test rig was built for bearing fault identification. The test rig replicates and investigates various bearing problems, such as those found in the inner and outer races. An accelerometer, type ADXL335, was interfaced to a data acquisition device (DAQ USB-6215) for collecting vibration signals under various operating circumstances. In addition, a load cell was embedded with the test rig, interfaced with a digital panel meter, and used for recording the applied load on the bearings. The time-domain signal analysis technique was used after acquiring vibration signals at various bearing health states. Then, the time-domain signal was converted to the frequency domain using the fast Fourier transform, and the result was analyzed to investigate the generated fault frequencies. Finally, the obtained frequencies were compared with the theoretical values extracted from the theoretical equations, and the method proved its effectiveness in detecting the fault generated.

Automated Segmentation of the Systolic and Diastolic Phases in Wrist Pulse Signal Using Long Short-Term Memory Network.

Purpose Single-period segmentation is one of the important steps in time-domain analysis of pulse signals, which is the basis of time-domain feature extraction. The existing single-period segmentation methods have the disadvantages of generalization, reliability, and robustness. Method This paper proposed a period segmentation method of pulse signals based on long short-term memory (LSTM) network. The preprocessing was performed to remove noises and baseline drift of pulse signals. Thus, LabelMe was used to label each period of the pulse signals into two parts according to the location of the starting point of main wave and the dicrotic notch, and the dataset of the pulse signal period segmentation was established. Consequently, the labeled dataset was input into the LSTM for training and testing, and the results were compared with sum slope function method. Result The remarkable result with the whole period segmentation accuracy of 92.8% was achieved for the segmentation of seven types of pulse signals. And the segmentation accuracies of the systolic phase, diastolic phase, and whole period using this method were higher than those of the sum slope function method. Conclusion LSTM-based pulse signal segmentation method can achieve outstanding, robust, and reliable segmentation effects of the systolic phase, diastolic phase, and whole period of pulse signals. The research provides a new idea and method for the segmentation of pulse signals.

A Source Localization Method Using Complex Variational Mode Decomposition.

Source localization with a passive sensors array is a common topic in various areas. Among the popular source localization algorithms, the compressive sensing (CS)-based method has recently drawn considerable interest because it is a high-resolution method, robust with coherent sources and few snapshots, and applicable for mixed near-field and far-field source localization. However, the CS-based methods rely on the dense grid to ensure the required estimation precision, which is time-consuming and impractical. This paper applies the complex variational mode decomposition (CVMD) to source localization. Specifically, the signal model of the source localization problem is similar to the time-domain frequency-modulated signal model. Motivated by this, we extend CVMD, initially designed for nonstationary time-domain signal analysis, to array signal processing. The decomposition results of the array measurements can correspond to the potential sources at different locations. Then, the sources’ direction and range can be estimated by model fitting with the decomposed subsignals. The simulation results show that the proposed CVMD-based method can locate the pure far-field, pure near-field, mixed far-field, and near-field sources. Notably, it can yield high-resolution localization for the coherent sources with one single snapshot with low computing time.

Application of the Time-Domain Signal Analysis for Electrical Appliances Identification in the Non-Intrusive Load Monitoring

The paper presents a novel method for non-intrusive appliances identification. It can be used for energy load disaggregation in a smart grid. The approach identifies changes in the state of the particular appliance by measuring and processing the common supply current signal. Analysis of the instantaneous changes in the aggregated current on the output of the analyzed circuit in the power network is exploited here. The signal is processed using the time alignment of the current and voltage signals samples represented in the array form. The scheme includes filtering, event detection and identification, which is performed by comparing parameters of the detected event against previously determined signatures of monitored appliances. The analysis is performed in the time domain; therefore (unlike other existing methods), the information contained in the original signal is not lost. The approach was tested in the laboratory designed specifically for this purpose. All tests have been conducted with up to 12 appliances operating at the same time in the single power supply circuit. The measurement setup was developed and used to record appliances’ switching on/off events. During tests, 2300 events for devices were recorded. Collected data were processed to identify particular devices with the accuracy of 98.8% and macro-averaged F-score measure of 0.9874. High identification accuracy was achieved despite the high number of devices operating in the background.

Water hammer response characteristics of wellbore-fracture system: Multi-dimensional analysis in time, frequency and quefrency domain

Water hammer fracture diagnostics is a promising fracture diagnostic method, which is nonintrusive, economical, timely performed and easily operated. In present studies, the propagation behavior of the water hammer in the wellbore-fracture system remains unclear, which brings uncertainty to locating the hydraulic-stimulated fractures. In this paper, the transient flow model was used to describe the water hammer pressure fluctuation, which was generated after the pump shut down during the hydraulic fracturing treatment, and the wellbore-fracture system was built by introducing the branch pipe to the wellbore as the fracture. The method of characteristics (MOC) was used to solve this model, then was validated by the computational fluid dynamics software FLUENT. Combining water hammer signal analysis in time, frequency, and quefrency domain, the fracture effect on the water hammer response characteristics in the wellbore-fracture system was investigated in multiple dimensions. We then built eight cases to simulate the water hammer pressure fluctuation in different wellbore-fracture systems, including the existence, the varied location and the varied length of the fracture and conducted the multi-dimensional analysis. After that, results were discussed and the response characteristics of the water hammer in the wellbore-fracture system were summarized. From this work, a multi-dimensional analysis method in time, frequency and quefrency domain is proposed. Meanwhile, the water hammer response characteristics of the wellbore-fracture system are sufficiently presented for the first time to accurately investigate the water hammer pressure propagation in the wellbore-fracture system and provide useful insights for water hammer diagnostics in field application.

A noninvasive and comprehensive method for continuous assessment of cerebral blood flow pulsation based on magnetic induction phase shift.

Cerebral blood flow (CBF) monitoring is of great significance for treating and preventing strokes. However, there has not been a fully accepted method targeting continuous assessment in clinical practice. In this work, we built a noninvasive continuous assessment system for cerebral blood flow pulsation (CBFP) that is based on magnetic induction phase shift (MIPS) technology and designed a physical model of the middle cerebral artery (MCA). Physical experiments were carried out through different simulations of CBF states. Four healthy volunteers were enrolled to perform the MIPS and ECG synchronously monitoring trials. Then, the components of MIPS related to the blood supply level and CBFP were investigated by signal analysis in time and frequency domain, wavelet decomposition and band-pass filtering. The results show that the time-domain baseline of MIPS increases with blood supply level. A pulse signal was identified in the spectrum (0.2–2 Hz in 200–2,000 ml/h groups, respectively) of MIPS when the simulated blood flow rate was not zero. The pulsation frequency with different simulated blood flow rates is the same as the squeezing frequency of the feeding pump. Similar to pulse waves, the MIPS signals on four healthy volunteers all had periodic change trends with obvious peaks and valleys. Its frequency is close to that of the ECG signal and there is a certain time delay between them. These results indicate that the CBFP component can effectively be extracted from MIPS, through which different blood supply levels can be distinguished. This method has the potential to become a new solution for non-invasive and comprehensive monitoring of CBFP.

Elongated bubble velocity estimation in vertical liquid-gas flows using flow-induced vibration

The multiphase flow occurs in various major industrial fields and nature. Furthermore, due to the single and multiphase flow characteristics, they generate vibration on the structure conveying or subjected to the flow. This paper investigates in an experimental apparatus, the possibility to use the flow-induced vibration from a vertical liquid-gas two-phase flow conveyed by a pipe to obtain the elongated bubble velocity. The vibration signal analysis in time domain showed that the structural excitation due to the elongated bubble rising in the stagnant liquid was significant to be distinguished from other excitations. The time-frequency analysis of slug and churn flow showed a significant amplitude variation in a specific frequency band. Finally, it was possible to obtain the elongated bubble velocity with reasonable accuracy by cross-correlating the envelope of a vibration signal filtered in a particular frequency band of two accelerometers. Thus, this paper presents a non-invasive and simple to mount technique to estimate the elongated bubble velocity in stagnant and moving liquid conditions.