Sensitive Frequency Band Research Articles

Islanding Detection With Positive Feedback of Selected Frequency for DC Microgrid Systems

The oscillation frequency of the point of common coupling (PCC) voltage is proposed as a new detecting freedom for dc-based islanding events in this article. In the presented method, the voltage positive feedback with a frequency selection is applied to a distributed generator to make the PCC voltage oscillate at the selected frequency when islanding events occur. With the frequency information, islanding events can be identified by the small-amplitude oscillation superimposed on the normal dc voltage without the necessity of voltage amplitude to be shifted out of the normal range. Thus, the islanding events can be effectively distinguished from other abnormal conditions that lead to large voltage fluctuations, and so, the accuracy of islanding detection is improved. Also, the power quality at islanding events is enhanced, which provides important benefits for seamless transfer from the unintentional islanding conditions to the controlled islanding conditions. Moreover, the frequency selection is designed to only transfer the voltage signal at the sensitive frequency band of the islanding condition. As a result, the deterioration of stability caused by the voltage positive feedback can be relieved under the grid-connected condition compared to the existing methods. Finally, the effectiveness of the proposed method has been verified by both time-domain nonlinear simulation and experimental results.

All-sky search in early O3 LIGO data for continuous gravitational-wave signals from unknown neutron stars in binary systems

Rapidly spinning neutron stars are promising sources of continuous gravitational waves. Detecting such a signal would allow probing of the physical properties of matter under extreme conditions. A significant fraction of the known pulsar population belongs to binary systems. Searching for unknown neutron stars in binary systems requires specialized algorithms to address unknown orbital frequency modulations. We present a search for continuous gravitational waves emitted by neutron stars in binary systems in early data from the third observing run of the Advanced LIGO and Advanced Virgo detectors using the semicoherent, GPU-accelerated, binaryskyhough pipeline. The search analyzes the most sensitive frequency band of the LIGO detectors, 50–300 Hz. Binary orbital parameters are split into four regions, comprising orbital periods of three to 45 days and projected semimajor axes of two to 40 light seconds. No detections are reported. We estimate the sensitivity of the search using simulated continuous wave signals, achieving the most sensitive results to date across the analyzed parameter space.5 MoreReceived 23 December 2020Accepted 3 February 2021DOI:https://doi.org/10.1103/PhysRevD.103.064017© 2021 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasGravitational wave detectionGravitational wave sourcesGravitational wavesPhysical SystemsBinary starsNeutron stars & pulsarsGravitation, Cosmology & Astrophysics

Adaptive sensitive frequency band selection for VMD to identify defective components of an axial piston pump

The underlying study investigates single valued neutrosophic entropy based adaptive sensitive frequency band selection for variational mode decomposition (VMD) for the purpose of identifying defective components in an axial pump. The proposed methodology is applied in the following steps. First, VMD is applied for decomposing vibration signals into various frequency bands, called as modes. After computing energy of each VMD, the lower (minimum) and upper (maximum) bounds from these energy readings are extracted for defect conditions, such as outer race, inner race, worn piston, faulty cylinder and valve plate, and blocked hole of the piston. Thereafter, energy interval ranges are obtained and further converted into the form of single valued neutrosophic sets (SVNSs). Then, the proposed neutrosophic entropy measure is deployed for quantifying the non-linear connection between each bearing defect conditions and various frequency bands. The mode having maximum neutrosophic entropy value is designated to the “most sensitive” frequency band. Thereafter, envelope demodulation is applied to the most sensitive selected frequency band for finding defective components. The proposed neutrosophic entropy and VMD based methodology is effective in providing a better insight for selecting suitable frequency band for carrying out envelope demodulation in comparison to existing methods.

Piezoelectric Sensor-Embedded Smart Rock for Damage Monitoring in a Prestressed Anchorage Zone

In this paper, a piezoelectric sensor-embedded smart rock is proposed for the electromechanical impedance monitoring of internal concrete damage in a prestressed anchorage zone. Firstly, a piezoelectric sensor-embedded smart rock is analyzed for impedance monitoring in concrete structures. An impedance measurement model is analyzed for the PZT (lead zirconate titanate)-embedded smart rock under compression in a concrete member. Secondly, a prototype of the smart rock embedded with a PZT sensor is designed in order to ascertain, sensitively, the variations of the impedance signatures induced by concrete damage in an anchorage zone. Thirdly, the performance of the smart rock is estimated from a numerical analysis and experimental tests. Variations in the impedance signals under compressive test cases are analyzed in order to predetermine the sensitive frequency band for the impedance monitoring. Lastly, an experiment on an anchorage zone embedded with the smart rocks and surface-mounted PZT sensors is conducted for the impedance measurement under a series of loading cases. The impedance variations are quantified in order to comparatively evaluate the feasibility of the sensor-embedded smart rock for the detection of internal concrete damage in the anchorage zone. The results show that the internal concrete damage was successfully detected using the PZT-embedded smart rock, thus enabling the application of the technique for anchorage zone health monitoring.

Estimating the Alumina Concentration During Aluminum Electrolysis by Identifying the Sensitive Frequency Band Using Marginal Kurtosis

The accurate and timely online estimation of the alumina concentration in an industrial aluminum electrolysis cell can improve its feed control performance. By using the proposed marginal kurtosis (MK), marginal kurtosis dominant frequency (MK-DF), integral feed frequency and sensitive frequency band (SFB), this study reveals that a normalized cell voltage with different current intensities and feed schemes has different alumina concentration SFBs and determines the general formula for calculating the SFB. On this basis, a general algorithm for acquiring the pseudo alumina concentration (PAC) using translation-invariant filtering is proposed in which the upper limit of the SFB provides the cutoff frequency of the low-pass filter. The PAC was used to estimate the absolute and relative concentrations of alumina. The absolute concentrations are validated using measured concentrations. Additionally, improved feed control performance of the industrial cells is presented using the relative concentration PAC-slope.

Characterization of systematic error in Advanced LIGO calibration

The raw outputs of the detectors within the Advanced Laser Interferometer Gravitational-Wave Observatory need to be calibrated in order to produce the estimate of the dimensionless strain used for astrophysical analyses. The two detectors have been upgraded since the second observing run and finished the year-long third observing run. Understanding, accounting, and/or compensating for the complex-valued response of each part of the upgraded detectors improves the overall accuracy of the estimated detector response to gravitational waves. We describe improved understanding and methods used to quantify the response of each detector, with a dedicated effort to define all places where systematic error plays a role. We use the detectors as they stand in the first half (six months) of the third observing run to demonstrate how each identified systematic error impacts the estimated strain and constrain the statistical uncertainty therein. For this time period, we estimate the upper limit on systematic error and associated uncertainty to be <7% in magnitude and <4 deg in phase (68% confidence interval) in the most sensitive frequency band 20–2000 Hz. The systematic error alone is estimated at levels of <2% in magnitude and <2 deg in phase.

Frequency spectrum and fatigue analysis of T-bolt fracture in DT III fastener based on Refined Model.

It is observed that T-bolt fracture of DT III fastener affects the safety of railway operations. The present study is aimed at finding solution to this critical issue of DT III T-bolt fracture. The paper is based on outcomes of field studies and analysis of relationship between bolt installation torque and stress. A refined DT III fastener model is established based on test data and nonlinear contact theory as per the actual size of each component. It is being observed that head-shank joint is a critical area during service. With respect to fatigue and stress, when the installation torque is less than 300 KN-mm, the bolt is prevented from yielding during installation. To a certain extent it also improves the fatigue life of bolt. From the frequency spectrum point of view, the vibration sensitive frequency band (1050-1100 Hz, 1230-1270 Hz) of bolt in service is very close to the main frequency of bolt vibration (1200 Hz). It may cause bolt fracture. In order to avoid resonance, the train operational speed should be strictly controlled between 115 km/h and 120 km/h.

Quantum squeezing schemes for heterodyne readout

Advanced gravitational-wave detectors are limited by quantum noise in their most sensitive frequency band. Quantum noise suppression techniques, such as the application of the quantum squeezed state of light, have been actively studied in the context of homodyne readouts. In this paper, we consider quantum squeezing schemes for the heterodyne readouts. This is motivated by a successful suppression of the higher-order-mode content by stable recycling cavities in advanced detectors. The heterodyne readout scheme requires precise tuning of the interferometer parameters and a broadband squeezing source, but is conceptually simple and elegant. We further show that it is compatible with the frequency-dependent squeezing, which reduces both the shot noise and the radiation-pressure noise. We propose a test of the heterodyne readout with squeezing in Advanced LIGO. This can serve as a pathfinder not only for the implementation in future detectors, such as Einstein Telescope and Cosmic Explorer, but also for general high-precision optical measurements.

Three-dimensional core-collapse supernova simulations of massive and rotating progenitors

ABSTRACT We present 3D simulations of the core-collapse of massive rotating and non-rotating progenitors performed with the general relativistic neutrino hydrodynamics code coconut-fmt. The progenitor models include Wolf-Rayet stars with initial helium star masses of $39\, \mathrm{ M}_{\odot }$ and $20\, \mathrm{ M}_{\odot }$, and an $18\, \mathrm{ M}_{\odot }$ red supergiant. The $39\, \mathrm{ M}_{\odot }$ model is a rapid rotator, whereas the two other progenitors are non-rotating. Both Wolf-Rayet models produce healthy neutrino-driven explosions, whereas the red supergiant model fails to explode. By the end of the simulations, the explosion energies have already reached $1.1\times 10^{51}\, $ and $0.6\times 10^{51}\, \mathrm{erg}$ for the $39\, \mathrm{ M}_{\odot }$ and $20\, \mathrm{ M}_{\odot }$ model, respectively. They produce neutron stars of relatively high mass, but with modest kicks. Due to the alignment of the bipolar explosion geometry with the rotation axis, there is a relatively small misalignment of 30° between the spin and the kick in the rapidly rotating $39\, \mathrm{ M}_{\odot }$ model. For this model, we find that rotation significantly changes the dependence of the characteristic gravitational-wave frequency of the f-mode on the proto-neutron star parameters compared to the non-rotating case. Its gravitational-wave amplitudes would make it detectable out to almost 2 Mpc by the Einstein Telescope. The other two progenitors have considerably smaller detection distances, despite significant low-frequency emission in the most sensitive frequency band of current gravitational-wave detectors.

A novel optimized multi-kernel relevance vector machine with selected sensitive features and its application in early fault diagnosis for rolling bearings

Since the vibration signal of mechanical equipment with early faults is highly similar to that of mechanical equipment under the normal state, it is still a great challenge to extract sensitive features from the original vibration signal to execute the intelligent fault diagnosis for mechanical equipment. An early fault diagnosis method for rolling bearings based on multi-kernel relevance vector machine and multi-domain features was proposed in this paper. In order to reflect the time-varying characteristics, the vibration signals and operation status of rolling bearings in time series were incorporated into the process of early fault diagnosis. The three steps of the early fault diagnosis method for rolling bearings were as follows. Firstly, the vibration signals of rolling bearings during operation were measured online. Secondly, the original vibration signals were decomposed by wavelet packet transformation. The fault features were extracted from sensitive frequency band by time domain statistical analysis as well as, frequency domain statistical analysis, and then the multi-domain feature set was constructed to fully characterize the intrinsic properties of vibration signals. The correlation analysis was adopted to eliminate insensitive features from original multi-domain feature set. The low-dimensional feature set that are highly sensitive to early failures was reconstructed to improve the computational efficiency for subsequent fault diagnosis. Finally, the intelligent fault diagnosis was carried out based on the multi-kernel relevance vector machine model. The performance of this proposed method has been validated in practical rolling bearing fault diagnosis. The results show that the proposed method can achieve higher diagnosis accuracy for rolling bearing under different working conditions than traditional single-kernel model and is effective in early fault diagnosis.

Identification of asphalt pavement transverse cracking based on vehicle vibration signal analysis

Transverse cracking is a common distress of semi-rigid asphalt pavement in China. Crack detection offers essential information for pavement performance evaluation and maintenance. A vibration-based method is proposed for transverse cracking identification of asphalt pavement. Vibration signals of the running vehicle at the transverse-cracked sections and the adjacent uncracked sections are measured and preliminarily analysed in time domain. Symlet3 wavelet is used for signal denoising. Frequency domain analysis of signals by fast Fourier transform (FFT) and discrete wavelet transform (DWT) shows that the occurrence of the transverse crack leads to a dramatic increase of energy in the sensitive frequency band (10∼20 Hz). Considering the computation efficiency, the dual relative energy (DRE) process by FFT and DWT is proposed for crack detection. In addition, the continuous wavelet time-frequency analysis is utilised to locate the transverse cracking precisely. This approach seems promising for the rapid identification of pavement cracks.

ACCUGRAM: A novel approach based on classification to frequency band selection for rotating machinery fault diagnosis

Frequency band selection (FBS) in rotating machinery fault diagnosis aims to recognize frequency band location including a fault transient out of a full band spectrum, and thus fault diagnosis can suppress noise influence from other frequency components. Impulsiveness and cyclostationarity have been recently recognized as two distinctive signatures of a transient. Thus, many studies have focused on developing quantification metrics of the two signatures and using them as indicators to guide FBS. However, most previous studies almost ignore another aspect of FBS, i.e. health reference, which significantly affect FBS performance. To address this issue, this paper investigates importance of a health reference and recognize it as the third critical aspect in FBS. With help of the health reference, the frequency band where the fault transient exists could be located. A novel approach based on classification is proposed to integrate all three aspects (impulsiveness, cyclostationarity, and health reference) for FBS. Classification accuracy is developed as a novel indicator to select the most sensitive frequency band for rotating machinery fault diagnosis. The proposed method (coined by accugram) has been validated on benchmark and experiment datasets. Comparison results show its effectiveness and robustness over conventional envelope analysis, the kurtogram, and the infogram.

Impact of road traffic noise on functional connectivity of human brain networks

With the rapid economic development, the rapid increase in motor vehicle holdings, a sharp increase in urban road traffic, road traffic noise exposure is becoming increasingly serious. This paper focuses on the impact of traffic noise exposure on the human brain. We collected road traffic noise sounds of different sound pressure levels (SPLs), and recorded the electroencephalograph (EEG) when subjects exposed under different SPLs’ road noise. Using the graphic theoretical-based EEG method, the effects of road traffic noise with different SPLs on the functional connectivity of human brain networks were studied. Using time-frequency analysis, the beta2 bands was found to be most sensitive frequency band to different SPLs road noise. The brain network characteristic parameters of different SPLs noises were calculated. It is found that in these two frequency bands, the higher the noise SPL, the lower the clustering coefficient and the higher the characteristic path length. In summary, the paper points out the specific effects of road noise with different SPLs on the human brains. It is found that the higher the SPL of noise, the greater the damage to the functional connectivity of the human brain. That means, noise exposure can affect the rate at which the brain processes, analyses, and transmits information. The research results can provide a basis for road noise evaluation and prevention.

Vibration Signature Extraction of High-Voltage Circuit Breaker by Frequency and Chaotic Analysis

This paper aims at presenting a novel approach for the analysis of vibration signals detected from circuit breaker (CB) opening or closing operations and to mechanical fault identification. An adaptive signal decomposition approach and chaotic nonlinear dynamic technique are used for extracting fault sensitive features of CB. The fault sensitive frequency band is derived using variational mode decomposition (VMD) combining with Hilbert marginal spectrum (HMS). The obtained sensitive frequency band(s) are employed for the reconstruction of the dynamical attractor (chaotic dynamics viewed from a phase space perspective, namely, the attractor-based perspective) following the Taken's embedding theorem. The invariant measures and ergodic quantities such as the largest Lyapunov exponent, correlation dimension, and Kolmogorov entropy which can be estimated on the reconstructed attractor are presented as the fault sensitive features. The CB fault is estimated in a chaotic space using these sensitive features. The effectiveness of the developed method is evaluated by using data sets recorded from several vacuum CBs with the same type.

Detection of Inception Cavitation in Centrifugal Pump by Fluid‐Borne Noise Diagnostic

This paper presents an experimental methodology that is capable of sensitively detecting the cavitation inception in a centrifugal pump. Firstly, with a centrifugal pump of ns = 117 as research object, the cavitation performance, the bubble pattern distribution at impeller inlet, and the vibration and noise were synchronously measured at different flow conditions each with several cavitation coefficients. The change laws of total level of vibration and noise signals throughout the cavitation process were emphatically investigated. After comparing the sensitivity and reliability of different detection methods, the method based on overall sound pressure level of liquid‐borne noise is found to present high sensitivity to cavitation. Secondly, by comparing the affected 1/3 octave spectrum by changing flow and cavitation coefficients, the highly sensitive frequency band to cavitation was obtained. Then, a new inception cavitation detection method was proposed based on Pauta principle. Finally, the method was verified through an ultra‐low‐specific speed pump (ns = 25). The results show that the total pressure level of liquid‐borne noise increases firstly and then decreases with the development of cavitation. The broadband SPL of liquid‐borne noise between 2000 and 3000 Hz can be used to detect the inception cavitation, and the cavitation detection threshold is determined as 1.0%.

Effects of CW Interference on the BDS Receiver and Analysis on the Coupling Path of Electromagnetic Energy

The present research is carried out on the issue that the BDS (BeiDou Navigation Satellite System) receiver exerts some vulnerability to CW (continuous wave) interference. The analysis on the interference mechanism of navigation signal is implemented based on the model of direct sequence spread spectrum communication system while the method of parallel code phase search is employed to simulate the capture capability of the receiver under CW interference. In the CST-MWS, the simulation was made for the coupling path and electromagnetic energy into the navigation receiver, concluding that the antenna functions as the major obtained coupling path. On that basis, the RF radiation interference experiment for the antenna of the BDS receiver was conducted in the semi-anechoic chamber. In addition, the RF injection interference experiment was made on the basis of the power coupler. The sensitive frequency band of the receiver under the interference was obtained through the experiment. Furthermore, findings are made that the ratio of the output power of the RF signal generator under the RF irradiation method to the output power with the RF injection method takes no variation with change of the interference frequency. Therefore, The RF injection method can be used instead of RF irradiation method under CW interference in order to gain simplified experimental equipment, which provides a more professional theoretical basis for anti-interference research.

Frequency Phase Space Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis

As one of the most important components in rotating machinery, rolling bearings are fragile due to their harsh working environment. The empirical wavelet transform (EWT) method has been used to identify bearing faults by constructing an adaptive filter bank and decomposing the vibration signal into different modes. However, the EWT method separates different parts based on the local extremum of the Fourier spectrum. This segmentation strategy is highly susceptible to other components such as gears, and sometimes the fault characteristics of bearings cannot be detected. To overcome the above drawbacks, frequency phase space empirical wavelet transform (FPSEWT) method is proposed in this paper, which divides the spectrum into several parts and uses the Teager energy distribution as a reference for dividing the Fourier spectrum. Then, the differential search (DS) algorithm is applied to automatically identify the optimal right and left boundaries of the sensitive frequency band. The effectiveness of the proposed approach is verified by both simulated signals and experimental data. The fault feature ratio (FFR) values of sensitive components increased from 8.08% to 18.67%, which indicate that the proposed method can successfully extract fault symptoms of rolling element bearings in the presence of environmental disturbance.

An adaptive sensitive frequency band selection method for empirical wavelet transform and its application in bearing fault diagnosis

Empirical wavelet transform (EWT) is an adaptive wavelet based analysis which can be used to extract useful amplitude modulated-frequency modulated (AM-FM) mono components from a bearing vibration signal. However, the pre-requisite segmentation method on the Fourier support of a signal without a rigorous theoretical principle has limited the application of EWT in bearing fault diagnosis. To overcome this difficulty, an adaptive frequency band selection technique utilizing the Harmonic Significance Index (HSI) and Particle Swarm Optimization (PSO) is proposed in this paper. In this approach, HSI is employed as the fitness value of PSO to quantify the fault information contained in various frequency bands, and the optimal parameters (i.e., the lower cutoff frequency and the bandwidth) of the most sensitive frequency band (i.e., the band having the largest HSI value) identified by the PSO are then used in EWT to bandpass filter the original signal to extract the fault related AM-FM mono component. Results from the case studies presented in this work confirm the effectiveness of the proposed technique for bearing fault diagnosis. The proposed technique is particularly useful for situations where a bearing defect signal is contaminated by strong non-Gaussian noise such as random impacts from other mechanical components.

Machine learning-based self-powered acoustic sensor for speaker recognition

Herein, we report a new platform of machine learning-based speaker recognition via the flexible piezoelectric acoustic sensor (f-PAS) with a highly sensitive multi-resonant frequency band. The resonant self-powered f-PAS was fabricated by mimicking the operating mechanism of the basilar membrane in the human cochlear. The f-PAS acquired abundant voice information from the multi-channel sound inputs. The standard TIDIGITS dataset were recorded by the f-PAS and converted to frequency components by using a Fast Fourier Transform (FFT) and a Short-Time Fourier Transform (STFT). The machine learning based Gaussian Mixture Model (GMM) was designed by utilizing the most highest and second highest sensitivity data among multi-channel outputs, exhibiting outstanding speaker recognition rate of 97.5% with error rate reduction of 75% compared to that of the reference MEMS microphone.

Towards the design of gravitational-wave detectors for probing neutron-star physics

The gravitational waveform of merging binary neutron stars encodes information about extreme states of matter. Probing these gravitational emissions requires the gravitational-wave detectors to have high sensitivity above 1 kHz. Fortunately for current advanced detectors, there is a sizeable gap between the quantum-limited sensitivity and the classical noise at high frequencies. Here we propose a detector design that closes such a gap by reducing the high-frequency quantum noise with an active optomechanical filter, frequency-dependent squeezing, and high optical power. The resulting noise level from 1 kHz to 4 kHz approaches the current facility limit and is a factor of 20 to 30 below the design of existing advanced detectors. This will allow for precision measurements of (i) the post-merger signal of the binary neutron star, (ii) late-time inspiral, merger, and ringdown of low-mass black hole-neutron star systems, and possible detection of (iii) high-frequency modes during supernovae explosions. This design tries to maximize the science return of current facilities by achieving a sensitive frequency band that is complementary to the longer-baseline third-generation detectors: the10 km Einstein Telescope, and 40 km Cosmic Explorer. We have highlighted the main technical challenges towards realizing the design, which requires dedicated research programs. If demonstrated in current facilities, the techniques can be transferred to new facilities with longer baselines.