scholarly journals Vibrations in CDFW

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 704
Author(s):  
Daniel Soares de Alcantara ◽  
Pedro Paulo Balestrassi ◽  
José Henrique Freitas Gomes ◽  
Carlos Alberto Carvalho Castro
Keyword(s):  
Time Domain ◽  
Low Frequency ◽  
Welding Process ◽  
Statistical Characteristics ◽  
Crest Factor ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Complex Process ◽  
The Time Domain ◽  
Peak Value

Continuous drive friction welding is a solid-state welding process that has been experimentally proven to be a fast and reliable method. This is a complex process; deformations in the viscosity of a material alter the friction between the surfaces of the pieces. All these dynamics cause changes in the vibration signals; the interpretation of these signals can reveal important information. The vibration signals generated during the friction and forging stages are measured on the stationary part of the structure to determine the influence of the manipulated variables on the time domain statistical characteristics (root mean square, peak value, crest factor, and kurtosis). In the frequency domain, empirical mode decomposition is used to characterize frequencies. It was observed that it is possible to identify the effects of the manipulated variables on the calculated statistical characteristics. The results also indicate that the effect of manipulated variables is stronger on low-frequency signals.

scholarly journals Analysis method of the cavitation vibration signals in poppet valve based on EEMD

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199811
Author(s):  
Beibei Li ◽  
Qiao Zhao ◽  
Huaiyi Li ◽  
Xiumei Liu ◽  
Jichao Ma ◽  
...  
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Center Frequency ◽  
Analysis Method ◽  
Vibration Signals ◽  
Poppet Valve ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Cavitation Intensity ◽  
Eemd Method ◽  
Peak Value

To study the vibration characteristics of the poppet valve induced by cavitation, the signal analysis method based on the ensemble empirical mode decomposition (EEMD) method was studied experimentally. The component induced by cavitation was separated from the vibration signals through the EEMD method. The results show that the IMF2 component has the largest amplitude and energy of all components. The root mean square (RMS) value, peak value of marginal spectrum, and center frequency of marginal spectrum of the IMF2 component were studied in detail. The RMS value and the peak value of the marginal spectrum decrease with a decrease of cavitation intensity. The center frequency of marginal spectrum is between 12 kHz and 20 kHz, and the center frequency first increases and then decreases with a decrease of cavitation intensity. The change rate of the center frequency also decreases with an increase of inlet pressure.

scholarly journals A research on fiber-optic vibration pattern recognition based on time-frequency characteristics

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881346 ◽  
Author(s):  
Tabi Fouda Bernard Marie ◽  
Dezhi Han ◽  
Bowen An ◽  
Jingyun Li
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Time Domain ◽  
Fiber Optic ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Vibration Pattern ◽  
The Time Domain

To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.

scholarly journals A SFTD Algorithm for Optimizing the Performance of the Readout Strategy of Residence Time Difference Fluxgate

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3985 ◽  
Author(s):  
Siyu Chen ◽  
Yanzhang Wang ◽  
Jun Lin
Keyword(s):  
Residence Time ◽  
Time Domain ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Transformation Method ◽  
Time Difference ◽  
Single Frequency ◽  
Time Frequency ◽  
Frequency Magnetic Field ◽  
The Time Domain

Residence time difference (RTD) fluxgate sensor is a potential device to measure the DC or low-frequency magnetic field in the time domain. Nevertheless, jitter noise and magnetic noise severely affect the detection result. A novel post-processing algorithm for jitter noise reduction of RTD fluxgate output strategy based on the single-frequency time difference (SFTD) method is proposed in this study to boost the performance of the RTD system. This algorithm extracts the signal that has a fixed frequency and preserves its time-domain information via a time–frequency transformation method. Thereby, the single-frequency signal without jitter noise, which still contains the ambient field information in its time difference, is yielded. Consequently, compared with the traditional comparator RTD method (CRTD), the stability of the RTD estimation (in other words, the signal-to-noise ratio of residence time difference) has been significantly boosted with sensitivity of 4.3 μs/nT. Furthermore, the experimental results reveal that the RTD fluxgate is comparable to harmonic fluxgate sensors, in terms of noise floor.

Time Domain Zero Field NMR and NQR

1986 ◽  
Vol 41 (1-2) ◽  
pp. 440-444 ◽  
Author(s):  
A. Bielecki ◽  
D. B. Zax ◽  
A. M. Thayer ◽  
J. M. Millar ◽  
A. Pines
Keyword(s):  
Time Domain ◽  
Relaxation Times ◽  
Low Frequency ◽  
High Sensitivity ◽  
Lattice Relaxation ◽  
Spin Lattice Relaxation ◽  
Zero Field ◽  
Spin Lattice ◽  
Field Cycling ◽  
The Time Domain

Field cycling methods are described for the time domain measurement of nuclear quadrupolar and dipolar spectra in zero applied field. Since these techniques do not involve irradiation in zero field, they offer significant advantages in terms of resolution, sensitivity at low frequency, and the accessible range of spin lattice relaxation times. Sample data are shown which illustrate the high sensitivity and resolution attainable. Comparison is made to other field cycling methods, and an outline of basic instrumental requirements is given.

Experimental Analysis of the Effect of Cutting Conditions on the Initial Penetration in Drilling: A Comparative Study Between Conventional and Split Chisel Drills

2009 ◽  
Author(s):  
Sungwoo Park ◽  
Issam Abu-Mahfouz ◽  
Amit Banerjee
Keyword(s):  
Time Domain ◽  
Drilling Process ◽  
Drill Bit ◽  
Penetration Process ◽  
Initial Penetration ◽  
Split Point ◽  
The Time Domain ◽  
Twist Drills ◽  
Skewness And Kurtosis ◽  
Peak Value

To improve the performance and the capability of the drilling process, it is necessary to understand the mechanics of drilling. In particular, drill bit vibrations lead to undesirable effects such as chatter, hole location and roundness errors. In this study, the wandering motion during initial penetration in drilling is experimentally investigated. Four quantities, namely, mean, peak-to-peak value, skewness, and kurtosis are calculated for the time-domain orbital signals. The orbital signals were obtained using proximity probes. The drilling was performed in plates of aluminum, brass, steel, and stainless steel using conventional point and split-chisel symmetrical twist drills. The conventional and split-point drills show different wandering paths during the penetration process. In general, split-point drills produced larger size orbits with more chatter during initial penetration, but showed better centering. On the other hand, conventional drills were found to produce holes that were more offset with smaller size and smoother wandering motion around the actual centers.

scholarly journals Preterm-Term Birth Classification Using EMD-Based Time-Domain Features of Single-Channel Electrohysterogram Data

2021 ◽  
Author(s):  
Suparerk Janjarasjitt
Keyword(s):  
Preterm Birth ◽  
Time Domain ◽  
Single Channel ◽  
Support Vector ◽  
Computational Results ◽  
Intrinsic Mode Functions ◽  
Term Birth ◽  
Emg Signal ◽  
Mode Decomposition ◽  
The Time Domain

Abstract The preterm birth anticipation is a crucial task that can reduce the rate of preterm birth and also the complications of preterm birth. Electrohysterogram (EHG) or uterine electromyogram (EMG) data have been evidenced that they can provide an information useful for preterm birth anticipation. Four distinct time-domain features, i.e., mean absolute value, average amplitude change, difference absolute standard deviation value, and log detector, commonly applied to EMG signal processing are applied and investigated in this study. A single-channel of EHG data is decomposed into its constituent components, i.e., intrinsic mode functions, using empirical mode decomposition (EMD) before their time-domain features are extracted. The time-domain features of intrinsic mode functions of EHG data associated with preterm and term births are applied for preterm-term birth classification using support vector machine (SVM) with a radial basis function. The preterm-term classifications are validated using 10-fold cross validation. From the computational results, it is shown that the excellent preterm-term birth classification can be achieved using a single-channel of EHG data. The computational results further suggest that the best overall performance on preterm-term birth classification is obtained when thirteen (out of sixteen) EMD-based time-domain features are applied. The best accuracy, sensitivity, specificity, and F1-score achieved are, respectively, 0.9382, 0.9130, 0.9634, and 0.9366.

scholarly journals Modal approximation for plasmonic resonators in the time domain: the scalar case

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Lorenzo Baldassari ◽  
Pierre Millien ◽  
Alice L. Vanel
Keyword(s):  
Time Domain ◽  
Homogeneous Medium ◽  
Low Frequency ◽  
Two Dimensions ◽  
Boundary Integral ◽  
Scalar Case ◽  
Singular Boundary ◽  
Metallic Nanoparticle ◽  
The Time Domain ◽  
Modal Approximation

AbstractWe study the electromagnetic field scattered by a metallic nanoparticle with dispersive material parameters in a resonant regime. We consider the particle placed in a homogeneous medium in a low-frequency regime. We define modes for the non-Hermitian problem as perturbations of electro-static modes, and obtain a modal approximation of the scattered field in the frequency domain. The poles of the expansion correspond to the eigenvalues of a singular boundary integral operator and are shown to lie in a bounded region near the origin of the lower-half complex plane. Finally, we show that this modal representation gives a very good approximation of the field in the time domain. We present numerical simulations in two dimensions to corroborate our results.

scholarly journals Fault Diagnosis of Rolling Bearing based on an Improved Denoising Technique using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method

2021 ◽  
Author(s):  
Prashant Kumar Sahu ◽  
Rajiv Nandan Rai
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Reconstruction ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Adaptive Thresholding ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Envelope Spectrum

Abstract The vibration signals for rotating machines are generally polluted by excessive noise and can lose the fault information at the early development phase. In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using CEEMD algorithm. The IMFs grouping and selection are formed based upon the correlation coefficient value. The noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction. The effectiveness of the proposed method denoised signals are measured based on kurtosis value and the envelope spectrum analysis. The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing.

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