Transient Features Research Articles

Data-driven fault detection and isolation in DC microgrids without prior fault data: A transfer learning approach

The lack of fault data is the major constraint on data-driven fault detection and isolation schemes for DC microgrids. To solve this problem, this paper develops an adversarial-based deep transfer learning model that can detect and classify short-circuit faults in DC microgrids without using historical fault data. In this transfer learning framework, the knowledge of faults is extracted from the transient features of line currents during normal operating disturbances, which is adversarially augmented and then transferred to a target domain as the labels of faults. With the transferred knowledge, a deep learning model combining convolutional neural network and attention-based bidirectional long short-term memory is trained, which is strengthened by attention and soft-voting ensemble mechanisms. In verification tests, this model reaches a high accuracy of over 90% in classifying various short-circuit faults in a multi-terminal DC microgrid model within a short response time of less than 1 ms. Moreover, it is robust against measurement noises and adaptive to system configuration changes. The test results prove the effectiveness of the proposed method in the protection of DC microgrids without prior knowledge of faults.

Transient Fault Signal Identification of AT Traction Network Based on Improved HHT and LSTM Neural Network Algorithm

This paper aims to address the difficult to pinpoint fault cause of the full parallel AT traction power supply system with special structure. The fault characteristics are easily covered up, and high transition impedance only affects the singularity of the wavehead, making the traveling waves hard to identify. Moreover, the classification accuracy of the traditional time-frequency analysis method is not sufficiently high to distinguish precisely. In this paper, a fault classification method of traction network based on single-channel improved Hilbert–Huang transform and deep learning is proposed. This method extracts effective fault features directly from the original fault signals and classifies the fault types at the same time. The accuracy of data categorization is increased by directly applying the Hilbert–Huang transform to fault signals to extract transient fault features and produce one-dimensional feature data, which are analyzed by the time-frequency energy spectrum. Using the similarity recognition method of long-short-term memory neural network, the extracted high-frequency one-dimensional feature data are trained and tested to classify fault signals more accurately. In order to verify the effectiveness of this method, several kinds of short-circuit and lightning strike faults are continuously simulated and verified in this paper. Considering various fault conditions and factors, the proposed improved HHT+LSTM method is compared with the LSTM method for direct processing of the original signals. The improved HHT + LSTM classification algorithm achieves an accuracy of 99.99%.

Robust FDTD Modeling of Graphene-Based Conductive Materials with Transient Features for Advanced Antenna Applications.

The accurate modeling of frequency-dispersive materials is a challenging task, especially when a scheme with a transient nature is utilized, as it is the case of the finite-difference time-domain method. In this work, a novel implementation for the modeling of graphene-oriented dispersive materials via the piecewise linear recursive convolution scheme, is introduced, while the time-varying conductivity feature is, additionally, launched. The proposed algorithm is employed to design a reduced graphene-oxide antenna operating at 6 GHz. The transient response to graphene's conductivity variations is thoroughly studied and a strategy to enhance the antenna performance by exploiting the time-varying graphene oxide is proposed. Finally, the use of the featured antenna for modern sensing applications is demonstrated through the real-time monitoring of voltage variation.

Cross-section spectra and transient characteristics of Er3+ emissions in Gd3(Al, Ga)5O12 garnet single crystal

We measured fluorescence spectra of visible and infrared electronic transitions at 530 (2H11/2 → 4I15/2), 560 (4S3/2 → 4I15/2), 670 (4F9/2 → 4I15/2), 980 (4I11/2 → 4I15/2), 1530 (4I13/2 → 4I15/2) and 2700 nm (4I11/2 → 4I13/2) of Er3+ in Gd3(Al0.4Ga0.6)5O12 garnet single-crystal excited at the wavelength 520 nm (for the 980 nm emission) or 980 nm (for other emissions). From measured fluorescence spectrum, Er3+ emission cross-section spectrum was calibrated for each transition. Absorption cross-section spectra were further derived on the basis of McCumber equation, and compared with those spectra obtained from measured absorption spectrum on the basis of the Beer-Lambert law. The comparison showed that the results given by two methods can be regarded as same within the error. The transient features of visible and infrared emissions were also studied under pulse excitation. The results showed that, while the three IR emissions at 980, 1530 and 2700 nm all follow a mono-exponential function, both green and red 980-nm-upconverted emissions at 560 and 670 nm exhibit non-mono-exponentially decaying feature, and each decaying trace can be fitted by a sum of two exponential functions. The non-mono-exponential feature is related to cross relaxation caused by non-radiative short-distance energy transfer between clustered Er3+ sites.

Ultrafast Dynamics of the Topological Semimetal GdSbxTe2–x–δ in the Presence and Absence of a Charge Density Wave

Time-resolved dynamics in charge-density-wave materials have revealed interesting out-of-equilibrium electronic responses. However, these are typically only performed in a single material possessing a CDW. As such, it is challenging to separate subtle effects originating from the CDW. Here, we report on the ultrafast dynamics of the GdSbxTe2–x–δ series of materials where EF can be tuned, resulting in a change from an undistorted tetraganal phase to a CDW with a wavevector that depends on x. Using mid-infrared, near-infrared, and visible excitation, we find the dynamics are sensitive to both EF and the presence of the CDW. Specifically, as the Sb content of the compounds increases, transient spectral features shift to higher probe energies. In addition, we observe an enhanced lifetime and change in the sign of the transient signal upon removing the CDW with high Sb concentrations. Finally, we reveal fluence- and temperature-dependent photoinduced responses of the differential reflectivity, which provide evidence of transient charge density wave suppression in related telluride materials. Taken together our results provide a blueprint for future ultrafast studies of CDW systems.

A Two-Stage Rolling Bearing Weak Fault Feature Extraction Method Combining Adaptive Morphological Filter with Frequency Band Selection Strategy

To extract the weak fault features hidden in strong background interference in the event of the early failure of rolling bearings, a two-stage based method is proposed. The broadband noise elimination ability of an adaptive morphological filter (AMF) and the superior capability of a frequency band selection (FBS) strategy for fault transient location identification are comprehensively utilized by the proposed method. Firstly, the AMF with a simple theory and high calculation efficiency is used as a preprocessing program to enhance the fault transient features. Then, the proposed FBS strategy based on the sparsity index (SI) is utilized to further handle the filtered signal processed by the AMF. Finally, the constructed optimum bandpass filter based on the analysis result of the FBS is used to further filter the handled signal processed by AMF and envelope spectral analysis is applied on the last filtered signal to realize the ideal fault feature extraction effect. Compared with the other traditional FBS methods based on kurtosis or the other index, the proposed FBS strategy based on SI has strong robustness to noise. One experimental signal and one engineering vibration signal are used, respectively, to verify the feasibility of the proposed method.

Stationary and Transient Asymmetric Features in Tropical Cyclone Eye with Wavenumber-1 Instability: Case Study for Typhoon Haishen (2020) with Atmospheric Motion Vectors from 30-Second Imaging

Abstract Dynamics of low-level flows in the eye of Typhoon Haishen (2020) in its late phase of intensification are investigated with a special rapid-scan observation of the Himawari-8 geosynchronous satellite conducted every 30 s. This is accomplished by deriving storm-relative atmospheric motion vectors at an unprecedentedly high spatiotemporal resolution by tracking clouds across five consecutive visible-light reflectivity. The overall low-level circulation center was situated several kilometers away from the storm center defined in terms of the inner edge of the lower part of eyewall clouds. The shift direction is rearward of the storm translation, consistently with a numerical study of the tropical cyclone (TC) boundary layer. Over the analysis period of 10 h, azimuthal-mean tangential wind around this center was increased at each radius within the eye, and the rotational angular velocity was nearly homogenized. The instantaneous low-level circulation center is found to orbit around the overall circulation center at distances around 5 km. Its orbital angular speed was close to the maximum angular speed of azimuthal-mean tangential winds. This rotating transient disturbance is found to transport angular momentum inward, which explains the tangential wind increase and the angular velocity homogenization in the eye. These features are consistent with an algebraically growing wavenumber-1 barotropic instability, whose impact on TC structures has not been explored. This instability enhances wavenumber-1 asymmetry in ring-shaped vorticity, which can be induced by various processes such as translation, environmental shear, and exponential barotropic instability. Therefore, it may appear broadly in TCs to affect wind distribution in their eyes. Significance Statement Axially asymmetric transient features in the inner cores of tropical storms have been suggested to profoundly affect the structures and the time evolutions of tropical storms. However, the scarcity of observations has hindered studying such processes observationally. By using a specially conducted high-frequency satellite imaging of Typhoon Haishen (2020), we derived atmospheric motion vectors nearly homogeneously at an unprecedentedly high spatiotemporal resolution. Various kinds of asymmetric motions in low-level flows in the eye were found. Of particular interest is a special type of wavenumber-1 instability whose role has not drawn much attention; the instability was found to provide angular momentum transport consistent with the measured homogenization of the rotation.

Some transient mechanical features of salt caverns behaviour

Some transient mechanical features of salt caverns behaviour

Monitoring the Process and Characterizing Symptoms of Suckling Mouse Inoculation Promote Isolating Viruses from Ticks

Objective: Suckling mouse inoculation is an important method that has been used for years to isolate viruses from ticks; however, this method has usually been briefly described in the literature on a case-by-case basis upon successful isolation rather than providing extensive details. Methods: This study describes the procedure from preparation of tick homogenates to identification of virus isolation using the suckling mouse inoculation method. The transient and persistent features were characterized and the incidence of manifestations that developed in the suckling mice, especially in mice from which viruses were isolated, is reported. Results: We identified 22 symptoms that developed in mice, including 13 transient symptoms that recovered by the end of the observation period and 7 persistent symptoms that the mice suffered from throughout the observation period. Persistent symptoms (lateral positioning and dead) and transient symptoms (malaise, emaciation, and difficulty turning over) were the main symptoms based on the high overall incidence. Moreover, we showed that mice from which viruses were isolated had a concentrated period and advanced days of disease onset. Conclusion: This study provides detailed information necessary for better use of suckling mouse inoculation to isolate viruses from ticks, which may benefit optimization of this method to identify, discover, and acquire tick-borne viruses.

Imaging PIP2 and BCR microclusters in B cell immunological synapse.

The humoral immune response is dependent on B cell activation and differentiation, which is typically triggered by the formation of immunological synapses at the interface between B cells and the antigen presenting surfaces. However, due to the highly dynamic and transient feature of immunological synapses, it has been difficult to capture and investigate the molecular events that occur within them. The planar lipids bilayer (PLB) supported antigen presenting surface combined with high-resolution high-speed total internal reflection fluorescence microscope (TIRFM) live cell imaging system has been proved to be a powerful tool that allows us to visualize the dynamic events in immunological synapse. In addition, the phospholipid phosphatidylinositol-(4,5)-biphosphate (PIP2) plays a unique role in B cell activation, and it is difficult to investigate the synaptic dynamics of PIP2 molecules. Hence, we describe here the general procedures for the utilization of a PLB based antigen presenting system combining TIRFM based imaging methods to visualize the spatial-temporal co-distribution of PIP2 and BCR microcluster within the B cell immunological synapse.

Fault transient feature extraction method of 66kV transmission line based on improved wavelet threshold and CEEMDAN-FME

In the 66kV transmission line, the compensation effect of the arc suppression coil, so the amplitude of fault zero sequence current is weak after the occurrence of single-phase grounding responsibility. The fault characteristics are not obvious, and there is environmental noise interference during fault signal acquisition, which further increases the difficulty of ground fault feature extraction. To solve the above problems, a fault transient feature extraction method of 66kV transmission line is proposed, based on improved wavelet threshold and Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) - Fuzzy measure entropy (FME). The characteristics of single-phase grounding transient fault in small current grounding system are analyzed. The transient fault signal was preprocessed by an improved wavelet threshold method, and the transient fault characteristics were extracted accurately by CEEMDAN-FME processing. The proposed method has good anti-noise performance through the simulation comparison and verification of denoising processing and transient feature extraction by MATLAB. It can effectively extract fault features when the interference degree is significant.

A full generalization of the Gini index for bearing condition monitoring

The classic Gini index (GI) is generalized recently by using nonlinear weight sequences as sparsity measures for sparse quantification and machine condition monitoring. The generalized GIs with different weight parameters are more robust to random transients. However, they show insufficient performance in discriminating repetitive transients under noise contamination. To overcome this shortage, this paper proposes a two-parameter generalization method to tune not only the weight parameter but also the norm order, allowing for a full generalization of the classic GI to quantify transient features and leading to new statistical indicators which are named fully generalized GIs (FGGIs). Mathematical derivations show that FGGIs satisfy at least four of the six typical attributes of sparsity measures and that those with weight parameter equal to one satisfy at least five sparse attributes, proving that they are a new family of sparsity measures. Numerical simulations demonstrate that FGGIs can monotonically evaluate the sparseness of the signals and that the FGGIs with appropriate parameters exhibit improved performance in resisting random transient interferences and discriminating noise-contaminated repetitive transients compared to traditional sparsity measures. The performance of FGGIs in the condition monitoring of rolling element bearings is validated using two different run-to-failure experiment datasets, including a gradual failure and a sudden failure. The results show that increasing the norm order can improve the capability of FGGIs to characterize transient fault features, allowing more accurate trending of bearing health conditions, and therefore achieving better condition monitoring performance than the traditional sparsity measures.

Femtosecond Extreme Ultraviolet Spectroscopy of an Iridium Photocatalyst Reveals Oxidation State and Ligand Field Specific Dynamics

Femtosecond X-ray absorption spectroscopy at the Ir O3-edge and N6,7-edges is performed on the photocatalyst iridium(III) tris(2-phenylpyridine), Ir(III)(ppy)3 using a tabletop high-harmonic source. Extreme ultraviolet (XUV) absorption between 44 and 76 eV measures transitions from the Ir 5p3/2 and 4f5/2,7/2 core to 5d valence orbitals, and the position of these spectral features is shown to be sensitive to the oxidation state and ligand field of the metal center. Upon excitation of the singlet metal-to-ligand charge transfer (1MLCT) band at 400 nm, a shift in the spectra due to the formation of the Ir(IV) center is observed, as is the creation of a new spectral feature corresponding to transitions into the t2g hole. Vibrational cooling of the MLCT state on the 3 and 16 ps time scales is measured as changes in the intensity of the transient features. This work establishes XUV spectroscopy as a useful tool for measuring the electronic structure of third row transition metal photosensitizers and catalysts at ultrafast time scales.

Photoinduced chlorophyll charge transfer state identified in the light-harvesting complex II from a marine green alga Bryopsis corticulans

The light-harvesting complex II of Bryopsis corticulans (B-LHCII), a green alga, differs from that of spinach (S-LHCII) in chlorophyll (Chl) and carotenoid (Car) compositions. We investigated ultrafast excitation dynamics of B-LHCII with visible-to-near infrared time-resolved absorption spectroscopy. Absolute fluorescence quantum yield (Φ FL) of LHCII and spectroelectrochemical (SEC) spectra of Chl a and b were measured to assist the spectral analysis. Red-light excitation at Chl Qy-band, but not Car-band, induced transient features resembling the characteristic SEC spectra of Chl a ⋅+ and Chl b ⋅-, indicating ultrafast photogeneration of Chl-Chl charge transfer (CT) species; Φ FL and 3Car∗ declined whereas CT species increased upon prolonging excitation wavelength, showing positive correlation of 1Chl∗ deactivation with Chl-Chl CT formation. Moreover, ultrafast Chl b-to-Chl a and Car-to-Chl singlet excitation transfer were illustrated. The red-light induction of Chl-Chl CT species, as also observed for S-LHCII, is considered a general occurrence for LHCIIs in light-harvesting form.

Transient Polycomb activity represses developmental genes in growing oocytes

BackgroundNon-genetic disease inheritance and offspring phenotype are substantially influenced by germline epigenetic programming, including genomic imprinting. Loss of Polycomb Repressive Complex 2 (PRC2) function in oocytes causes non-genetically inherited effects on offspring, including embryonic growth restriction followed by post-natal offspring overgrowth. While PRC2-dependent non-canonical imprinting is likely to contribute, less is known about germline epigenetic programming of non-imprinted genes during oocyte growth. In addition, de novo germline mutations in genes encoding PRC2 lead to overgrowth syndromes in human patients, but the extent to which PRC2 activity is conserved in human oocytes is poorly understood.ResultsIn this study, we identify a discrete period of early oocyte growth during which PRC2 is expressed in mouse growing oocytes. Deletion of Eed during this window led to the de-repression of 343 genes. A high proportion of these were developmental regulators, and the vast majority were not imprinted genes. Many of the de-repressed genes were also marked by the PRC2-dependent epigenetic modification histone 3 lysine 27 trimethylation (H3K27me3) in primary–secondary mouse oocytes, at a time concurrent with PRC2 expression. In addition, we found H3K27me3 was also enriched on many of these genes by the germinal vesicle (GV) stage in human oocytes, strongly indicating that this PRC2 function is conserved in the human germline. However, while the 343 genes were de-repressed in mouse oocytes lacking EED, they were not de-repressed in pre-implantation embryos and lost H3K27me3 during pre-implantation development. This implies that H3K27me3 is a transient feature that represses a wide range of genes in oocytes.ConclusionsTogether, these data indicate that EED has spatially and temporally distinct functions in the female germline to repress a wide range of developmentally important genes and that this activity is conserved in the mouse and human germlines.

A Data-Driven Fault Prediction Method for Nuclear Power Systems Based on End-to-End Deep Learning Framework

With the increase in system complexity and operational performance requirements, nuclear energy systems are developing in the direction of intelligence and unmanned, which also requires a higher demand for its safety so that intelligent fault diagnosis and prediction have become a technology that nuclear power plants need to develop at present. At the same time, due to the rapid development of deep learning technology, it has become a meaningful development direction to predict the fault state of nuclear power plants within the framework of supervised deep learning. Usually, the network structure model used in fault diagnosis and prediction requires professional design, which may cost a lot of time and make it difficult to achieve optimal results. For this purpose, we present an end-to-end deep network for nuclear power system prediction (EDN-NPSP), which can automatically mine the transient features of various detection data in the NPS at the current moment through heterogeneous convolution kernels that can increase the receptive field and then predict the feature evolution results of the NPS in the future through a special deep CNN. The results provide an assessment of the future state of NPS. Based on EDN-NPSP presented in this work, we can avoid complicated manual feature extraction and provide the predicted state directly and rapidly. It will provide operators with useful prediction information and enhance the nuclear energy system fault prediction capabilities.

Robust event detection for residential load disaggregation

Nonintrusive load monitoring (NILM) can facilate the transition to energy-efficient and low-carbon buildings. Event detection is the first and most critical step in event-based NILM and can improve the efficiency and performance of NILM by decreasing inference times to the number of events with transient features extracted from events. However, existing event detection methods with fixed parameters may fail to achieve high accuracy in the case of uncertain and complicated residential load changes such as high fluctuation, long transition, and near simultaneity in both power and time dimensions. Besides, it is difficult to transfer the fixed parameter to new households with different load profiles. Furthermore, most of these methods prove that they are able to detect events but not able to extract features for load disaggregation. This paper proposes a robust event detection method with adaptive parameters to deal with such issues. Specifically, a window with adaptive margins, multi-window screening, and adaptive threshold method is proposed to detect events in aggregated load data with high sampling rate (>1 Hz). The proposed method captures the transitions by adaptively tuning parameters including window width, margin width, and thresholds. It can also achieve good performance with blind parameter setting so that it is suitable for unknown households or datasets. Furthermore, it captures complete transitions that are indispensable for transient feature extraction. Case studies on a 20 Hz dataset, the 50 Hz LIFTED dataset, and the 60 Hz BLUED dataset show that the proposed method can robustly outperform other state-of-the-art event detection methods. The robust performance of the proposed method is also verified by a cross validation of parameters among different datasets. Lastly, the proposed event detection method is demonstrated to have the merits of improving the performance of load disaggregation.

Excited State and Transient Chemistry of a Perylene Derivative (DBP). An Untold Story.

Transient chemistry of sensitizing dyes is important to obtain insights into the photochemical conversion processes of light harvesting assemblies. We have now employed transient absorption spectroscopy (pulsed laser and pulse radiolysis) to characterize the excited state and radical intermediates of a perylene derivative, (5,10,15,20-Tetraphenylbisbenz[5,6]indeno[1,2,3-cd:1',2',3'-lm]perylene (DBP). The distinguishable transient absorption features for the singlet and triplet excited states and radical anion and radical cation provide spectral fingerprints to identify the reaction intermediates in photochemical energy and electron transfer processes of composite systems involving DBP. For example, identifying these transients in the energy transfer processes of the rubrene-DBP system would aid in establishing their role as annihilator-emitter for triplet-triplet annihilation up-conversion (TTA-UC). The transient characterization thus serves as an important mechanistic fingerprint for elucidating mechanistic details of systems employing DBP in optoelectronic applications.

Role of micellar entanglements on kinetics of shear banding flow formation

We investigate the effects of micellar entanglement number on the kinetics of shear banding flow formation in a Taylor–Couette flow. Three sets of wormlike micellar solutions, each set with a similar fluid elasticity and zero-shear-rate viscosity, but with varying entanglement densities, are studied under the startup of steady shear. Our experiments indicate that in the set with low fluid elasticity, the transient shear banding flow is characterized by the formation of a transient flow reversal in a range of entanglement densities. Outside of this range, the transient flow reversal is not observed. For the sets of medium and high elasticities, the transient flow reversals exist for relatively small entanglement densities and disappear for large entanglement densities. Our analysis shows that wall slip and elastic instabilities do not affect the transient flow feature. We identify a correlation between micellar entanglement number, the width of the stress plateau, and the extent of the transient flow reversal. As the micellar entanglement number increases, the width of the stress plateau first increases; then, at a higher micellar entanglement number, the plateau width decreases. Therefore, we hypothesize that the transient flow reversal is connected to the micellar entanglement number through the width of the stress plateau.

Acoustic feature enhancement in rolling bearing fault diagnosis using sparsity-oriented multipoint optimal minimum entropy deconvolution adjusted method

Rolling element bearings are of great importance and widely used in rotating machineries, whose fault detection and diagnosis (FDD) are essential for insuring reliability of the entire mechanical system. Therefore, extracting fault-related transient features from noisy signals to reveal bearing weak fault is a crucial prerequisite for long term condition monitoring. However, in complex working conditions, the measured acoustic signals are typically multi-component, submerged by strong interference noise and affected by unknown transmission path, resulting in the indistinctive fault-induced features and unsatisfactory diagnosis accuracy. To tackle this problem, a sparsity-oriented Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) method is proposed for bearing fault feature enhancement and diagnosis based on acoustic signals. As a non-iterative blind deconvolution approach, MOMEDA has been proven to be an effective tool to extract fault-related impulses from the noisy signal and compensate the complex unknown transmission path, enabling target propositioning and fault indication of bearings at an earlier termination condition. Furthermore, a sparsity operation which is originally designed for acoustic signal analysis, based upon the Laplace distribution characteristics of the fault-induced outliers, can further suppress the noise component and enhance the periodic fault impulses. The feasibility and effectiveness of the proposed sparsity-oriented MOMEDA method is validated by both simulated and experimental data. It is worth mentioning that bearing cage fault diagnosis and bearing mixed fault diagnosis, usually receiving limited attention before, are also investigated by the proposed method. The results demonstrate that the presented method gets preferable performance than existing methods, and it can achieve robust FDD of rolling bearings based on acoustic signals.