Impulse Characteristics Research Articles

Multioperator Morphological Undecimated Wavelet for Wheelset Bearing Compound Fault Detection

Wheelset bearing compound faults are observed as impulses in the vibration measurements but immersed in noise. The morphological undecimated wavelet (MUW) is an effective tool for recovering fault-related impulses from vibration mixtures. To date, the reported MUWs adopt an identical morphological operator in each level of decomposition without exception. However, effectively capturing signal signatures by repeatedly using a noise elimination operator, an impulse extraction operator, or even the product of two operators is unattainable. Spurred by this deficiency, in this paper, we propose a multi-operator MUW (MOMUW). A three-level structure: noise reduction, impulse extraction, and further denoising and feature enhancement, is developed in the MOMUW, and different morphological operators are designed for each decomposition level to more purposefully denoise and extract impulse features. The developed MOMUW is applied to measured wheelset bearing vibration data, with the results demonstrating that it can accurately detect wheelset bearing compound faults. Compared with the reported MUWs, the proposed approach presents superior performance. Furthermore, MUWs with varying operators and levels are analyzed and compared. Their success and failure in bearing fault diagnosis are interpreted and discussed, laying a theoretical foundation for constructing new MUWs.

Tensor Denoising Assisted Time-Reassigned Synchrosqueezing Wavelet Transform for Gear Fault Diagnosis

The gear fault signal is characterized by strong and/or weak impulsive features and is often accompanied by strong background noise, which makes the gear fault feature extraction more difficult. Hence, this paper proposes a new time-frequency analysis method called the tensor denoising (TD) assisted time-reassigned synchrosqueezing wavelet transform (TD-TSWT). First, to improve the accuracy of subsequent time-frequency analysis, a TD method based on high-order singular value decomposition is proposed as a preprocessing technique according to the cyclostationary characteristics of gear faults and the Hankel matrix. Meanwhile, combined with the advantages of sample entropy calculation, the concept of using sample entropy to determine the reasonable singular order is proposed. Second, to propose TSWT for revealing gear fault features. TSWT by introducing continuous wavelet transform to obtain the basis function which is more similar to gear fault impulses. Moreover, according to the properties of the gear fault signal, the group delay estimation operator in the frequency domain is designed for TSWT, which is more suitable for the gear fault feature extraction. Finally, the results of simulations and engineering cases show that the method provides a useful tool for the diagnosis of gear faults.

Analysis of heating of printed circuit board conductors on a metal base for spacecraft devices at pulsed current

The article presents the results of the analysis of heating of printed circuit conductors of printed circuit boards mounted on a metal base, on-board device of spacecraft when pulsed current flows through them. Thermal transient and impulse characteristics of a system consisting of a printed conductor and a printed circuit board are considered. The analysis of the dependence of the temperature rise rate of printed conductors and the duration of transient processes on the linear dimensions of the system elements is given. It is noted in the article that the time of transient processes most strongly depends on the thickness of the layer of insulating materials. All the results were obtained in the process of modeling transients in the ANSYS 2019 R1 Transient Thermal module. Practical recommendations on the consideration of thermal transients in printed conductors in the design of printed circuit boards are given.

An adaptive generalized logarithm sparse regularization method and its application in rolling bearing fault diagnosis

The generalized logarithm sparse regularization method (G-log) for fault diagnosis of rotating devices can effectively reconstruct repetitive transient shocks from noise-disturbed signals, but its reconstruction accuracy frequently becomes inferior due to unsuitable regularization parameters. Moreover, conventional sparse regularization methods perform nothing on the input signals to guarantee that the impulse characteristics remain constant during the entire iteration process, which exacerbates the influence of noise on the reconstruction accuracy. To overcome these challenges, an adaptive generalized logarithm sparse regularization method (AG-log) based on the second-order cyclostationary indicator (ICS2) and the improved maximum correlation Pearson correlation coefficient deconvolution (IMCPCCD) method is proposed in this paper. Firstly, the optimal threshold parameter k for each iteration of AG-log is determined based on the ICS2 criterion to ensure the optimal reconstruction accuracy, while the optimal combination of iteration numbers N and k is established. Secondly, the original signal and the IMCPCCD filtered signal are alternately used as the input signal of AG-log according to the parity of the iterative steps to reduce the interference of noise. Finally, the application on simulated and two engineering case signals demonstrates that AG-log has better reconstruction accuracy compared with conventional nonconvex sparse regularization methods.

Prevalence of Psychopathy, Intimate Partner Homicide, and Suicide Risk in Spain

The most extreme form of intimate partner violence against women (IPVAW) is female intimate partner homicide (female IPH). The main goal of this study was to analyze the prevalence of psychopathy in a sample of Spanish male offenders of female IPH. This study also aimed to examine the relationship between psychopathy and committing suicide after female IPH in a sample of 76 Spanish male offenders of female IPH. We examined the presence or lack of psychopathic traits or psychopathy itself, using the two factors and four facets of the Hare Psychopathy Checklist-Revised (PCL-R). Applying this procedure, we obtained a series of descriptive statistics and frequencies, to which we applied Student’s t-test and variance analyses. We found a low prevalence of subjects diagnosed as psychopath at a cut-off score equal or greater than 30 on the PCL-R, but a higher prevalence was found with a cut-off score of 25. The presence of factor I traits (personality and interpersonal characteristics) was greater than factor II (impulsive and antisocial behavior) among female IPH offenders. Higher scores on factor II and lower on factor I were related to an increased risk of suicide after committing female IPH. The study data support international research showing a significant presence of factor I traits (e.g., callousness and lack of empathy) among IPH offenders. Also, these data support that those male offenders of female IPH who presents characteristics of impulsivity and antisocial behavior do tend to commit suicide more frequently after committing female IPH.

Trichotillomania, Skin Picking Disorder, and Different Aspects of Impulsivity: A Systematic Review

Trichotillomania and skin picking disorder are psychiatric disorders with body-focused repetitive behaviors (BFRB). Impulsivity is considered to have a role in the etiology of BFRB. Various methods have been implemented to demonstrate different aspects of impulsivity due to its multidimensional construct. This systematic review aimed to synthesize existing studies to examine different aspects of impulsivity in BFRB. A systematic literature review was conducted in January 2021. Some original studies evaluating the relationship between impulsivity and trichotillomania or skin picking disorder were obtained. Twenty studies were examined after a systematic search of the MEDLINE database. The findings revealed that impulsivity was investigated using a self-report scale in five studies and behavioral tasks in 18 studies. Five studies reported higher trait impulsivity in BFRB with different self-report scales. Differences have been found in the urgency, motor, and attentional subscales. Eight studies measuring motor impulsivity (response inhibition) with behavioral tasks showed higher impulsivity in BFRB and its subgroups. Three studies evaluating cognitive impulsivity with behavioral tasks reported that BFRB is not related to reflection and decision-making impulsivity. However, functional alterations in BFRB patients fMRI, during a choice impulsivity task, were shown in one study only. Different aspects of impulsivity are associated with BFRB. The most commonly identified impairment with behavioral tasks, response inhibition impairment, is conceptually similar to the urgency and motor subscales determined by self-report scales. These results support the patients' self-reports and explain the patients' tendency to act rashly under an increasing sense of tension. However, further studies should be conducted to make a definite conclusion about cognitive impulsivity characteristics of BFRB patients. [ Psychiatr Ann . 2022;52(12):527–540.]

Fault Feature of Gearbox Vibration Signals Based on Morphological Filter Dynamic Convolution Autoencoder

Vibration signals are widely used in fault diagnosis of rotating machinery. Although deep neural networks (DNNs) have been widely used in feature learning of vibration signals, they have not shown good performance in the extraction of impulsive features and noise filtering. In this study, a new DNN, morphological filter dynamic convolutional autoencoder (MF-DCAE), is proposed for fault feature extraction from vibration signals. First, a multiscale morphological filter layer embedded with a convolutional autoencoder (CAE) is proposed to extract fault features of vibration signals. Kurtosis is used to fuse the feature information extracted by morphological operators with different scales. Dynamic convolution is used to adjust the weight of each convolution kernel for adaptive feature extraction. Finally, residual learning is used to connect the encoder and decoder to obtain good feature learning of vibration signal. The effectiveness of MF-DCAE is verified on two gearbox testbeds. The results show that MF-DCAE can perform both noise reduction and feature learning on vibration signals in an unsupervised learning manner. The comparison results show that the feature extraction performance of MF-DCAE is better than that of the state-of-the-art DNNs.

Sparse Representation Convolutional Autoencoder for Feature Learning of Vibration Signals and its Applications in Machinery Fault Diagnosis

Vibration signals are widely utilized in many fields, which can reflect machine health state. Those typical deep learning techniques cannot learn impulsive features from vibration signals due to interference of strong background noise. Supervised learning greatly rely on vast labeled data, which limits the implementation of deep learning in industry applications. Hence, in this article, a new deep neural network (DNN), sparse representation convolutional autoencoder (SRCAE), is proposed to extract impulsive components of vibration signals for machinery fault diagnosis in an unsupervised manner. A sparse representation (SR) block is proposed to extract impulsive components of vibration signals and transform the time-domain signal to a sparse domain by sparse mapping of a convolutional graph. The SR block is inserted into a deep network to remove noise and learn impulsive features for machinery fault diagnosis. Furthermore, an unsupervised selective feature transmission mechanism is proposed to improve training efficiency and realize feature filtering simultaneously. Finally, the effectiveness of SRCAE is verified on rotary machine fault diagnosis experiments. The testing results show that SRCAE has good noise filtering and impulsive components extraction performance. The recognition accuracy of SRCAE reached 97.16% based on the fivefold cross validation, which demonstrates the outperformance of SRCAE in comparison with state-of-the-art DNNs.

Evaluation of Psychological Resilience, Alexithymia, and Impulsivity Characteristics in Persons Who Attempted Suicide with Drugs.

In this study, it was aimed to research the connection between psychological resilience, alexithymia, depression, anxiety and impulsivity in patients who attempted suicide with drugs. The patient group (n = 66) who attempted suicide with drugs and the control group (n = 66) without any psychiatric diagnosis were examined in the study. Sociodemographic data form, Psychological Resilience Scale, Toronto Alexithymia Scale (TAS-20), Beck Depression Inventory, Beck Anxiety Inventory, Barratt Impulsivity Scale (BIS-11) were used in the study. It was found that PRS dedication and challenge are significantly higher in the control group (p = .039 and p = .003, respectively). When the undiagnosed group who attempted suicide were compared with the control group, it was observed that there is a significant difference between BIS-11 total and TAS total after bonferonni correction (respectively; p = .002; p = .001), while, there is no statistically significant difference in terms of PRS total score (p = .483). It was observed that patients who attempted suicide with drugs had low levels of psychological resilience, high alexithymia levels. In the absence of an additional diagnosis of depression, psychological resilience did not differ in terms of suicide attempts. In the suicide group, those who did not receive any additional diagnosis were not more impulsive than those who were depressed.

Development of electric scooter alerting sounds using psychoacoustical metrics

In recent years electric micromobility transportation, including electric scooters, has seen a surge in popularity due to technological advances and the move to lower emission transport. Although offering a range of societal benefits, such as reduced pollution and increased personal mobility, concerns have been raised regarding the implications for pedestrian safety, most notably within the blind and partially sighted community. The issue of pedestrian safety is well studied in the context of larger electric vehicles (EVs), and indeed regulations are now in place that specify mandatory Acoustic Vehicle Alerting Systems (AVAS) for such vehicles. However, limited research has been done on the development of acoustic alerting systems for micromobility. In this paper, the development of an electric scooter (e-scooter) AVAS is considered by taking a perception-influenced design approach to designing alert sounds that optimise detectability and annoyance. A listening experiment has been conducted using ambisonic soundscapes and simulated auralisations of e-scooter passes at 20 km/h, in which a detection-based task and annoyance rating task were conducted. Objective metrics for detectability and annoyance were subsequently evaluated in relation to the subjective responses, so as to enable a more focused approach to the development of alert sounds. Results show that without additional alert sounds, the rate of detection for e-scooters in a soundscape of 60 dBA is as low as 23%. Regression analysis showed that the objective metric of Zwicker’s psychoacoustic annoyance is a useful predictor of subjective annoyance for AVAS sounds, with a coefficient of determination of R2=0.96, and explains more variance than other metrics previously reported in the literature. Partial loudness was also studied as a predictor of detectability, with strong positive association seen (R2≈0.9). Of the alert sounds evaluated, those comprising pure tones with frequency content in the 800 Hz - 1 kHz range, and with amplitude modulation or impulsive characteristics, offered the greatest balance between detectability and annoyance. This study offers much needed research into detectability of electric micromobility transport in a range of environmental noise conditions, and furthermore provides objective metrics for the development of micromobility AVAS sounds going forward.

Investigation of Impulse and Continuous Discharge Characteristics of Large-Capacity Lithium-Ion Batteries

Lithium-ion batteries are one of the most popular and efficient energy storage devices. In this paper, the characteristics of high-capacity lithium-iron-phosphate batteries during the impulse and long-term operation modes of batteries with different levels of the discharge current are considered. A modified DP-model is proposed. The novelty of the model is the possibility to calculate the activation polarization parameters for different discharge currents. The state of charge is estimated using a high-order polynomial. Based on the developed model, transient processes with rapid load changes and the dependence of the battery voltage on the state of charge were obtained. Here, the model is intended to be used for the design of energy storage systems. The results showed that the DP-model is reliable under the tested conditions and can be used for the considered application.

Validity of the law of similarity for impulse and DC breakdown of gases in the vicinity of the Paschen’s minimum

The study discusses the validity of the law of similarity for the impulse breakdown of gases at low pressure and small inter-electrode distances. The research has been done theoretically, experimentally and numerically. The experiments were conducted under well-controlled laboratory conditions. The combined measurement uncertainty was less than 5%. The experiments were carried out with gases SF6, N2 and He. The electrodes were made of copper, but also of metals with different values of the work function. The obtained results are presented through the impulse characteristics for similar systems. Such a presentation only provided qualitative information. In order to obtain quantitative information, a modified Wilcoxon Rank Sum Test (U test) was performed. The obtained results show that the law of similarity is valid for the DC breakdown of gases, but it is not valid for the impulse breakdown of gases. It can be concluded that the law of similarity is inapplicable for the design of gas-insulated systems under low pressure at small values of the inter-electrode distance.

Multi-physics coupling analysis on neutronics, thermal hydraulic and mechanics characteristics of a nuclear thermal propulsion reactor

As a highly developmental potential propulsion technology, nuclear thermal propulsion (NTP) technology has characteristics of high power and high specific impulse. A large number of relevant studies for NTP reactor have been carried out, but most studies only focused on the overall reactor performance parameters, ignoring the core reactivity safety and local assembly behavior safety. In this study, a cross-scale neutronics, thermal hydraulic and mechanics coupling model of the core-assembly is established by using three-dimensional Monte Carlo code and commercial CFD program to analyze three aspects: reactor control response security characteristics, heat release homogeneity of reactor and thermal hydraulic parameters. By means of value comparison of control system, three-dimensional power flattening and sensitivity analysis of single variable thermal–hydraulic parameter, the thermal hydraulic temperature safety and mechanical thermal stress safety of assemblies are significantly improved under control safety and inherent safety, which proves the importance of the neutronics, thermal hydraulic and mechanics characteristic balance.This paper can provide a theoretical basis for study on the assembly multi-physics behavior and safety characteristics of NTP reactors.

Fault feature extraction for planetary bearing of CRF pump in nuclear power plant based on TFDC-QPSO-optimised MOMEDA

To address the issue of weak fault features and difficulty in feature extraction for planetary gearbox bearings of the circulation water pump (CRF pump) unit in the conventional island of a nuclear power plant, a deconvolution method, named the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is optimised by a mixing operator improved quantum behaviour particle swarm optimisation (QPSO) algorithm, to extract the periodic fault impulse of planetary gearbox bearings. In the mixing operator, a differential evolution operator is introduced to improve particle swarms’ diversity and enhance the algorithm’s global optimisation capability. Meanwhile, a proposed adaptive CrossOver operator is incorporated into the algorithm to increase its convergence speed. Then, combining these two types of operators can construct a parameter optimisation algorithm displaying both global optimisation capability and high algorithm execution efficiency. Consider capturing the periodic impulsive features relating to bearing faults. A fitness function based on the characteristic information of the deconvolution signal in the time and frequency domain is proposed. This function serves as the objective function to facilitate the parameter optimisation on MOMEDA. Thereby, it enhances the optimisation capability of our feature extraction method. Experiments were conducted by adopting the proposed method on the signals collected from the inner- and outer-race faulty planetary bearing in a test bed, in which favourable feature extraction results are obtained. After the comparative analysis, we observed that both significances of features extracted by this method and the execution efficiency of the algorithm are superior compared to other methods. The on-site feature extraction results of the input shaft bearing in the planetary gearbox of a CRF pump unit in a nuclear power plant also demonstrated the engineering practicability of the method proposed in this work.

Characterization of nanosecond laser-induced underwater filamentation using acoustic measurements.

This investigation aims to understand the temporal and spectral characteristics of underwater acoustic impulses due to ∼10n s laser-induced filamentation at various incident laser energies. In addition to the experimental study, finite element analysis (FEA) has been used in conjunction with experimentally acquired data to simulate and visualize acoustic impulse propagation and interaction across the water-rigid boundary. In the time domain, the increase in the underwater filament length is measured by the peak-to-peak (P k-P k) overpressures as a function of input laser energies. The P k-P k overpressures are maximum around the focal plane and are symmetrical on either side. This variation in the P k-P k overpressure is traced to characterize the spatial extent of the underwater filament. The arrival time of underwater acoustic impulse varies within the 8-9µs for input laser energies. It was observed that with increasing laser beam power, the conversion efficiency of the acoustic impulse decreases, and the underwater filament length increases. In the frequency domain, with increasing input laser energy, the peak frequency of the underwater acoustic impulse shifts toward the lower frequencies. Using FEA, the short-time Fourier transform spectrogram simultaneously visualizes the arrival time and instantaneous frequency of the simulated acoustic impulse. The simulated acoustic impulse produced a single instantaneous peak frequency of around 76kHz. Upon reflection from the water-rigid boundary, we observed that the reflected signal also has a single instantaneous peak frequency of around 76kHz, while higher-frequency components are completely dissipated. Our results are promising for the development of remote laser-based acoustic generation and sensing applications.

Maximum correlation Pearson correlation coefficient deconvolution and its application in fault diagnosis of rolling bearings

Blind deconvolution (BD) has been proved to be an effective tool for recovering periodic impulses from strong background noise signals is widely used for fault diagnosis of bearings and gears. However, the existing BD focus more on improving the quantity and amplitude-frequency characteristics of interested periodic impulses without considering their phase-frequency characteristics and impulse waveform characteristics (attenuation coefficient and oscillation frequency), which results in severe distortion of the reconstructed signal. In this scenario, a new BD method, named maximum correlation Pearson correlation coefficient deconvolution (MCPCCD), is presented in this paper. Firstly, a new objective function is constructed which include two terms: correlation Pearson correlation coefficient (CPC) and signal fidelity term (SFT). CPC is a new norm with strong sensitivity to interested impulse signals, while SFT is a dimensionless error measure similar to RMSE. Then, the optimal FIR filter is obtained by maximizing the objective function of the reconstructed signal. Meanwhile, a new preprocessing step is designed to reduce the requirement of MCPCCD for periodic prior. Finally, the application on simulated and two experimental signals indicates that MCPCCD significantly outperforms the traditional BD method in recovering the periodic impulses.

Element analysis and its application in rotating machinery fault diagnosis

Conventional signal processing methods make it difficult to extract the fault impulse features from the target signal, and the time-frequency representation has energy ambiguity. Thus, it is critical to develop new approaches for mechanical fault diagnostics. In this paper, the element analysis method, which was originally utilized in the marine field, is applied to the field of mechanical fault diagnosis for the first time. A de-noising technique of rotating machinery signals based on the element analysis method is proposed. The proposed method first determines the corresponding wavelet parameters according to the mechanical fault signals and constructs the element model. Then the method performs the Morse wavelet transform on the element model, and calculates the signal impulse point from the wavelet transform to obtain the signal’s fault characteristic frequency. Furthermore, the method can also reconstruct the signal by utilizing a small number of solitary points in the time or scale plane. The performance of the method is verified by analyzing simulated signals and mechanical vibration signals collected from different experimental platforms. The results demonstrate that the method has excellent signal characteristic extraction capability and successfully diagnoses different kinds of rotating machinery faults.

Research on Transient Torsional Vibration Test of Harmonic Reducer

The torsional vibration of harmonic reducer is related to the positioning accuracy and Trajectory Accuracy of industrial robots. Based on this, this paper studies and experiments the impact response of harmonic reducer. By establishing the torsional vibration model with periodic variable stiffness of harmonic reducer, the unit impulse response and characteristics are analyzed; The time-varying torsional stiffness of the harmonic reducer is verified by setting up an experimental platform for the harmonic reducer, and the transient torsional vibration characteristics of the harmonic reducer are obtained The research will provide basic data for the optimal application of harmonic reducer.

ИМПУЛЬСНЫЕ ХАРАКТЕРИСТИКИ КРЕМНИЕВЫХ СТРУКТУР С N-P ПЕРЕХОДОМ, ОБЛУЧЕННЫХ ПРОТОНАМИ

Currently, methods are being actively developed to create semiconductor structures with desiredproperties by irradiation with ionizing particles (radiation defect engineering). The interactionof radiation defects with impurities, dislocations and other structural defects causes a change in theproperties of semiconductors and semiconductor devices. Irradiation with protons makes it possibleto controllably create radiation defects with a distribution maximum in a pre-calculated region. Theaim of this work is to analyze the effect of irradiation with low-energy protons on the impulse characteristicsof silicon structures with an n+-p junction. The task is to determine the effective lifetime  ofcharge carriers in the space charge region (SCR) of the n+-p junction. The n+-p-p+-structures madeof silicon grown by the Czochralski method, irradiated from the side of the n+-layer by a low-energyproton flux at sample temperatures of 300 K and 83 K were studied. To measure the impulse characteristics,bipolar rectangular voltage pulses with a constant amplitude of 10 mV and a frequency of1 MHz were used. The experimental data are explained using models of nonstationary charge carriertransport in inhomogeneous semiconductors and the formation of radiation defects in silicon underthe action of protons. Depth distributions of the average number of primary radiation defects arecalculated: interstitial silicon, vacancies, divacancies created by one proton per unit length of theprojective path. It is shown that irradiation with protons with a dose of 1015 cm2 and an energy of40 keV does not change the value of , but with an energy of 180 keV creates a region with an effectivelifetime of 5.5108 s in the SCR of the n+-p junction.

Research on the Sparse Optimization Method of Periodic Weights and its Application in Bearing Fault Diagnosis

The vibration state monitoring signal of rolling bearings usually shows periodic transient impulse characteristics at the time of the fault, but in the fault sprouting stage and actual monitoring, its impulse components are easily disturbed by random pulses, harmonics, background noise, etc., which increases the difficulty of extracting the periodic shock characteristics of the fault. This paper proposed a rolling bearing fault feature extraction method based on sparse coefficient weighting theory and periodic enhancement strategy to effectively extract weak periodic shock features with a low signal-to-noise ratio. In the proposed period weighted sparse wavelet representation (PWSWR) method, firstly, based on the sparse wavelet model, the mean kurtosis (MK) index was introduced as a weight to distinguish the contribution of the wavelet coefficients of each signal component. Secondly, a sparse signal period enhancement strategy was proposed using the improved envelope harmonic product spectrum period estimation method. The strategy is embedded into the sparse representation model to achieve the periodic enhancement of filtered signals. Finally, the envelope detection of the sparse signal was carried out to identify the fault. The results of bearing fault simulation signals and two engineering test signals showed that the proposed method could effectively extract weak fault features of bearings and had certain advantages compared with other sparse methods.