Impulsive Components Research Articles

A 1.5D Spectral Kurtosis-Guided TQWT Method and Its Application in Bearing Fault Detection

Bearings are the key parts of rotating machinery, and their operation status is related to the operation safety of the whole equipment. Vibration signals often contain periodic impulse components which can reflect the fault state of bearings. However, due to the interference of signal transmission path and the influence of operating environment noise, the periodic impulse components in the signal are often submerged by the nonperiodic transient impulse components, modulation harmonic components, and noise components. Therefore, the core problem of bearing fault diagnosis theory is used to accurately extract the frequency band of bearing fault state information and suppress the frequency band of interference information. In this paper, the signal is processed by the tunable Q-factor wavelet transform (TQWT), the midfrequency band of the signal is tightly divided by selecting different Q-values, and the 1.5D spectral kurtosis defined in frequency domain is used to select the optimal subband. Simulated analysis shows that this method can avoid low-frequency harmonic interference, nonperiodic transient impulse components, and strong noise components in the time domain. Therefore, it can effectively realize the selection of the subbands of periodic impulse components and effectively extract fault feature information. Through experimental signal analysis, TQWT has good sparsity decomposition characteristics and can reasonably divide the signal frequency band, so as to separate the useful fault characteristic frequency band and interference frequency band. At the same time, compared with the kurtosis index, 1.5D spectral kurtosis has better robustness and resolution for low signal-to-noise ratio signals, which can achieve the purpose of fault characteristic band extraction.

Psychometric properties of the short UPPS-P scale in adolescents: Gender, age invariance, and validity among italian youth

Impulsivity is a multifaceted construct associated with poor planning skills, difficulty in maintaining attention, and behavioral disinhibition—especially in conditions of highly activated affective states. Impulsivity plays a prominent role in adolescents, in whom high levels of impulsivity expose them to a greater risk of exhibiting risky behaviors or developing pathological conditions. Currently, one of the most popular frameworks to explain and assess impulsivity is the UPPS-P, which distinguishes between five impulsivity components, and from which the UPPS-P Scale and its short form are derived. In light of the relatively low number of psychometric analyses of the scale in adolescents, this study aimed to verify the psychometric properties of the Short UPPS-P Scale among Italian adolescents. The participants were 647 adolescents (68% males, mean age = 16.9, SD = 1.1 years). Confirmatory factor analysis supported the five-factor model of the scale, which was found to be invariant across gender and age and to have had good internal consistency values. Gender and age differences were also investigated. Evidence for validity was provided by supporting relationships between specific UPPS dimensions, gambling frequency, problem gambling, and problematic use of social networks. Overall, the findings showed that the Short UPPS-P Scale is a promising instrument to assess the multidimensional construct of impulsivity in adolescents in both research and clinical practice.

Predictive model of social media and mobile phone’s problematic use: Impulsivity and social anxiety

The number of social media and mobile phone users is constantly increasing. Researchers worldwide argue that studying the problematic use of these technologies is of the utmost importance to develop better interventions in the future. The study proposes a predictive model of problematic use of social media and mobile phones in relation to social anxiety the components of impulsivity and emotional regulation. El uso problematico de las redes sociales y del telefono movil ha sido argumentado por investigadores detodo del mundo.

An optimized initialization for LDPC decoding over GF(q) in impulsive noise environments.

Modern navigation satellite communication has the characteristic of high transmitting rate. To avoid bit errors in data transmission, low density parity check (LDPC) codes are widely recognized as efficient ways for navigation communication. Conventionally, the LDPC decoding is applied for additive white Gaussian noise (AWGN) channel and degrades severely while facing the impulsive noise. However, navigation communication often suffers from impulsive interference due to the occurrence of high amplitude "spikes". At this time, the conventional Gaussian noise assumption is inadequate. The impulsive component of interference has been found to be significant which influences the reliability of transmitted information. Therefore the LDPC decoding algorithms for AWGN channel are not suitable for impulsive noise environments. Consider that LDPC codes over GF(q) perform better than binary LDPC in resisting burst errors for current navigation system, it is necessary to conduct research on LDPC codes over GF(q). In this paper, an optimized initialization by calculating posterior probabilities of received symbols is proposed for non-binary LDPC decoding on additive white Class A noise (AWAN) channel. To verify the performance of the proposed initialization, extensive experiments are performed in terms of convergence, validity, and robustness. Preliminary results demonstrate that the decoding algorithm with the optimized initialization for non-binary LDPC codes performs better than the competing methods and that of binary LDPC codes on AWAN channel.

FPGA-Based Time-Domain Channel Estimation in Gaussian Mixture Model

The performance of time-domain channel estimation deteriorates due to the presence of Gaussian mixture model (GMM) noise, which results in high mean squared error (MSE) as a challenging issue. The performance of the estimator further decreases when the complexity of the estimator is high due to the high convergence rate. In this paper, an optimized channel estimation method is proposed with low complexity and high accuracy in the GMM environment. In this channel estimation, an improved Gauss-Seidel iterative method is utilized with a minimum number of iterations. The convergence rate of the Gauss-Seidel method is improved by estimating an appropriate initial guess value when no guard bands are used in the orthogonal frequency-division multiplexing (OFDM) symbol. Simulation results provide an acceptable MSE for GMM environments, up to the probability of 5% impulsive noise component. This paper also presents the design and implementation of the proposed estimator in the NEXYS-2 FPGA platform that provides resources allocation, reconfigurability, schematic, and the timing diagram for detailed insight.

Convolutional plug-and-play sparse optimization for impulsive blind deconvolution

Impulsive blind deconvolution (IBD) is a fundamental ill-posed inverse problem in fault diagnosis community. Current IBD methods mainly utilize the intrinsic prior knowledge of impulsive sources to design various regularization terms (e.g., kurtosis, sparsity) to alleviate its ill-posedness. However, the great potentiality of statistical distribution structures embedded in observation data hasn’t been exploited to establish more effective model and algorithm for IBD problem. Leveraging recent plug-and-play (PnP) strategy, a convolutional sparse optimization framework (dubbed COPS) is proposed to address it. Firstly, based on the fact that the absolute envelope of Gaussian noises follows a Rayleigh distribution and sparse impulsive components can be viewed as its outliers, a noise-aware statistical threshold is introduced to design a data-driven sparse penalty. Secondly, from a Bayesian perspective, a mapping relation between residual distribution and model hyper-parameter is unveiled, and then an adaptive parameter penalty is established to dynamically select model hyper-parameters. Lastly, two penalties are plugged into ADMM solver by PnP strategy to guarantee algorithmic convergence. Comprehensive numerical simulations confirm the COPS’s advantages in terms of robustness, convergence, scalability and effectiveness. Diagnostic results of planetary gearbox faults further corroborate the COPS retains better deconvolutional accuracy than the state-of-the-art IBD techniques.

Short-Time/-Angle Spectral Analysis for Vibration Monitoring of Bearing Failures under Variable Speed

Vibration-condition monitoring aims to detect bearing damages of rotating machinery’s incipient failures mainly through time–frequency methods because of their efficient analysis of nonstationary signals. However, by having failures with impulse behavior, short-term events have a tendency to be diluted under variable-speed conditions, while information on frequency changes tends to be lost. Here, we introduce an approach to highlighting bearing impulsive failures by measuring short-term spectral components to deal with variable-speed vibrations. The short-term estimator employs two sliding windows: a small one that measures the instantaneous amplitude level and tracks impulsive components and a large interval that evaluates the average background amplitude. Aiming to characterize cyclo-non-stationary processes with impulsive behavior, the emphasizing high-order-based estimator based on the principle of spectral entropy is introduced. For evaluation, both visual inspection and classifier performance are assessed, contrasting the spectral-entropy estimator with the widely used spectral-kurtosis approach for dealing with impulsive signals. The validation of short-time/-angle spectral analysis performed on three datasets at variable speed showed that the proposed spectral-entropy estimator is a promising indicator for emphasizing bearing failures with impulse behavior.

Relationship between Impulsivity and Meta-Cognition with Cognitive Failures

Abstract Introduction: Inadvertent mistakes are often major problems in daily life. Cognitive failure is the nomenclature attributed to these defects and cognitive failures questionnaire measures the self-reported frequency of these mistakes. It is hypothesized that impulsivity and meta-cognition have underlying associations with cognitive failures. The present study aimed to explore the relationship between impulsivity and meta-cognition with cognitive failures. Method: A randomly selected sample of 125 university students (age range: 18-22) administered the Cognitive Failures Questionnaire (CFQ), UPPS Impulsive Behavior Scale Questionnaire, and Meta-Cognition Questionnaire (MCQ). Data were analyzed using regression. Results: The results indicated that the component of meta-cognition, cognitive confidence had a positive significant relationship with cognitive failures (r=0.51, p<0.01). Furthermore, amongst components of impulsivity, urgency had a significant negative relationship with cognitive failures (r=-0.44, p<0.01). A regression analysis revealed that cognitive confidence and urgency could predict cognitive failure variance. According to Beta coefficients, relative potion of cognitive confidence to predict CF was 51% at first step and 40% in the second. Relative potion of urgency in the second step was 27%. Conclusion: The effect of little urgency and inefficient cognitive confidence is noticeable in distraction, memory problems, blunders and lack of names remembering.

Transient impulses enhancement based on adaptive multi-scale improved differential filter and its application in rotating machines fault diagnosis

Transient impulses caused by local defects are critical for the fault detection of rotating machines. However, they are extremely weak and overwhelmed in the strong noise and harmonic components, making the transient features are very difficult to be extracted. This paper proposes an adaptive multi-scale improved differential filter (AMIDIF) to enhance the identification of transient impulses for rotating machine fault diagnosis. In this scheme, firstly, the AMIDIF is performed to decompose the measured signal of rotating machine into a series of multi-scale improved differential filter (MIDIF) filtered signals. Subsequently, in view of the MIDIF filtered signals exhibit varying extents of validity in revealing fault features, a weighted reconstruction method using correlation analysis is proposed in which the weighted coefficients are counted and distributed to the corresponding MIDIF filtered signals to highlight the effective MIDIF filtered signals and weaken the invalid ones. Finally, the transient impulse components of rotating machinery are obtained by multiplying the weighted coefficients and the MIDIF filtered signals under different scales. Furthermore, the fault types of rotating machines are inferred from the fault defect frequencies in the envelope spectrum of the transient impulses. Simulation analysis and experimental studies are implemented to verify the performance of the AMIDIF compared with the state-of-the-art methods including spectral kurtosis (SK), multi-scale average combination different morphological filter (ACDIF) and multi-scale morphology gradient product operation (MGPO). The results prove that the AMIDIF has excellent performance in extracting transient features for rotating machines fault diagnosis.

Dependency measures for the diagnosis of local faults in application to the heavy-tailed vibration signal

The extraction of the informative frequency band from the signal with heavy-tailed noise is introduced to detect local damage. The algorithm for the vibration signal from a bearing installed in the ore crusher is proposed. From a signal processing point of view, this is related to the detection and recognition of cyclic and non-cyclic impulsive components in the signal. It is assumed that the non-periodic impulses have amplitudes significantly higher than the cyclic impulses – usually fully hidden in the non-Gaussian noise - which makes the diagnosis difficult. As both components are non-Gaussian, a periodicity detector can be used as an alternative to the impulsiveness criteria. In this paper, the proposed informative frequency band selectors utilize the dependency measures, i.e., the Pearson, Spearman and Kendall correlations. These dependency measures are applied to the time–frequency domain representation of the signal to find similarities between sub-signals associated with frequency bands. Three types of frequency bands are expected – the first one – with just Gaussian noise, the second one – with cyclic impulsive behavior, and the last one - with non-cyclic impulsive behavior. For simplicity, it is assumed that frequency bands occupied by cyclic and non-cyclic impulses are different. Finally, one may obtain a map of similarities between all frequency bands, and based on this map the 1D selector for informative frequency band identification can be estimated. The main contribution of the paper is the introduction of the holistic procedure of the fault diagnosis containing the time–frequency representation of vibration signal, including the estimation of the dependency map (D-map) using three different dependency measures, D-map de-noising, and the integration of D-map to one-dimensional (1D) selector. The introduced procedure is applied to the complex simulated data and then to real vibrations from the heavy-duty machine.

Maximum cross-correlated kurtosis-based unsaturated stochastic resonance and its application to bearing fault diagnosis

Considering the random impulses of mechanical noise and the limitations involved while identifying mechanical fault impulse signals via traditional measurement indices of signal-to-noise ratio, which require the characteristic frequency to be known in advance, this study proposes an adaptive unsaturated stochastic resonance method employing maximum cross-correlated kurtosis as the signal detection index. The proposed method combines the features of a cross-correlated coefficient to indicate periodic fault transients and those of spectrum kurtosis to locate these transients in the frequency domain. Actual vibration signals collected from motor and gear bearings subjected to heavy noise are used to demonstrate the effectiveness of the proposed method. Through a coarse tree-based machine learning method, the proposed method is verified to be more suitable for explaining the periodic impulse components of bearing signals, as compared to the ensemble empirical mode decomposition denoising method and unsaturated stochastic resonance using the kurtosis-intercorrelation index.

Enhancing gearbox vibration signals under time-varying operating conditions by combining a whitening procedure and a synchronous processing method

Condition monitoring is very important to avoid unexpected breakdowns of expensive rotating machines found in the power generation and mining industries. The fault information in vibration signals is often buried in low energy frequency bands. In addition to this, time-varying operating conditions and the presence of impulsive components result in the signal to have time-varying spectral content, which impedes the condition inference process. The instantaneous power spectrum is a powerful method that for bearing and gear damage detection under time-varying operating conditions. In this work, we propose a new method of using the instantaneous power spectrum for improved fault diagnosis under time-varying conditions by enhancing the fault information in the vibration signals. It is shown on experimental datasets that the proposed method enhances the fault information in the signal, whereafter it can be used to obtain reliable condition indicators under time-varying operating conditions.

Modulation of control: Can HD-tDCS targeting the dACC reduce impulsivity?

BackgroundThe dorsal anterior cingulate cortex (dACC) and its neurocircuits are central in impulsivity, and maladaptive dACC activity has been implicated in psychological disorders characterized by high trait impulsivity. High-Definition transcranial Direct Current Stimulation (HD-tDCS) is a non-invasive neuromodulation tool that, with certain electrode configurations, can be optimized for targeting deeper subcorticalbrainstructures, such as the dACC. ObjectivesUsing behavioural and electrophysiological measures we investigated whether HD-tDCS targeting the dACC could modulate two key components of impulsivity, inhibitory control and error processing. MethodsTwenty-three healthy adults with high trait impulsivity participated in two experimental sessions. Participants received active or sham HD-tDCS in counterbalanced order with a wash-out period of at least 3 days, as part of a single-blind, cross-over design. EEG was recorded during the Go-NoGo task before, directly after, and 30 min after HD-tDCS. ResultsHD-tDCS targeting the dACC did not affect inhibitory control performance on the Go-NoGo task, but there was evidence for a delayed change in underlying neurophysiological components of motor inhibition (NoGo P3) and error processing (error related negativity; ERN) after one session of HD-tDCS. ConclusionHD-tDCS has potential to modulate underlying neurophysiological components of impulsivity. Future studies should further explore to what degree the dACC was affected and whether multi-session HD-tDCS has the capacity to also induce behavioural changes, particularly in clinical samples characterized by high trait impulsivity.

The Role of Impulsivity and Cognitive Emotion Regulation in the Tendency Toward Addiction in Male Students

Background: Addiction is one of the most important issues today that has affected all institutions, including the family and the society, and the close connection between this issue and the correct methods of raising children in the family is undeniable. Objective: This study aimed to investigate the role of impulsivity and cognitive emotion regulation in the tendency toward addiction in students of Ardabil universities. Methods: The methodology applied in this research was descriptive correlation. A total of 520 male students who study at Ardabil universities were selected by 2-stage random sampling. To measure the variables, the Barratt Impulsivity Scale, the Cognitive Emotion Regulation Questionnaire (CERQ-P), and the Addiction Preparation Scale of Weed and Butcher were used. Results: To analyze the findings, the Pearson correlation coefficient and multiple regressions were used. The obtained results show that there is a positive correlation between impulsivity and tendency to addiction (P&lt;0.01). Also, there is a correlation among all components of impulsivity and tendency to addiction (P&lt;0.01). Statistically, there is a positive correlation between cognitive emotion regulation and tendency to addiction (P&lt;0.01). The results of multivariate regression showed that motor impulsivity, cognitive impulsivity, nonplanning, self-blame, acceptance, rumination, and putting into perspective were significantly able to predict the tendency to addiction. Conclusions: Noting that the variables of impulsivity and cognitive emotion regulation can properly predict the tendency to addiction, university counseling centers can, through workshops, teach the students different ways of controlling these variables in a variety of situations and so they will be able to prevent the tendency toward drug use.

A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning

The localized faults of rolling bearings can be diagnosed by its vibration impulsive signals. However, it is always a challenge to extract the impulsive feature under background noise and non-stationary conditions. This paper investigates impulsive signals detection of a single-point defect rolling bearing and presents a novel data-driven detection approach based on dictionary learning. To overcome the effects harmonic and noise components, we propose an autoregressive-minimum entropy deconvolution model to separate harmonic and deconvolve the effect of the transmission path. To address the shortcomings of conventional sparse representation under the changeable operation environment, we propose an approach that combines K-clustering with singular value decomposition (K-SVD) and split-Bregman to extract impulsive components precisely. Via experiments on synthetic signals and real run-to-failure signals, the excellent performance for different impulsive signals detection verifies the effectiveness and robustness of the proposed approach. Meanwhile, a comparison with the state-of-the-art methods is illustrated, which shows that the proposed approach can provide more accurate detected impulsive signals.

Impulsivity, Lack of Premeditation, and Debts in Online Gambling Disorder.

Background and Objectives: Gambling disorder (GD) is a recurrent and persistent problematic gambling behavior that impairs multiple areas of an individual's life. GD can persist through two modes: online or offline. This study aims to compare sociodemographic, clinical, and psychological characteristics between treatment-seeking online and offline gamblers and analyze the effect of the gambling mode (online or offline) on anxiety, depression, impulsivity, and debts.Methods: Seventy-nine treatment-seeking gamblers (96.2% males), who were simultaneously receiving treatment at a specialized Pathological Gambling and Behavioral Addictions Unit, participated in this study. The sample was divided into two subsamples: online (n = 29, 100% males) and offline (n = 50, 94% males); the characteristics of these two groups were compared and analyzed using Chi-Square test (χ2), t-Test or Mann–Whitney U-test (p < 0.05). Multiple linear regression analyses were performed to determine the effects of gambling mode on significant variables (lack of premeditation and debts).Results: The online sample with a mean age of 29.4 years mainly chose to engage in sports betting (45%, p < 0.05) and showed a higher lack of premeditation levels (25.8 points, p < 0.05) than the offline sample. In addition, the online sample was younger with respect to their onset to gambling (20.2 years, p < 0.05) and the beginning of their gambling problems (25 years, p < 0.05) compared to the offline sample. Online gambling increased the levels of lack of premeditation by an average of 5.43 points compared to offline gambling (p < 0.05). Accumulated debts of the online sample were lower (€11,000) than those of the offline sample (€12,000). However, the interaction between age and gambling mode revealed that online gamblers increased their debt amounts with age at an average increase of €2,726.33 per year compared to offline gamblers (p < 0.05). No significant influence of gambling mode was found on GD severity, anxiety, and depression levels.Conclusions: Gambling mode has a significant relationship with lack of premeditation—a component of impulsivity—and accumulation of debts in treatment-seeking people with GD; however, no relationship was found with the rest of the variables analyzed. Future research with larger samples is needed to confirm these findings.

A test of the pathway model of problematic smartphone use.

Background and aims Problematic smartphone use (PSU) has been described as a growing public health issue. In the current study, we aimed to provide a unique and comprehensive test of the pathway model of PSU. This model posits three distinct developmental pathways leading to PSU: (1) the excessive reassurance pathway, (2) the impulsive pathway and (3) the extraversion pathway. Methods Undergraduate students ( n = 795, 69.8% female, mean age = 23.80 years, sd = 3.02) completed online self-report measures of PSU (addictive use, antisocial use and dangerous use) and the psychological features (personality traits and psychopathological symptoms) underlying the three pathways. ResultsBayesian analyses revealed that addictive use is mainly driven by the excessive reassurance pathway and the impulsive pathway, for which candidate etiopathological factors include heightened negative urgency, a hyperactive behavioural inhibition system and symptoms of social anxiety. Dangerous and antisocial use are mainly driven by the impulsive pathway and the extraversion pathway, for which candidate etiopathological factors include specific impulsivity components (lack of premeditation and sensation seeking) and primary psychopathy (inclination to lie, lack of remorse, callousness and manipulativeness).Discussion and conclusionsThe present study constitutes the first comprehensive test of the pathway model of PSU. We provide robust and original results regarding the psychological dimensions associated with each of the postulated pathways of PSU, which should be taken into account when considering regulation of smartphone use or tailoring prevention protocols to reduce problematic usage patterns.

Time-delay systems with delayed impulses: A unified criterion on asymptotic stability

The paper deals with the global asymptotic stability of general nonlinear time-delay systems with delay-dependent impulses through the Lyapunov–Krasovskii method. We derive a unified stability criterion which can be applied to a variety of impulsive systems. The cases when each of the continuous dynamics and the impulsive component is either stabilizing or destabilizing are investigated. Both theoretically and numerically, we demonstrate that the obtained result is more general than those existing in the literature.

An Adaptive Weighted Adjacent Difference Sparse Representation for Bearing Fault Diagnosis

Vibration monitoring is the effective and convenient method for machine fault diagnosis. However, the fault feature extraction (i.e., impulse components) is difficult due to strong noise. In this article, a novel adaptive weighted adjacent difference sparse representation (AWAD-SR) is proposed for bearing fault diagnosis, which promotes the sparsity while reducing noise interference. First, the adjacent difference sparse regularization term is proposed for impulsive feature extraction from vibration signal. Second, an adaptive weighting method is proposed for solving the challenging problem on setup of regularization parameter that affects the feature extraction performance of those sparse representation (SR) methods. AWAD-SR setups adaptively the regularization parameter of each sampling point in a vibration signal according to their amplitude, which improves not only the feature extraction performance but also the applicability in practical engineering application cases. Finally, an effective majorization-minimization (MM)-based optimization algorithm is proposed for solving the sparse optimization problem of the weighted SR, which has the advantages of fast convergence speed. The effectiveness and superiority of AWAD-SR are validated on the simulation signal and two bearing vibration signals. The comparison results show that feature extraction of AWAD-SR from vibration signals is superior to other state of-the-art approach.

Some features of the acceleration impulse response function

Impulse response functions (IRFs) and frequency response functions (FRFs) are fundamental quantities that describe the dynamic behaviour of a linear vibrating system in the time and frequency domains respectively. The acceleration IRF is of particular concern in this paper, because unlike the displacement and velocity IRFs it contains a Delta function as well as a decaying oscillation. The origin of this Delta function is shown to be due to the causality constraint rather than the system. To illustrate the characteristics of the IRFs and FRFs, simulations are presented for a single-degree-of-freedom system, and are supported by some laboratory experimental work. The acceleration IRF is partitioned into the impulse component (Delta function for the simulations) and the oscillatory component. They are separately transformed to the frequency domain to illustrate their effects in the accelerance FRFs for both simulated and measured data.