Impulsive Components Research Articles

A non-convex denoising model for impulse and Gaussian noise mixture removing using bi-level parameter identification

<p style='text-indent:20px;'>We propose a new variational framework to remove a mixture of Gaussian and impulse noise from images. This framework is based on a non-convex PDE-constrained with a fractional-order operator. The non-convex norm is applied to the impulse component controlled by a weighted parameter <inline-formula><tex-math id="M1">\begin{document}$ \gamma $\end{document}</tex-math></inline-formula>, which depends on the level of the impulse noise and image feature. Furthermore, the fractional operator is used to preserve image texture and edges. In a first part, we study the theoretical properties of the proposed PDE-constrained, and we show some well-posdnees results. In a second part, after having demonstrated how to numerically find a minimizer, a proximal linearized algorithm combined with a Primal-Dual approach is introduced. Moreover, a bi-level optimization framework with a projected gradient algorithm is proposed in order to automatically select the parameter <inline-formula><tex-math id="M2">\begin{document}$ \gamma $\end{document}</tex-math></inline-formula>. Denoising tests confirm that the non-convex term and learned parameter <inline-formula><tex-math id="M3">\begin{document}$ \gamma $\end{document}</tex-math></inline-formula> lead in general to an improved reconstruction when compared to results of convex norm and other competitive denoising methods. Finally, we show extensive denoising experiments on various images and noise intensities and we report conventional numerical results which confirm the validity of the non-convex PDE-constrained, its analysis and also the proposed bi-level optimization with learning data.</p>

Fast Spectral Correlation Detector for Periodic Impulse Extraction of Rotating Machinery

Periodic impulse components are a typical symptom of rotating machinery failure, which are often masked by heavy background noise. It is of great practical significance to research how to obtain periodic impulse components to achieve fault diagnosis of rotating machinery. Fast spectrum correlation (Fast-SC), as a typical non-stationary and nonlinear signal processing method, has been studied in feature extraction by suppressing background noise to enhance periodic impulse components. However, effectively determining the bandwidth of Fast-SC is still a challenging issue. To address this issue, a novel method called Fast-SC detector is developed, which decomposes the measured signal of rotating machinery into multiple Fast-SC slices with different frequency bands. The whale optimization algorithm (WOA) is utilized to optimize the number of Fast-SC slices with minimum mean entropy as the optimal Fast-SC bandwidth. In addition, an adaptive combination morphological filter (ACMF) is applied to suppress the residual noise and narrowband impulses in the WOA-based Fast-SC slices to enhance the fault features. Finally, the Fast-SC detector is constructed using the average denoising Fast-SC slices to infer the type of rotating machinery. The proposed Fast-SC detector is compared with existing methods by applying simulation signal model and experimental data. The results prove that Fast-SC detector is superior to some excellent periodic impulse extraction algorithms in extracting the symptoms of rotating machinery failure.

A Meta-Analysis of the Distinct Effects of Neurofeedback (Particularly TBR) on Inattention and Hyperactivity

This meta-analytical study examined the effect of neurofeedback on reducing both the (a) inattentive and (b) hyperactive / impulsive components of children diagnosed with Attention- eficit / Hyperactivity Disorder (ADHD). The main finding of the meta-analysis was that theta-beta ratio training achieved identical, statistically significant, and practically large effects on both inattention (g = 0.92) and hyperactivity / impulsivity (g = 0.77). An analysis of the literature indicates that the effectiveness of theta-beta ratio-based neurofeedback appears to derive from a general, top-down effect applicable in the frontal regions of the brain, particularly areas and structures related to information processing. One recommendation emerging from the meta-analysis is for neurofeedback practitioners and researchers to better document details related to site placement and bandwidths in order to identify better protocols based on theta-beta ratios. In addition, the importance of alpha generation was highlighted as a means of going beyond static theta-beta ratios in both the diagnosis of ADHD and the measurement of neurofeedback protocol success over time.

Weak Fault Feature Extraction of Rolling Bearing Based on SVMD and Improved MOMEDA

In order to solve the problem that it is very difficult to extract fault features directly from the weak impact component of early fault signal of rolling bearing, a method combining continuous variational mode decomposition (SVMD) with modified MOMEDA based on Teager energy operator is proposed. Firstly, the low resonance impulse component in the fault signal is separated from the harmonic component and noise by SVMD, and then the Teager energy operator is used to enhance the impulse feature in the low resonance component to ensure that the accurate fault period is selected by the MOMOEDA algorithm. After further noise reduction by MOMEDA, the envelope spectrum of the signal is analyzed, and finally the fault location is determined. The results of simulation and experimental data show that this method can accurately and effectively extract the characteristic frequency of rolling bearing weak fault.

Symplectic Ramanujan Mode Decomposition and its application to compound fault diagnosis of bearings

The existing compound-fault diagnosis methods of rolling bearings have their own defects, which makes their accuracy of fault diagnosis impossible to be guaranteed. Therefore, this paper attempts to combine symplectic similarity transformation with Ramanujan subspace theory, and then a periodic impulse extraction method called symplectic Ramanujan mode decomposition (SRMD) method is proposed. SRMD separates the components with different fault features through symplectic similarity transformation and hierarchical clustering method to obtain symplectic clustering components (SCCs). At the same time, SRMD uses the Ramanujan subspace theory to extract the major periodic impulse components of each component to be extracted, and then obtains symplectic Ramanujan components (SRCs). The results show that SRMD is a resultful method in compound-fault diagnosis of bearings with excellent periodic impulse extraction ability.

Fault feature extraction of rolling bearings using local mean decomposition-based enhanced sparse coding shrinkage

Rolling bearing are one of the most important rotating components in the rotating machine and they are prone to failure due to complex harsh working conditions. However, it’s a difficult issue to extract the fault feature from heavy noisy signal. In this paper, a new multilayer hybrid denoising method called local mean decomposition (LMD)-based enhanced sparse coding shrinkage (SCS) (named LMD-EnSCS) is proposed for impulse component extraction from bearing vibration signals. First, LMD is used to decompose the signal into a set of product functions (PFs) to enable impulse component as prominent as possible. Moreover, the multiple criteria is proposed to select the effective PF components. The rest filtering layer is used to enhance and extract the impulsive feature through minimum entropy deconvolution (MED) and sparse coding shrinkage (SCS) respectively. MED is able to enhance the impulsive component of noisy signal, which improve the impulsive extraction ability of SCS effectively. The simulation signal and bearing signal are used to verify the effectiveness and superiority of LMD-EnSCS, and the results show that LMD-EnSCS can extract the fault feature and perform well for bearing fault diagnosis.

The network structure of impulsive personality and temporal discounting

Impulsivity is an ill-defined umbrella term. Therefore, in addition to factor-analytic models, network models that characterize the relative positions of their components can offer novel insights. Here, we investigated the structure of impulsivity in 1000 participants using the Short UPPS-P Impulsive Behavior Scale and a temporal discounting task. Our results revealed an acceptable fit of a five-factor solution of the Short UPPS-P but question the separability of Positive and Negative Urgency. Additionally, Lack of Premeditation showed high closeness centrality and was – like Positive and Negative Urgency – significantly yet weakly related to temporal discounting. It remains to be investigated further to what extent Lack of Premeditation is a common cause or consequence of other impulsivity components.

THE ROLE OF IMPULSIVITY IN PREDICTING THE SUBJECTIVE WELL-BEING OF STUDENTS IN PRIVATE HIGHER EDUCATION

<p>The primary purpose of this study was to investigate associations between attention impulsivity, motor impulsivity and non-planning impulsivity measured according to the Barratt Impulsiveness Scale-11 (BIS) and indicators of subjective well-being (SWB) measured by the Flourishing Scale (FS) and Personal Wellbeing Index (PWI) in students at selected private higher education institutions (N = 514, 52% women, 48% men). The aim of the current study was to explore the impact of gender on the aforementioned associations. Relationships between impulsivity and subjective well-being were examined taking into account the multifactoral structure of impulsiveness. The main findings of the study show that: (a) attention impulsivity predicted low prosperity and low levels of satisfaction with standard of living, health, personal achievements, safety and future security; (b) motor impulsivity showed bivariate but not unique relationships between prosperity and satisfaction with personal health, achievements and personal safety; (c) non-planning impulsivity was found to be uniquely associated with lower subjective prosperity and lower satisfaction with personal achievements and personal relationships; and (d) gender did not moderate the relationship between BIS components and SWB indexes. Impulsivity substrates explained between 4 and 17% of the variance in subjective well-being indexes. In sum, the results showed that the three components of impulsivity are distinct yet partially overlapping. </p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0998/a.php" alt="Hit counter" /></p>

Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults

Aiming at the difficulty of extracting and classifying early bearing faults, a fault diagnosis method based on weighted average time-varying filtering empirical mode decomposition and improved eigenclass is proposed in this paper. Firstly, the bearing fault signal is decomposed into a series of intrinsic mode functions by the signal decomposition method, and the amplitude of the component is modulated by the weighted average method to enhance the fault impulse component. Then, the fractional Fourier transform is used to filter the reconstructed signal. Regarding classification issues, the eigenclass classifier is optimized by the IDE method that can be used for feature dimensionality reduction. Finally, the optimal features are selected and input into the IDE-EigenClass model. The experimental results show that the bearing fault diagnosis method proposed in this paper has higher accuracy and stability than the traditional PNN, SVM, BP, and other methods.

Parametric simulator of cyclic and non-cyclic impulsive vibration signals for diagnostic research applications

In recent years cyclostationary analysis of vibration signals is considered to be one of the most potent approaches for diagnostics of machines with rotating components. However, it is a subject of an extensive research towards extending its robustness due to its significant inefficiency in the presence of non-cyclic impulsive components in measured data. This problem is especially visible in datasets measured on machines such as ore crushers, where the high-energy impacts are a natural phenomenon. Unfortunately, due to practical inaccessibility, real-life datasets necessary to properly study this problem are extremely difficult to obtain. To address this issue, the authors propose an easy to use simulator of impulsive components. It covers both cyclic components that can describe various types of fault signatures, and non-cyclic ones that can represent impacts occurring naturally due to the nature of machine operation. Simulated signals have been compared with real ones to ensure a high similarity degree, which in turn guarantees a relatively high level of realism.

Fault-related impulsive component detection for vibration-based diagnostics in the presence of random impulsive noise

In this paper, the authors propose the methodology for vibration-based diagnostics towards local damage detection in rolling element bearings in the presence of non-Gaussian noise. In real-life cases, the main problem making the analysis difficult is the non-Gaussianity of the high-energy noise present in the operational environment. Because of this fact, popular impulsiveness-related detection techniques cannot be used. In the presented article, a real-life data measured in an industrial scenario will be presented and a proposition of an approach to cyclic component extraction will be discussed. The proposed approach takes advantage of the Cyclic Spectral Coherence (CSC) map as multidimensional data representation. It can be very useful for indicating cyclic modulated components in the otherwise non-cyclic signal content. However, due to the limitations of statistics used in CSC map calculation impacting the quality of CSC map in the presence of non-cyclic impulsive behavior in the signal, Nonnegative Matrix Factorization idea is used as a method for component separation. The presented method allows for obtaining carrier-related and modulation-related features of the component of interest. The main advantage of the presented method is pairing very useful multidimensional data representation (CSC) with very potent decomposition technique (NMF), that allows to reconstruct separated components in the final stage of the analysis. As a consequence, the time series of the component can be reconstructed based on the carrier feature.

An [formula omitted]-norm based color image deblurring model under mixed random-valued impulse and Gaussian noise

This article introduces a novel model that restores color images degraded by blurring and mixed random-valued (RV) impulse and Gaussian noise. The model includes a data-fidelity term that consists of RV impulse noise and Gaussian noise components, and total variation regularization. Specifically, the data-fidelity term uses the l0- and l2-norms to represent the RV impulse and Gaussian noise components respectively. This results in an appropriate separation of these two noise components and thus enables simultaneous elimination of the mixed noise. Moreover, total variation regularization allows sufficient denoising in homogeneous regions while maintaining edges. To solve the nonconvex and nonsmooth model, we adopt a proximal alternating minimization approach and the alternating direction method of multipliers. These techniques contribute to an iterative algorithm with a convergence analysis provided. The experimental results demonstrate the effectiveness of the proposed model compared to other existing models, as measured by visual inspection and image quality assessments.

Application of Wavelet Filtering to Vibrational Signals from the Mining Screen for Spring Condition Monitoring

The main task of mineral processing plants is to further process the raw material extracted in the mining faces into a concentrate with the highest possible concentration of the final product. In practice, it is a complex process in which several stages can be distinguished. After the ore has been transported to the surface by the skip shaft, one of the first steps is sieving the ore, which is typically performed using vibrating mining screens. In a typical Ore Enrichment Plant, the screening process is carried out by several such machines. This is a typical bottleneck in the technological chain. For this reason, the main challenge for users is to achieve the highest reliability and efficiency of these technical facilities. The solution is to focus on predictive maintenance strategies based on the development of monitoring and advanced diagnostic procedures capable of estimating the time of safe operation. This work was developed as part of an advanced diagnostic system ensuring comprehensive technical conditioning and early fault detection of components such as the engine, transmission, bearings, springs, and screen. This article focuses on vibration data. The problem of damage detection in the presence of periodically impulsive components resulting from falling feed material on the screen and its further screening process has been considered. These disturbances are of a non-Gaussian noise nature, the elimination of which is essential to extract the fault-related signal of interest. One solution may be to properly smooth and filter the raw signal. In this article, a wavelet filtering technique is applied. First, the wavelet filtering procedure is described. In the next step, the performance of a wavelet filter is investigated depending on its parameters. Then, the results of wavelet filtering are compared with such methods as low-pass filtering and smoothing using a moving average. Finally, the impact of wavelet filtering on the calculation of screen trajectories is investigated.

The extraction of informative vibration components of bearings by different adaptive methods

It was suggested to select the best adaptive method after proper comparative researches, for the extraction of informative vibration components of bearings. The description and drawbacks of empirical mode decomposition method were presented, and the properties of improved ensemble empirical mode decomposition method and complete ensemble empirical mode decomposition with adaptive noise method were highlighted. A simulated additive signal contained impulse, modulation components and two sinusoids. The extracted intrinsic mode functions were the decomposition results of the first two adaptive methods, which failed to separate impulse and modulation components. Meanwhile, the intrinsic mode functions of the third adaptive method had separately impulse and modulation components, and the method proved to be effective in the separation of the vibration components during the vibrodiagnostics of bearings and gearboxes of the industrial equipment.

Applying Kurtosis as an Indirect Metric of Noise Temporal Structure in the Assessment of Hearing Loss Associated With Occupational Complex Noise Exposure.

Objective:The association of occupational noise-induced hearing loss (NIHL) with noise energy was well documented, but the relationship between occupational noise and noise temporal structure is rarely reported. The objective of this study was to investigate the principal characteristics of the relationship between occupational NIHL and the temporal structure of noise.Methods:Audiometric and shift-long noise exposure data were collected from 3102 Chinese manufacturing workers from six typical industries through a cross-sectional survey. In data analysis, A-weighted 8-h equivalent SPL (LAeq.8h), peak SPL, and cumulative noise exposure (CNE) were used as noise energy indicators, while kurtosis (β) was used as the indicator of noise temporal structure. Two NIHL were defined: (1) high-frequency noise-induced hearing loss (HFNIHL) and (2) noise-induced permanent threshold shift at test frequencies of 3, 4, and 6 kHz (noise-induced permanent threshold shift [NIPTS346]). The noise characteristics of different types of work and the relationship between these characteristics and the prevalence of NIHL were analyzed.Results:The noise waveform shape, with a specific noise kurtosis, was unique to each type of work. Approximately 27.92% of manufacturing workers suffered from HFNIHL, with a mean NIPTS346 of 24.16 ± 14.13 dB HL. The Spearman correlation analysis showed that the kurtosis value was significantly correlated with the difference of peak SPL minus its LAeq.8h across different types of work (p < 0.01). For a kurtosis-adjusted CNE, the linear regression equation between HFNIHL% and CNE for complex noise almost overlapped with Gaussian noise. Binary logistic regression analysis showed that LAeq.8h, kurtosis, and exposure duration were the key factors influencing HFNIHL% (p < 0.01). The notching extent in NIPTS at 4 kHz became deeper with the increase in LAeq.8h and kurtosis. HFNIHL% increased most rapidly during the first 10 years of exposure. HFNIHL% with β ≥ 10 was significantly higher than that with β < 10 (p < 0.05), and it increased with increasing kurtosis across different CNE or LAeq.8h levels. When LAeq.8h was 80 to 85 dB(A), the HFNIHL% at β ≥ 100 was significantly higher than that at 10 ≤ β < 100 or β < 10 (p < 0.05 and p < 0.01, respectively).Conclusions:In the evaluation of hearing loss caused by complex noise, not only noise energy but also the temporal structure of noise must be considered. Kurtosis of noise is an indirect metric that is sensitive to the presence of impulsive components in complex noise exposure, and thus, it could be useful for quantifying the risk for NIHL. It is necessary to re-evaluate the safety of permissible exposure limit of 85 dB(A) as noise with a high kurtosis value can aggravate or accelerate early NIHL.

Investigation on enhanced mathematical morphological operators for bearing fault feature extraction

Morphological filtering has been extensively applied to rotating machinery diagnostics, whereas traditional morphological operators cannot effectively extract fault-triggered transient impulse components from noisy mechanical vibration signal. In this paper, a framework of generalized compound morphological operator (GCMO) is presented to enhance the extraction ability of impulsive fault features. Further, several new compound morphological operators are developed for transient impulse extraction by introducing the product, convolution, and cross-correlation operations into the GCMO framework. In addition, a novel strategy for selecting the structural element length is proposed to optimize the repetitive impulse feature extraction of the compound morphological operators. The fault feature extraction performance of the developed compound morphological operators is investigated and validated on the simulation signals and measured railway bearing vibration signals, and compared with the combined morphological operators and five existing feature extraction methods. The results demonstrate that the morphological cross-correlation operators are more efficient in repetitive fault impulse feature extraction and bearing fault diagnosis than the combined morphological operators and the comparison methods.

Enhanced periodic mode decomposition and its application to composite fault diagnosis of rolling bearings

The impulse components of different periods in the composite fault signal of rolling bearing are extracted difficultly due to the background noise and the coupling of composite faults, which greatly affects the accuracy of composite fault diagnosis. To accurately extract the periodic impulse components from the composite fault signals, we introduce the theory of Ramanujan sum to generate the precise periodic components (PPCs). In order to comprehensively extract major periods in composite fault signals, the SOSO-maximum autocorrelation impulse harmonic to noise deconvolution (SOSO-MAIHND) method is proposed to reduce noise and enhance the relatively weak periodic impulses. Based on this, an enhanced periodic mode decomposition (EPMD) method is proposed. The experimental results indicate that the EPMD is an effective method for composite fault diagnosis of rolling bearings.

Explosive Outbursts at School.

Explosive outbursts (EO) by students are an intensely distressing experience for that student as well as for all school staff and students present during the outburst. These EO are characterized by rapid escalations, usually far out of proportion to precipitating events, may include significant verbal and/or physical aggression, require intensive staff intervention, are often difficult for the student to process, and are typically recurrent. These explosions cross multiple psychiatric and educational diagnostic categories and require diverse interventions to address behavioral, emotional, impulsive, and sensory components. Interventions for each stage of an EO can be used to deescalate these events.

A Bayesian inverse dynamic approach for impulsive wave loading reconstruction: Theory, laboratory and field application

The measurement of wave forces acting on marine structures is a complicated task, both during physical experiments and, even more so, in the field. Force transducers adopted in laboratory experiments require a minimum level of structural movement, thus violating the main assumption of fully rigid structure and introducing a dynamic response of the system. Sometimes the induced vibrations are so intense that they completely nullify the reliability of the experiments. On-site, it is even more complex, since there are no force transducers of the size and capacity able to measure such massive force intensity acting over the very large domain of a marine structure. To this end, this investigation proposes a Bayesian methodology aimed to remove the undesired effects from the directly (laboratory applications) or indirectly (field applications) measured wave forces. The paper presents three applications of the method: i) a theoretical application on a synthetic signal for which MATLAB® procedures are provided, ii) an experimental application on laboratory data collected during experiments aimed to model broken wave loading on a cylinder upon a shoal and iii) a field application designed to reconstruct the wave force that generated recorded vibrations on the Wolf Rock lighthouse during Hurricane Ophelia. The proposed methodology allows the inclusion of existing information on breaking and broken wave forces through the process-based informative prior distributions, while it also provides the formal framework for uncertainty quantification of the results through the posterior distribution.Notable findings are that the broken wave loading shows similar features for both laboratory and field data. The load time series is characterised by an initial impulsive component constituted by two peaks and followed by a delayed smoother one. The first two peaks are due to the initial impact of the aerated front and to the sudden deceleration of the falling water mass previously upward accelerated by the initial impact. The third, less intense peak, is due to the interaction between the cylinder and remaining water mass carried by the individual wave.Finally, the method allows to properly identify the length of the impulsive loading component. The implications of this length on the use of the impulse theory for the assessment or design of marine structures are discussed.

Minimum entropy morphological deconvolution and its application in bearing fault diagnosis

The bearing fault signal can be seen as convolution of periodical impulses and interference components. The minimum entropy deconvolution (MED) is effective approach for the deconvolution of signal. However, the MED is vulnerable to random impulse and interference components. To solve the problem, an improved method, named minimum entropy morphological deconvolution (MEMD), is proposed in this paper. Firstly, the amplitude frequency response of two typical morphological operators (MOs) are discussed. These operators are then introduced into MED to filter the sample matrix. The optimal MO is selected based on the amplitude ratio of diagonal slice spectrum (DSS). Eventually, the filtered result is analyzed by DSS to identify the fault type. In MEMD, the influence of random shocks is eliminated and the scale of SE can be determined adaptively. The MEMD is verified by simulation and experimental signals. Comparison study is implemented and the analysis results verify its effectiveness and feasibility.