Transient Signal Detection on the Basis of Energy and Zero Crossing Detectors

Waghmare, Rahul G.; Nalbalwar, Sanjay L.; Das, Arnab

doi:10.1016/j.proeng.2012.01.843

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…In this study, the zero-crossing measurement method ( Waghmare et al, 2012 ; Zhu et al, 2017 ) was used to determine the response signals' starting point. And the first 128 sampling points were selected as the interested signals for future processing.…”

Section: Resultsmentioning

confidence: 99%

Eggshell crack detection based on the transient impact analysis and cross-correlation method

Sun

Zhang

Feng

et al. 2021

Current Research in Food Science

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To explore the transient impact process for cracked eggshell detection, an equivalent mechanical model was built based on a self-designed automatic excitation device. Through analysis of power spectrum from dynamic force signal, it was found that the impact speed affects only the impaction energy. In contrast, the material of the impact head and the weight of the excitation rod determine the energy and the cut-off frequency of the impaction. When the weight of impact tup is less than 6.62 g, the cut-off frequency of excitation impact can cover the egg's inherent frequency. Then, an optimized experiment system was designed to acquire the response acoustic signals. The cross-correlation analysis and Bayes classification methods were carried out to detect the cracked eggshell. In the conducted experiments, a crack detection level of 97% and a false rejection level of 1% were achieved. From the findings, it can be concluded that the proposed method will assist in optimizing the impact device and simplifying the classification algorithm for an online detection system.

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Section: Resultsmentioning

confidence: 99%

Eggshell crack detection based on the transient impact analysis and cross-correlation method

Sun

Zhang

Feng

et al. 2021

Current Research in Food Science

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“…The optimal choice of N depends on the time interval over which signals are taken, background noise, and other environmental factors. Typical values range from N = 100 to N = 40960, see [8,21] for details.) Under these assumptions, we take the logarithm in (20) and rearrange terms to arrive at a scaled threshold γ.…”

Section: P(x)mentioning

confidence: 99%

Trajectory Optimization of Unmanned Aerial Vehicles in theElectromagnetic Environment

Atayev,

Fliege,

Zemkoho

2023

Preprint

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We consider a type of routing problems common in defence and security, in which we control a fleetof unmanned aerial vehicles (UAVs) that have to reach one or more target locations without beingdetected by an adversary. Detection can be carried out by a variety of sensors (radio receivers, cameras,personnel, etc) placed by the adversary around the target sites. We model the act of detecting a UAVfrom first principles by noting that sensors work by monitoring frequencies in the electromagneticspectrum for signals or noise emitted. By this, we are able to provide a flexible and versatile nonlinearoptimisation framework in which the problem is modeled as a novel trajectory optimisation problemwith paths of the UAVs as continuous arcs in an Euclidean space. The flexibility of our approach isexhibited by the fact that we can easily consider various relevant objectives, among them minimisingthe overall probability of detection and maximising the location error that the adversary experienceswhen trying to locate our UAVs. Our model is also versatile enough to consider the act of jamming, inwhich one or more of our UAVs intentionally send out signals to interfere with the operations of theadversary’s sensors. Numerical results show the flexibility of our framework, and that we can solverealistic instances of this problem type.

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“…e measures widely used to represent the characteristics of ZC include the mean ZCR, density of the time interval between continuous ZCs, and excess threshold measure. According to the experience of literature [33], compared with other ZC measurements, the ZC feature of excess threshold measurement is more conducive to fault diagnosis at relatively high SNR.…”

Section: Zc Characteristic Feature Algorithmmentioning

confidence: 99%

“…Gonzalez and Kinsner showed that ZC could be used to identify different parts of the composite signal, and the advantage of the ZC computing immediately in the time domain is attractive to real time implementation [32]. Waghmare et al presented a methodology based on piecewise energy and corresponding mean of signal ZC in environmental noise, which has a broad application prospect in dealing with the underwater target-radiated noise [33]. For the first time, Nayana and Geethanjali used time domain parameters, waveform parameters, slope sign changes (SSC), simple sign integral, and Wilson amplitude to established mean absolute value and ZC to identify failures of motors, and by comparing with conventional features, they proved the proposed features perform better [34].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization for Fault Diagnosis of Electromechanical Systems Based on Zero Crossing Algorithm

Chen

Liu

2020

Mathematical Problems in Engineering

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The demand of system security and reliability in the modern industrial process is ever-increasing, and fault diagnosis technology has always been a crucial research direction in the control field. Due to the complexity, nonlinearity, and coupling of multitudinous control systems, precise system modeling for fault diagnosis is attracting more attention. In this paper, we propose an improved method of electromechanical systems fault diagnosis based on zero-crossing (ZC) algorithm, which can present the calculation model of zero-crossing rate (ZCR) and optimize the parameters of ZC algorithm by establishing a criterion function model to improve the diagnosis accuracy and robustness of ZC characteristic model. The simulation validates the influence of different signal-to-noise ratio (SNR) on ZC feature recognition ability and indicates that the within-between distance model is effective to enhance the diagnose accuracy of ZC feature. Finally, the method is applied to the diagnosis of motor fault bearing, which confirms the necessity and effectiveness of the model improvement and parameter optimization and verifies the robustness to the load.

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Transient Signal Detection on the Basis of Energy and Zero Crossing Detectors

Cited by 12 publications

References 4 publications

Eggshell crack detection based on the transient impact analysis and cross-correlation method

Eggshell crack detection based on the transient impact analysis and cross-correlation method

Trajectory Optimization of Unmanned Aerial Vehicles in theElectromagnetic Environment

Modeling and Optimization for Fault Diagnosis of Electromechanical Systems Based on Zero Crossing Algorithm

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