scholarly journals Wire Rope Weak Defect Signal Processing Methods Based on Improved SVD and Phase Space Reconstruction

2021 ◽  
Vol 50 (4) ◽  
pp. 752-768
Author(s):  
Muchao Chen ◽  
Yanxiang He
Keyword(s):  
Signal Processing ◽  
Phase Space ◽  
Defect Detection ◽  
Wire Rope ◽  
Phase Space Reconstruction ◽  
Dynamic Feature ◽  
Nonlinear Simulation ◽  
Processing Methods ◽  
Time Frequency ◽  
Signal Processing Methods

Due to the complexity of the interference operation environment of wire rope, the detection signals are usually weak and coupled in time-frequency domain, which makes the defect difficult to recognize, while the signal characterizations in phase space are also needed to be studied. Combining the nonlinear dynamic feature identification theories, phase space characteristics and chaotic features of wire rope defect detection signals are mainly investigated in this paper. First, principles of phase space reconstruction method for wire rope detection signals are presented by the chaotic dynamic indexes calculation of embedded dimension and delay time. Second, the change trends of the correlation dimension, approximate entropy and Lyapunov index of different phase space reconstructed wire rope defect detection signals are studied through the nonlinear simulation and analysis. Finally, a phase space reconstruction algorithm based on improved SVD is proposed, and the new algorithm is also compared with traditional signal processing methods. These results obtained by 6 groups of experiments were also evaluated and compared by the parameters of signal-to-noise ratio (SNR) and phase space trajectory chart, which manifests that the improved algorithm not only can increase the weak detection signal SNR to about 2.3dB of wire rope effectively, but also demonstrate the feasibility of the proposed methods in application.

Application of Advanced Signal Processing Methods to Automotive Systems Testing

2004 ◽  
Author(s):  
Steve M. Rohde ◽  
William J. Williams ◽  
Mitchell M. Rohde
Keyword(s):  
Signal Processing ◽  
Consumer Preferences ◽  
Processing Methods ◽  
Time Frequency ◽  
Vehicle Systems ◽  
Pedal Feel ◽  
Mathematical Analyses ◽  
Noise Vibration ◽  
Virtual Product Development ◽  
Signal Processing Methods

During the past twenty years there have been rapid developments in the creation and application of mathematical computer-based capabilities and tools (e.g., FEA) to simulate and synthesize vehicle systems. This has led to the concept of virtual product development. In parallel with the development of these tools, an equally sophisticated set of tools have been developed in the area of advanced signal processing. These tools, based upon mathematical and statistical modeling techniques, enable the extraction of useful information from data and have application throughout the entire vehicle creation process. Moreover, signal processing bridges the gap between the “virtual” and the “real” worlds — an extremely important concept that is changing the entire nature of what is thought of as “testing.” This paper discusses the use of advanced signal processing methods in vehicle creation with particular emphasis on its use in vehicle systems testing. Modern Time Frequency Analysis (TFA), a technique that was specifically designed to study transient signals and was in part pioneered by one of the authors (WJW), is highlighted. TFA expresses a signal jointly in time and frequency at very high resolution and as such can often provide profound insights. Applications of TFA to vehicle systems testing are presented related to Noise, Vibration, and Harshness (NVH) that enable sound quality analyses. For example, using TFA predictive models of consumer preferences for transient sounds that are useful to the automotive engineer in testing and modifying new vehicle subsystem designs are discussed. Other applications that are discussed deal with brake pedal feel, and characterizing vehicle crash signals. In the latter case TFA has resulted in some unique insights that were not provided by conventional statistical and mathematical analyses.

Comparative characteristics of anti-collision processing of radio signal from identification tags on surface acoustic waves

2019 ◽  
pp. 48-56 ◽  
Author(s):  
A. V. Sorokin ◽  
A. P. Shepeta ◽  
V. A. Nenashev ◽  
G. M. Wattimena
Keyword(s):  
Signal Processing ◽  
Surface Acoustic Waves ◽  
Group Identification ◽  
Sensor Data ◽  
Correct Identification ◽  
Processing Methods ◽  
Time Frequency ◽  
Advantages And Disadvantages ◽  
Identification Tags ◽  
Signal Processing Methods

Introduction:Collision of information signals is a common problem in the measurement of physical magnitudes, such as temperature, pressure, stress, etc., with acoustic-electronic sensors. This problem is caused by overlapping response signals in the time domain, which makes it difficult to interpret correctly the device identification codes or the sensor data received.Purpose:Analysis of anticollision algorithms for radio-frequency tag code detection and identification by response information signals from acoustic-electronic devices which use the methods of time, frequency and frequency-time division of the response radio signals.Methods:Probabilistic methods for calculating the parameters of digital detectors of radio pulse bursts with given false alarm values and gaussian white noise background; individual code group identification methods when studying the attenuation of acoustic-electric signal during their propagation in the tag substrate, taking into account the dependence of the attenuation on the tag topology.Results:We have derived analytical expressions to calculate the probability of the correct identification of each tag, taking into account the dependence on tag topology, attenuation characteristics, the anti-collision signal processing methods and the signal-to-noise ratios. Curves which allow you to compare the advantages and disadvantages of the considered anti-collision signal processing methods are calculated and shown in the article. The analysis of the graphic charts demonstrating the correct identification probability has shown that identification tags with frequency-time coding have better ratios as compared to frequency or time methods of collision prevention.Practical relevance:The obtained result allows you to effectively evaluate the condition of technical objects, improving the predictability and prevention of possible environmental and man-made disasters.

scholarly journals Evaluating Steady-State Visually Evoked Potentials-Based Brain-Computer Interface System Using Wavelet Features and Various Machine Learning Methods

2021 ◽  
Author(s):  
Ebru Sayilgan ◽  
Yilmaz Kemal Yuce ◽  
Yalcin Isler
Keyword(s):  
Signal Processing ◽  
Steady State ◽  
Evoked Potentials ◽  
Wavelet Function ◽  
Processing Methods ◽  
Training Time ◽  
Time Frequency ◽  
Wavelet Features ◽  
Classification Evaluation ◽  
Signal Processing Methods

Steady-state visual evoked potentials (SSVEPs) have been designated to be appropriate and are in use in many areas such as clinical neuroscience, cognitive science, and engineering. SSVEPs have become popular recently, due to their advantages including high bit rate, simple system structure and short training time. To design SSVEP-based BCI system, signal processing methods appropriate to the signal structure should be applied. One of the most appropriate signal processing methods of these non-stationary signals is the Wavelet Transform. In this study, we investigated both the effect of choosing a mother wavelet function and the most successful combination of classifier algorithm, wavelet features, and frequency pairs assigned to BCI commands. SSVEP signals that were recorded at seven different stimulus frequencies (6–6.5 – 7 – 7.5 – 8.2 – 9.3 – 10 Hz) were used in this study. A total of 115 features were extracted from time, frequency, and time-frequency domains. These features were classified by a total of seven different classification processes. Classification evaluation was presented with the 5-fold cross-validation method and accuracy values. According to the results, (I) the most successful wavelet function was Haar wavelet, (II) the most successful classifier was Ensemble Learning, (III) using the feature vector consisting of energy, entropy, and variance features yielded higher accuracy than using one of these features alone, and (IV) the highest performances were obtained in the frequency pairs with “6–10”, “6.5–10”, “7–10”, and “7.5–10” Hz.

scholarly journals Time-Frequency Analysis of Compressor Rotating Stall by Means of Wavelet Transform

1996 ◽  
Author(s):  
Shengfang Liao ◽  
Jingyi Chen
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Analysis ◽  
Rotating Stall ◽  
Processing Methods ◽  
Stall Inception ◽  
Time Frequency ◽  
Signal Processing Methods ◽  
Conventional Signal

In this paper, an application of Wavelet Transform, which is a newly developed time-frequency technique of signal processing, is demonstrated in analyzing compressor rotating stall signals. In contrast to conventional signal processing methods, e.g. Fourier Transform, Wavelet Transform is very suitable for analyzing transient processes as rotating stall inception in compressors. In this study, some typical rotating stall signals are processed via Morlet’s wavelet. It is concluded that Wavelet Transform has a great advantage in detecting rotating stall inceptions, which are usually very weak and embedded in relatively stronger noises. In the diagrams resulted from the transform, every emergence of precursor as well as full stall signals of a certain frequency is illustrated versus time.

SEEG Recordings: From Signal Processing to Models of Epileptogenic Networks

2018 ◽  
pp. 520-534
Author(s):  
Fabrice Wendling ◽  
Pascal Benquet ◽  
Fabrice Bartolomei
Keyword(s):  
Signal Processing ◽  
Computational Models ◽  
Visual Analysis ◽  
Epileptiform Activity ◽  
Morphological Characteristics ◽  
Interictal Spikes ◽  
Processing Methods ◽  
Time Frequency ◽  
High Frequency Oscillations ◽  
Signal Processing Methods

Signal processing methods may constitute a substantial complement to visual analysis of SEEG signals in providing quantified information on signals (e.g. morphological characteristics) and in computing meaningful quantities that are not accessible to visual inspection (e.g. spectral properties or synchrony). In addition, and complementary to signal processing, computational neuroscience aims at developing models of epileptogenic networks and ultimately explaining some mechanisms involved in the generation of epileptiform activity. This chapter reviews a number of signal processing methods (time–frequency analysis, epileptogenicity index, and nonlinear correlation analysis) and computational models (at micro- and mesoscopic levels). The methods and models described illustrate the insight that can be gained about the information conveyed by SEEG signals recorded from epileptogenic networks observed during interictal (spikes and high-frequency oscillations) and ictal (fast-onset discharges) periods. Provided examples show that appropriate processing/modelling methods applied to electrophysiological signals can considerably improve the interpretation of SEEG recordings.

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