scholarly journals Separation Method of Impulsive Fault Component for Gasoline Engine Based on Acoustic Signal Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Ning Dayong ◽  
Jiang Yuhua ◽  
Sun Hongyu ◽  
Zhang Zengmeng ◽  
Gong Yongjun ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Signal Analysis ◽  
Gasoline Engine ◽  
Signal To Noise Ratio ◽  
Connecting Rod ◽  
Signal To Noise ◽  
Acoustic Signal Analysis ◽  
Abnormal Sound ◽  
Speed Range ◽  
Superimposed Signals

This paper presents a developed dislocation superimposed method (DSM) for automatically extracting the component of impulsive signals from abnormal noise signals of an engine at a single speed range on the basis of the initial DSM. This method consists of three steps: using a correlation analysis to select an appropriate starting superposition point, superimposing abnormal sound signals to improve the signal-to-noise ratio, and intercepting superimposed signals to separate the fault component. Experimental results show that the developed DSM can effectively extract the fault characteristics of cylinder knocking and connecting rod bearing knocking. The developed approach can be applied to separate the fault characteristics of other types of rotating machines.

Embedded Sensing Design of Gearboxes for Vibration Monitoring

2010 ◽  
Vol 29-32 ◽  
pp. 264-268
Author(s):  
Z.S. Chen ◽  
Yong Min Yang ◽  
Z.X. Ge ◽  
C. Li
Keyword(s):  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Embedded Sensors ◽  
Analysis Method ◽  
Signal To Noise ◽  
Normal Functions ◽  
Embedded Sensing ◽  
Vibration Signal Analysis

Vibration signal analysis is one of the most effective ways for condition monitoring of gearboxes. Traditional way is often to mount additional accelerometer sensors on their cases, which has two unavoidable defects: signal-to-noise ratio is often low due to long signal travel paths and it may be not allowable due to space limitations. While embedded diagnostics (ED) can solve these two problems well by embedding sensors close to fault sources. However, embedded sensing design is a great challenge of ED because embedded sensors must have effects on the structure integrity of a gearbox. So it is necessary to determine how to embed sensors in order to ensure normal functions of a gearbox. In this paper, a finite element-based structure analysis method was proposed to perform embedded sensing design of bearings and gears to determine the optimal modified structure size.

Time-Frequency Correlation Method for Improving the Accuracy in Detecting Leaks in Pipelines

2013 ◽  
Vol 650 ◽  
pp. 443-446 ◽  
Author(s):  
Valeriy Stepanovich Avramchuk ◽  
Valeriy Ivanovich Goncharov
Keyword(s):  
Signal Analysis ◽  
Method Development ◽  
Signal To Noise Ratio ◽  
Correlation Method ◽  
Signal Frequency ◽  
Signal To Noise ◽  
Frequency Correlation ◽  
Time Frequency ◽  
Valid Signal ◽  
Spectrum Data

This report offers the solution that allows increasing the correlation leak detectors accuracy to a certain extend. This solution is based on signals frequency spectrum data and signal analysis time-frequency correlation method development. The idea is to analyze the correlation of two signals, to determine valid signal frequency limits and to set on this basis frequency filters parameters to improve signal-to-noise ratio.

Abnormal Noise Diagnosis of Internal Combustion Engine Using Wavelet Spatial Correlation Filter and Symmetrized Dot Pattern

2011 ◽  
Vol 141 ◽  
pp. 168-173 ◽  
Author(s):  
Cheng Yang ◽  
Tao Feng
Keyword(s):  
Internal Combustion Engine ◽  
Spatial Correlation ◽  
Combustion Engine ◽  
Signal To Noise Ratio ◽  
Internal Combustion ◽  
Correlation Filter ◽  
Polar Coordinates ◽  
Signal To Noise ◽  
Abnormal Sound ◽  
Novel Method

In order to identify engine status correctly, a novel method of abnormal noise diagnosis of internal combustion engine based on the wavelet spatial correlation filter (WSCF) and symmetrized dot pattern (SDP) is proposed. Firstly, the gathered acoustic signals are processed by wavelet spatial correlation filter in order to improve the SNR (signal-to-noise ratio); then, the filtered signals are transformed into spatial polar coordinates through SDP method. The experimental results demonstrate that the proposed methods are good classifiers and it can diagnose abnormal sound of engine accurately.

scholarly journals Multiway Canonical Correlation Analysis of Brain Signals

2018 ◽  
Author(s):  
Alain de Cheveigné ◽  
Giovanni M. Di Liberto ◽  
Dorothée Arzounian ◽  
Daniel D.E. Wong ◽  
Jens Hjortkjær ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Brain Signals ◽  
Natural Sound ◽  
Noise Ratio

AbstractBrain signals recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratio due to the presence of multiple competing sources and artifacts. A common remedy is to average over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with the same identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method.

scholarly journals A High-Resolution Dyadic Transform for Non-Stationary Signal Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3041
Author(s):  
Eduardo Trutié-Carrero ◽  
Diego Seuret-Jimenez ◽  
José M. Nieto-Jalil
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform ◽  
Noise Ratio ◽  
The Fourier Transform ◽  
Non Stationary Signal

This article shows a new Te-transform and its periodogram for applications that mainly exhibit stochastic behavior with a signal-to-noise ratio lower than −30 dB. The Te-transform is a dyadic transform that combines the properties of the dyadic Wavelet transform and the Fourier transform. This paper also provides another contribution, a corollary on the energy relationship between the untransformed signal and the transformed one using the Te-transform. This transform is compared with other methods used for the analysis in the frequency domain, reported in literature. To perform the validation, the authors created two synthetic scenarios: a noise-free signal scenario and another signal scenario with a signal-to-noise ratio equal to −69 dB. The results show that the Te-transform improves the sensitivity in the frequency spectrum with respect to previously reported methods.

Application of Wavelet De-Noising in Non-Stationary Signal Analysis Based on the Parameter Optimization of Improved Threshold Function

2013 ◽  
Vol 448-453 ◽  
pp. 2068-2076
Author(s):  
Hai Zhao Nie ◽  
Hui Liu ◽  
Lei Shi
Keyword(s):  
Wavelet Analysis ◽  
Parameter Optimization ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Threshold Function ◽  
Mean Square ◽  
Signal To Noise ◽  
Simple Method ◽  
Threshold Functions ◽  
Non Stationary Signal

Using wavelet analysis for non-stationary signal de-noising of electro-mechanical system is considered to be the best approach, and wavelet threshold de-noising method is the most simple method that needs the minimum amount of calculation. But this method in selecting threshold functions needs to be improved. Based on different domestic and foreign methods of improving threshold function, propose an improved bivariate threshold function. According to the simulation of non-stationary signal de-noising, the results show that the optimal de-noising results of different signals exist by taking different parameters. Compared with all the de-noising effects, application of the bivariate threshold function considering signal-to-noise ratio and mean square error is superior to the traditional soft and hard threshold functions. At the same time, it can significantly improve the filtering precision, and reserve the main signal details while effectively removing the noise well.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

Correlation averaging of two-dimensional crystals: basic strategy and refinements

1986 ◽  
Vol 44 ◽  
pp. 136-139
Author(s):  
W. Baumeister ◽  
R. Rachel ◽  
R. Guckenberger ◽  
R. Hegerl
Keyword(s):  
Low Dose ◽  
Signal To Noise Ratio ◽  
Real Space ◽  
Fourier Domain ◽  
Two Dimensional ◽  
Signal To Noise ◽  
Unit Cells ◽  
Lateral Displacements ◽  
Established Technique ◽  
Crystalline Array

IntroductionCorrelation averaging (CAV) is meanwhile an established technique in image processing of two-dimensional crystals /1,2/. The basic idea is to detect the real positions of unit cells in a crystalline array by means of correlation functions and to average them by real space superposition of the aligned motifs. The signal-to-noise ratio improves in proportion to the number of motifs included in the average. Unlike filtering in the Fourier domain, CAV corrects for lateral displacements of the unit cells; thus it avoids the loss of resolution entailed by these distortions in the conventional approach. Here we report on some variants of the method, aimed at retrieving a maximum of information from images with very low signal-to-noise ratios (low dose microscopy of unstained or lightly stained specimens) while keeping the procedure economical.

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