scholarly journals Detection of Patterns in Pressure Signal of Compressed Air System Using Wavelet Transform

2021 ◽  
pp. 61-67
Author(s):  
Mohamad Thabet ◽  
David Sanders ◽  
Nils Bausch
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Compressed Air ◽  
Learning Tools ◽  
Time Frequency ◽  
Pressure Signal ◽  
Operating Modes ◽  
Air System ◽  
The Time Domain ◽  
Future Work

AbstractThis paper investigates detecting patterns in the pressure signal of a compressed air system (CAS) with a load/unload control using a wavelet transform. The pressure signal of a CAS carries useful information about operational events. These events form patterns that can be used as ‘signatures’ for event detection. Such patterns are not always apparent in the time domain and hence the signal was transformed to the time-frequency domain. Three different CAS operating modes were considered: idle, tool activation and faulty. The wavelet transforms of the CAS pressure signal reveal unique features to identify events within each mode. Future work will investigate creating machine learning tools for that utilize these features for fault detection in CAS.

scholarly journals Discrete and Dual Tree Wavelet Features for Real-Time Speech/Music Discrimination

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Timur Düzenli ◽  
Nalan Özkurt
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Wavelet Transforms ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Vanishing Moments ◽  
Zero Crossings ◽  
Time Frequency ◽  
Common Time ◽  
Classification Tool

The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features such as number of zero crossings, spectral centroid, spectral flux, and Mel cepstral coefficients. The artificial neural networks have been used as classification tool. The principal component analysis has been applied to eliminate the correlated features before the classification stage. For discrete wavelet transform, considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. The dual tree wavelet transform has also demonstrated a successful performance both in terms of accuracy and time consumption. Finally, a real-time discrimination system has been implemented using the Daubhecies8 wavelet which has the best accuracy.

CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms

2020 ◽  
Vol 18 (06) ◽  
pp. 2030001 ◽  
Author(s):  
Rodrigo Capobianco Guido ◽  
Fernando Pedroso ◽  
André Furlan ◽  
Rodrigo Colnago Contreras ◽  
Luiz Gustavo Caobianco ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Time ◽  
Wavelet Transforms ◽  
Signal Analysis ◽  
Applied Mathematics ◽  
Discrete Wavelet ◽  
Frequency Signal ◽  
Time Frequency ◽  
Subsequent Investigation ◽  
Different Types

Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.

scholarly journals Time-frequency analysis of the Surge Onset in the Centrifugal Blower

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Grzegorz Liskiewicz ◽  
Longin Horodko
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Continuous Wavelet Transform ◽  
Working Condition ◽  
Continuous Wavelet ◽  
Transient Phase ◽  
Centrifugal Blower ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Pressure Signal

Abstract Time frequency analysis of the surge onset was performed in the centrifugal blower. A pressure signal was registered at the blower inlet, outlet and three locations at the impeller shroud. The time-frequency scalograms were obtained by means of the Continuous Wavelet Transform (CWT). The blower was found to successively operate in four different conditions: stable working condition, inlet recirculation, transient phase and deep surge. Scalograms revealed different spectral structures of aforementioned phases and suggest possible ways of detecting the surge predecessors.

scholarly journals Quantitative feature analysis of continuous analytic wavelet transforms of electrocardiography and electromyography

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170250 ◽  
Author(s):  
Mark P. Wachowiak ◽  
Renata Wachowiak-Smolíková ◽  
Michel J. Johnson ◽  
Dean C. Hay ◽  
Kevin E. Power ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Wavelet Transforms ◽  
Continuous Wavelet ◽  
Theme Issue ◽  
Practical Applications ◽  
Time Frequency ◽  
New Family ◽  
Physiological Processes ◽  
Analytic Wavelet

Theoretical and practical advances in time–frequency analysis, in general, and the continuous wavelet transform (CWT), in particular, have increased over the last two decades. Although the Morlet wavelet has been the default choice for wavelet analysis, a new family of analytic wavelets, known as generalized Morse wavelets, which subsume several other analytic wavelet families, have been increasingly employed due to their time and frequency localization benefits and their utility in isolating and extracting quantifiable features in the time–frequency domain. The current paper describes two practical applications of analysing the features obtained from the generalized Morse CWT: (i) electromyography, for isolating important features in muscle bursts during skating, and (ii) electrocardiography, for assessing heart rate variability, which is represented as the ridge of the main transform frequency band. These features are subsequently quantified to facilitate exploration of the underlying physiological processes from which the signals were generated. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

COMPLEMENTARY ANALYSIS TO HEART SOUNDS WHILE USING THE SHORT TIME FOURIER AND THE CONTINUOUS WAVELET TRANSFORMS

2007 ◽  
Vol 19 (05) ◽  
pp. 331-339
Author(s):  
S. M. Debbal ◽  
F. Bereksi-Reguig
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Wavelet Transforms ◽  
Continuous Wavelet ◽  
Short Time Fourier Transform ◽  
Continuous Wavelet Transforms ◽  
Time Frequency ◽  
Quantitative Measurements ◽  
Short Time

This paper presents the analysis and comparisons of the short time Fourier transform (STFT) and the continuous wavelet transform techniques (CWT) to the four sounds analysis (S1, S2, S3 and S4). It is found that the spectrogram short-time Fourier transform (STFT), cannot perfectly detect the internals components of these sounds that the continuous wavelet transform. However, the short time Fourier transform can provide correctly the extent of time and frequency of these four sounds. Thus, the STFT and the CWT techniques provide more features and characteristics of the sounds that will hemp physicians to obtain qualitative and quantitative measurements of the time-frequency characteristics.

scholarly journals SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hongpo Zhang ◽  
Renke He ◽  
Honghua Dai ◽  
Mingliang Xu ◽  
Zongmin Wang
Keyword(s):  
Atrial Fibrillation ◽  
Wavelet Transform ◽  
Morbidity And Mortality ◽  
High Morbidity ◽  
Ecg Signal ◽  
Time Frequency ◽  
Ecg Signals ◽  
Convolution Process ◽  
The Time Domain ◽  
Atrial Fibrillation Detection

Atrial fibrillation is the most common arrhythmia and is associated with high morbidity and mortality from stroke, heart failure, myocardial infarction, and cerebral thrombosis. Effective and rapid detection of atrial fibrillation is critical to reducing morbidity and mortality in patients. Screening atrial fibrillation quickly and efficiently remains a challenging task. In this paper, we propose SS-SWT and SI-CNN: an atrial fibrillation detection framework for the time-frequency ECG signal. First, specific-scale stationary wavelet transform (SS-SWT) is used to decompose a 5-s ECG signal into 8 scales. We select specific scales of coefficients as valid time-frequency features and abandon the other coefficients. The selected coefficients are fed to the scale-independent convolutional neural network (SI-CNN) as a two-dimensional (2D) matrix. In SI-CNN, a convolution kernel specifically for the time-frequency characteristics of ECG signals is designed. During the convolution process, the independence between each scale of coefficient is preserved, and the time domain and the frequency domain characteristics of the ECG signal are effectively extracted, and finally the atrial fibrillation signal is quickly and accurately identified. In this study, experiments are performed using the MIT-BIH AFDB data in 5-s data segments. We achieve 99.03% sensitivity, 99.35% specificity, and 99.23% overall accuracy. The SS-SWT and SI-CNN we propose simplify the feature extraction step, effectively extracts the features of ECG, and reduces the feature redundancy that may be caused by wavelet transform. The results shows that the method can effectively detect atrial fibrillation signals and has potential in clinical application.

A common-reflection-point gather random noise attenuation method based on the synchrosqueezing wavelet transform

2021 ◽  
pp. 1-81
Author(s):  
Xiaokai Wang ◽  
Zhizhou Huo ◽  
Dawei Liu ◽  
Weiwei Xu ◽  
Wenchao Chen
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Seismic Data ◽  
Random Noise ◽  
Principal Component ◽  
Frequency Resolution ◽  
Reflection Point ◽  
Transform Method ◽  
Time Frequency ◽  
The Time Domain

Common-reflection-point (CRP) gather is one extensive-used prestack seismic data type. However, CRP suffers more noise than poststack seismic dataset. The events in the CRP gather are always flat, and the effective signals from neighboring traces in the CRP gather have similar forms not only in the time domain but also in the time-frequency domain. Therefore, we firstly use the synchrosqueezing wavelet transform (SSWT) to decompose seismic traces to the time-frequency domain, as the SSWT has better time-frequency resolution and reconstruction properties. Then we propose to use the similarity of neighboring traces to smooth and threshold the SSWT coefficients in the time-frequency domain. Finally, we used the modified SSWT coefficients to reconstruct the denoised traces for the CRP gather. Synthetic and field data examples show that our proposed method can effectively attenuate random noise with a better attenuation performance than the commonly-used principal component analysis, FX filter, and the continuous wavelet transform method.

Application of Wavelets in Detection of Cavities Under Pavements by Surface Waves

2003 ◽  
Vol 1860 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Parisa Shokouhi ◽  
Nenad Gucunski ◽  
Ali Maher
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Element Model ◽  
Energy Concentration ◽  
Response Analysis ◽  
Analysis Tool ◽  
Cavity Detection ◽  
Time Frequency ◽  
Homogeneous Half Space ◽  
Power Spectral

Application of wavelet transforms in the detection of underground shallow cavities is investigated. Wave propagation is simulated through a transient response analysis on an axisymmetric finite element model. Cavities in a homogeneous half-space and a pavement system of a variety of shapes and embedment depths are considered. The continuous wavelet transform is introduced as a new tool for cavity detection. Effects of different types of cavities on power spectral surfaces (power spectral amplitudes versus frequency and receiver location) and Gaussian wavelet time-frequency maps (wavelet transform coefficients versus time and frequency) are studied. Results show strong energy concentration in power spectral surfaces right in front of a cavity in certain frequency bands. Time and frequency signatures of waves reflected from near and far faces of the cavity can be clearly observed in the wavelet time-frequency maps. These observations are used to locate and estimate the size of the cavity. It is demonstrated that the wavelet transform is a promising analysis tool for cavity detection and characterization.

scholarly journals A time–frequency analysis based internal leakage detection method for hydraulic actuators

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668505 ◽  
Author(s):  
Zhikai Yao ◽  
Jian Tang ◽  
Ting Rui ◽  
Jinhui Duan
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Continuous Wavelet Transform ◽  
Control Valve ◽  
Experimental Tests ◽  
Continuous Wavelet ◽  
Time Frequency Analysis ◽  
Hydraulic Actuators ◽  
Time Frequency ◽  
Pressure Signal

Internal leakage in the hydraulic actuators is concerned in this article, which is caused by seal damage, resulting in the limited performance of the system. To study the issue, this article proposes a method based on time–frequency analysis for the detection in hydraulic actuators. First, the pressure signal after filtering of the actuator in one side is collected when the control valve is affected by sinusoidal-like inputs. Second, the time–frequency image of pressure signal in a period at different leakage levels is obtained after continuous wavelet transform. Third is the sum of pixels in the time–frequency image. It is shown that the feature pattern is established by the sum of pixels in the time–frequency image that internal leakage and its severity could be detected effectively. The proposed method required two baselines and premeasured the pressure signal at 11 leakage levels. Once the sum of pixels in the time–frequency image values, obtained from the time–frequency image by continuous wavelet transform based on wavelet Cmor1-1 in subsequent offline tests, are greater than the first baseline, a leakage alarm is triggered. Furthermore, a severe leakage alarm is triggered when the value is greater than the second baseline. Experimental tests show the accuracy of the proposed scheme at different mother wavelets, and it is done without knowing the model of actuator or leakage.

scholarly journals The Differential Pressure Signal De-noised by Domain Transform Combined with Wavelet Threshold

2017 ◽  
Vol 7 (1) ◽  
pp. 19-25
Author(s):  
Yuhao Zhang ◽  
Haihui Wang ◽  
Chao Li
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Aircraft Engine ◽  
Differential Pressure ◽  
Large Fluctuation ◽  
Pressure Signal ◽  
Robust Adaptive

AbstractIn the process of estimating the thrust of an aircraft engine, there is a big problem that the differential pressure signal has large fluctuation. To deal with this problem, we develop an effective and robust adaptive de-noising algorithm based on domain transform combined with wavelet transform (D-WT). First, we do the domain transform for the signal, then sample the transformed signal, and finally the wavelet threshold transform is performed for the signal. Compared with the traditional wavelet transforms, the D-WT method filters the noise effectively and keeps more details.

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