DNLS: a Detection Method based on Normalized Short-Time Fourier Transform-Radon Transform for Low Frequency Sonar Pulse Signal

Sound Sources ◽

Time Frequency ◽

The paper discusses means to predict sound source position emitted by fault machine components based on a single microphone moving in a linear track with constant speed. The position of sound source that consists of some frequency spectrum is detected by time-frequency distribution of the sound signal through Short Time Fourier Transform (STFT) and Continues Wavelet Transform (CWT). As the amplitude of sound pressure increases when the microphone moves closer, the source position and frequency are predicted from the peaks of time-frequency contour map. Firstly, numerical simulation is conducted using two sound sources that generate four different frequencies of sound. The second case is experimental analysis using rotating machine being monitored with unbalanced, misalignment and bearing defect. The result shows that application of both STFT and CWT are able to detect multiple sound sources position with multiple frequency peaks caused by machine fault. The STFT can indicate the frequency very clearly, but not for the peak position. On the other hand, the CWT is able to predict the position of sound at low frequency very clearly. However, it is failed to detect the exact frequency because of overlapping.

Seismic spectral decomposition using deconvolutive short-time Fourier transform spectrogram

Geophysics ◽

10.1190/geo2012-0125.1 ◽

2013 ◽

Vol 78 (2) ◽

pp. V43-V51 ◽

Cited By ~ 37

Author(s):

Wenkai Lu ◽

Fangyu Li

Keyword(s):

Fourier Transform ◽

Spectral Decomposition ◽

Reservoir Characterization ◽

Low Frequency ◽

Seismic Signal ◽

Frequency Resolution ◽

Distribution Method ◽

Time Frequency ◽

The spectral decomposition technique plays an important role in reservoir characterization, for which the time-frequency distribution method is essential. The deconvolutive short-time Fourier transform (DSTFT) method achieves a superior time-frequency resolution by applying a 2D deconvolution operation on the short-time Fourier transform (STFT) spectrogram. For seismic spectral decomposition, to reduce the computation burden caused by the 2D deconvolution operation in the DSTFT, the 2D STFT spectrogram is cropped into a smaller area, which includes the positive frequencies fallen in the seismic signal bandwidth only. In general, because the low-frequency components of a seismic signal are dominant, the removal of the negative frequencies may introduce a sharp edge at the zero frequency, which would produce artifacts in the DSTFT spectrogram. To avoid this problem, we used the analytic signal, which is obtained by applying the Hilbert transform on the original real seismic signal, to calculate the STFT spectrogram in our method. Synthetic and real seismic data examples were evaluated to demonstrate the performance of the proposed method.

Application of Short Time Fourier Transform and Wavelet Transform for Sound Source Localization Using Single Moving Microphone in Machine Condition Monitoring

KnE Engineering ◽

10.18502/keg.v1i1.488 ◽

2016 ◽

Vol 1 ◽

Cited By ~ 1

Author(s):

Meifal Rusli

Keyword(s):

Fourier Transform ◽

Wavelet Transform ◽

Sound Source ◽

Low Frequency ◽

Peak Position ◽

Source Position ◽

Sound Sources ◽

Time Frequency ◽

The paper discusses means to predict sound source position emitted by fault machine components based on a single microphone moving in a linear track with constant speed. The position of sound source that consists of some frequency spectrum is detected by time-frequency distribution of the sound signal through Short Time Fourier Transform (STFT) and Continues Wavelet Transform (CWT). As the amplitude of sound pressure increases when the microphone moves closer, the source position and frequency are predicted from the peaks of time-frequency contour map. Firstly, numerical simulation is conducted using two sound sources that generate four different frequencies of sound. The second case is experimental analysis using rotating machine being monitored with unbalanced, misalignment and bearing defect. The result shows that application of both STFT and CWT are able to detect multiple sound sources position with multiple frequency peaks caused by machine fault. The STFT can indicate the frequency very clearly, but not for the peak position. On the other hand, the CWT is able to predict the position of sound at low frequency very clearly. However, it is failed to detect the exact frequency because of overlapping.

High impedance fault detection method based on the short-time Fourier transform

IET Generation Transmission & Distribution ◽

10.1049/iet-gtd.2018.0093 ◽

2018 ◽

Vol 12 (11) ◽

pp. 2577-2584 ◽

Cited By ~ 24

Author(s):

Érica Mangueira Lima ◽

Caio Marco dos Santos Junqueira ◽

Núbia Silva Dantas Brito ◽

Benemar Alencar de Souza ◽

Rodrigo de Almeida Coelho ◽

...

Keyword(s):

Fourier Transform ◽

Fault Detection ◽

Detection Method ◽

High Impedance ◽

High Impedance Fault ◽

A Novel Short-Time Fourier Transform-Based Fall Detection Algorithm Using 3-Axis Accelerations

Mathematical Problems in Engineering ◽

10.1155/2015/394340 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7

Author(s):

Isu Shin ◽

Jongsang Son ◽

Soonjae Ahn ◽

Jeseong Ryu ◽

Sunwoo Park ◽

...

Keyword(s):

Fourier Transform ◽

Low Frequency ◽

Fall Detection ◽

Detection Algorithm ◽

Frequency Components ◽

Short Time ◽

Male Subjects ◽

Signal Vector ◽

Vector Magnitude

The short-time Fourier transform- (STFT-) based algorithm was suggested to distinguish falls from various activities of daily living (ADLs). Forty male subjects volunteered in the experiments including three types of falls and four types of ADLs. An inertia sensor unit attached to the middle of two anterior superior iliac spines was used to measure the 3-axis accelerations at 100 Hz. The measured accelerations were transformed to signal vector magnitude values to be analyzed using STFT. The powers of low frequency components were extracted, and the fall detection was defined as whether the normalized power was less than the threshold (50% of the normal power). Most power was observed at the frequency band lower than 5 Hz in all activities, but the dramatic changes in the power were found only in falls. The specificity of 1–3 Hz frequency components was the best (100%), but the sensitivity was much smaller compared with 4 Hz component. The 4 Hz component showed the best fall detection with 96.9% sensitivity and 97.1% specificity. We believe that the suggested algorithm based on STFT would be useful in the fall detection and the classification from ADLs as well.

Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

Applied Sciences ◽

10.3390/app11062582 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2582

Author(s):

Lucas M. Martinho ◽

Alan C. Kubrusly ◽

Nicolás Pérez ◽

Jean Pierre von der Weid

Keyword(s):

Fourier Transform ◽

Guided Waves ◽

Strain Level ◽

Energy Concentration ◽

Time Frequency ◽

Frequency Representation ◽

The Fourier Transform ◽

Short Time ◽

Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.