High-Resolution Phase Doppler Particle Sizing based on Hilbert transform signal processing

1996 ◽  
Vol 13 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Peter Lehmann ◽  
E. Hanno Schombacher ◽  
Thomas Wriedt
Keyword(s):  
Signal Processing ◽  
High Resolution ◽  
Hilbert Transform ◽  
Particle Sizing

scholarly journals Photonic RF channelizer based on 49GHz soliton crystal microcombs

2020 ◽  
Author(s):  
David Moss ◽  
Roberto Morandotti ◽  
Arnan Mitchell ◽  
xingyuan xu ◽  
mengxi tan ◽  
...  
Keyword(s):  
Signal Processing ◽  
High Resolution ◽  
Ring Resonator ◽  
Rf Receivers ◽  
High Q ◽  
Monolithically Integrated ◽  
Broadband Radio ◽  
Significant Step ◽  
Multi Wavelength ◽  
Reduced Complexity

We report a broadband radio frequency (RF) channelizer with up to 92 channels using a coherent microcomb source. A soliton crystal microcomb, generated by a 49 GHz micro-ring resonator (MRR), is used as a multi-wavelength source. Due to its ultra-low comb spacing, up to 92 wavelengths are available in the C band, yielding a broad operation bandwidth. Another high-Q MRR is employed as a passive optical periodic filter to slice the RF spectrum with a high resolution of 121.4 MHz. We experimentally achieve an instantaneous RF operation bandwidth of 8.08 GHz and verify RF channelization up to 17.55 GHz via thermal tuning. Our approach is a significant step towards the monolithically integrated photonic RF receivers with reduced complexity, size, and unprecedented performance, which is important for wide RF applications ranging from broadband analog signal processing to digital-compatible signal detection.

Fault Detection of Broken Rotor Bar Using an Improved form of Hilbert–Huang Transform

2018 ◽  
Vol 17 (02) ◽  
pp. 1850012 ◽  
Author(s):  
F. Sabbaghian-Bidgoli ◽  
J. Poshtan
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Hilbert Transform ◽  
Wavelet Packet ◽  
Amplitude Spectrum ◽  
Vibration Signal ◽  
External Noise ◽  
Vibration Signals ◽  
Broken Rotor Bar ◽  
Hilbert Huang Transform

Signal processing is an integral part in signal-based fault diagnosis of rotary machinery. Signal processing converts the raw data into useful features to make the diagnostic operations. These features should be independent from the normal working conditions of the machine and the external noise. The extracted features should be sensitive only to faults in the machine. Therefore, applying more efficient processing techniques in order to achieve more useful features that bring faster and more accurate fault detection procedure has attracted the attention of researchers. This paper attempts to improve Hilbert–Huang transform (HHT) using wavelet packet transform (WPT) as a preprocessor instead of ensemble empirical mode decomposition (EEMD) to decompose the signal into narrow frequency bands and extract instantaneous frequency and compares the efficiency of the proposed method named “wavelet packet-based Hilbert transform (WPHT)” with the HHT in the extraction of broken rotor bar frequency components from vibration signals. These methods are tested on vibration signals of an electro-pump experimental setup. Moreover, this project applies wavelet packet de-noising to remove the noise of vibration signal before applying both methods mentioned and thereby achieves more useful features from vibration signals for the next stages of diagnosis procedure. The comparison of Hilbert transform amplitude spectrum and the values and numbers of detected instantaneous frequencies using HHT and WPHT techniques indicates the superiority of the WPHT technique to detect fault-related frequencies as an improved form of HHT.

scholarly journals Rapid High-Resolution Wavenumber Extraction from Ultrasonic Guided Waves Using Adaptive Array Signal Processing

2018 ◽  
Vol 8 (4) ◽  
pp. 652 ◽  
Author(s):  
Shigeaki Okumura ◽  
Vu-Hieu Nguyen ◽  
Hirofumi Taki ◽  
Guillaume Haïat ◽  
Salah Naili ◽  
...  
Keyword(s):  
Signal Processing ◽  
High Resolution ◽  
Guided Waves ◽  
Array Signal Processing ◽  
Ultrasonic Guided Waves ◽  
Adaptive Array ◽  
Adaptive Array Signal Processing
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