Permanent magnet synchronous machine stator windings fault detection by Hilbert–Huang transform
The Hilbert–Huang transform (HHT) is a time-frequency signal analysis method based on empirical mode decomposition and the Hilbert transform. It is well suited for reliable fault detection since it is unaffected by transient conditions which might cause ...
Fernando Alvarez-Gonzalez +3 more
doaj +1 more source
Cognitive Radio Sensing Using Hilbert Huang Transform
Vast segments of the frequency spectrum are reserved for primary (licensed) users. These legacy users often un-der-utilize their reserved spectrum thus causing bandwidth waste. The unlicensed (secondary) users can take advantage of this fact and exploit the spectral holes (vacant spectrum segments). Since spectrum occupancy is transient in nature it is
K. A. Narayanankutty +5 more
openaire +2 more sources
Cross‐Modal Urban Sensing: Evaluating Sound–Vision Alignment Across Street‐Level and Aerial Imagery
ABSTRACT Environmental soundscapes carry rich ecological and social information, yet remain underutilized in geographic analysis. This study introduces a unified cross‐modal evaluation framework to examine how urban sounds align with visual representations from street‐level and aerial perspectives, and how different visual representation strategies ...
Pengyu Chen +3 more
wiley +1 more source
Advanced Feature Analysis of Eddy Current Testing Signals for Rail Surface Defect Characterization
The maintenance of the railways is of paramount importance for safe and reliable transport. Eddy Current Testing (ECT) provides high-resolution time-series signals that capture subtle anomalies on the rail surface. This paper expands on previous analyses
Michele Quercio +3 more
doaj +1 more source
Segmentation of Killer Whale Vocalizations Using the Hilbert-Huang Transform
The study of cetacean vocalizations is usually based on spectrogram analysis. The feature extraction is obtained from 2D methods like the edge detection algorithm.
Olivier Adam
doaj +1 more source
Gear Fault Diagnosis Method Based on Deep Transfer Learning
Aiming at the problem of insufficient gear fault samples, a fault diagnosis method of transfer learning based on Hilbert-Huang spectrum and pre-trained VGG16 model is proposed.
Liu Shihao, Wang Xiyang, Gong Tingkai
doaj +2 more sources
Quantifying and Classifying Non‐Stationary Turbulence Under Sub‐Mesoscale Disturbances
Abstract Non‐stationary turbulence induced by sub‐mesoscale disturbances can substantially bias flux estimates, yet is largely overlooked in existing evaluation frameworks. Using turbulence measurements over the heterogeneous Loess Plateau, we found that classical stationarity tests classify >60% of records containing clear submesoscale disturbances as
Yue Xu +7 more
wiley +1 more source
Due to the characteristics of structure, brushless direct current motor has a long lifetime, which makes it more difficult to obtain failure data. Accordingly, valid degeneration characteristics are required in life prediction.
Wenhui Fan +3 more
doaj +1 more source
How do you make a time series sing like a choir? Using the Hilbert-Huang transform to extract embedded frequencies from economic or financial time series [PDF]
The Hilbert-Huang transform (HHT) was developed late last century but has still to be introduced to the vast majority of economists. The HHT transform is a way of extracting the frequency mode features of cycles embedded in any time series using an ...
Crowley, Patrick M
core
Signal Processing Methods Monitor Cranial Pressure [PDF]
Dr. Norden Huang, of Goddard Space Flight Center, invented a set of algorithms (called the Hilbert-Huang Transform, or HHT) for analyzing nonlinear and nonstationary signals that developed into a user-friendly signal processing technology for analyzing ...
core +1 more source

