A New Time-Frequency Spectrogram Analysis of FH Signals by Image Enhancement and Mathematical Morphology

2007 ◽  
Author(s):  
Xu Mankun ◽  
Ping Xijian ◽  
Li Tianyun ◽  
Xu Mantian
Keyword(s):  
Image Enhancement ◽  
Mathematical Morphology ◽  
Time Frequency ◽  
New Time

scholarly journals New Time-Frequency Transient Features for Nonintrusive Load Monitoring

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1437
Author(s):  
Mahfoud Drouaz ◽  
Bruno Colicchio ◽  
Ali Moukadem ◽  
Alain Dieterlen ◽  
Djafar Ould-Abdeslam
Keyword(s):  
Transient Signal ◽  
Learning Tools ◽  
Time Frequency ◽  
Energy Measure ◽  
Load Monitoring ◽  
Feature Based ◽  
Stockwell Transform ◽  
Processing Techniques ◽  
Equipment Laboratory ◽  
New Time

A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively.

Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo
Keyword(s):  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Critical Role ◽  
Real Data ◽  
Monitoring Method ◽  
Frequency Spectra ◽  
Arrival Times ◽  
Time Frequency ◽  
Fracturing Process ◽  
Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

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