Power Spectral Entropy-based Graph Construction for Rotating Machinery Diagnosis Using Multi-sensor

2021 ◽  
Author(s):  
Chaoying Yang ◽  
Kaibo Zhou ◽  
Jie Liu ◽  
Qi Xu
Keyword(s):  
Rotating Machinery ◽  
Spectral Entropy ◽  
Power Spectral

Quantifying the complexity of human colonic pressure signals using an entropy measure

2016 ◽  
Vol 61 (1) ◽  
pp. 127-132 ◽  
Author(s):  
Fei Xu ◽  
Guozheng Yan ◽  
Kai Zhao ◽  
Li Lu ◽  
Zhiwu Wang ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Colonic Motility ◽  
Approximate Entropy ◽  
Entropy Measure ◽  
Intrinsic Mode Functions ◽  
Spectral Entropy ◽  
Mode Decomposition ◽  
Power Spectral ◽  
Reconstructed Signal ◽  
Non Stationary Signal

Abstract Studying the complexity of human colonic pressure signals is important in understanding this intricate, evolved, dynamic system. This article presents a method for quantifying the complexity of colonic pressure signals using an entropy measure. As a self-adaptive non-stationary signal analysis algorithm, empirical mode decomposition can decompose a complex pressure signal into a set of intrinsic mode functions (IMFs). Considering that IMF2, IMF3, and IMF4 represent crucial characteristics of colonic motility, a new signal was reconstructed with these three signals. Then, the time entropy (TE), power spectral entropy (PSE), and approximate entropy (AE) of the reconstructed signal were calculated. For subjects with constipation and healthy individuals, experimental results showed that the entropies of reconstructed signals between these two classes were distinguishable. Moreover, the TE, PSE, and AE can be extracted as features for further subject classification.

scholarly journals Fault Feature Extraction of Hydraulic Pumps Based on Symplectic Geometry Mode Decomposition and Power Spectral Entropy

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 476 ◽  
Author(s):  
Zhi Zheng ◽  
Ge Xin
Keyword(s):  
Feature Extraction ◽  
Symplectic Geometry ◽  
Spectral Entropy ◽  
Hydraulic Pump ◽  
Mode Decomposition ◽  
Power Spectral ◽  
Reconstructed Signal ◽  
Pump Signal ◽  
The Difference ◽  
Fault Feature Extraction

Aiming at fault feature extraction of a hydraulic pump signal, a new method based on symplectic geometry mode decomposition (SGMD) and power spectral entropy (PSE) is proposed. First, the SGMD is applied to decompose a multi-component fault signal, then the N symplectic geometry components (SGCs) can be obtained. Second, the N SGCs are reconstructed as a signal of interest and, consequently, the power spectral entropy of each constructed signal is computed to quantify the complexity and uncertainty of their spectra. Finally, the difference value (D-value) between the adjacent entropies is used as a SGCs criterion, whose turning point indicates the most information of reconstructed signal. Hydraulic pump signals are tested and verified, and results demonstrate that the proposed method can extract the richest fault feature information of hydraulic pump signals effectively.

scholarly journals Correction to: EEMD-based online milling chatter detection by fractal dimension and power spectral entropy

2020 ◽  
Vol 111 (7-8) ◽  
pp. 2401-2402
Author(s):  
Yongjian Ji ◽  
Xibin Wang ◽  
Zhibing Liu ◽  
Zhenghu Yan ◽  
Li Jiao ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Chatter Detection ◽  
Spectral Entropy ◽  
Power Spectral ◽  
Milling Chatter

EEMD-based online milling chatter detection by fractal dimension and power spectral entropy

2017 ◽  
Vol 92 (1-4) ◽  
pp. 1185-1200 ◽  
Author(s):  
Yongjian Ji ◽  
Xibin Wang ◽  
Zhibing Liu ◽  
Zhenghu Yan ◽  
Li Jiao ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Chatter Detection ◽  
Spectral Entropy ◽  
Power Spectral ◽  
Milling Chatter

scholarly journals Implications of Noise on Neural Correlates of Consciousness: A Computational Analysis of Stochastic Systems of Mutually Connected Processes

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 583
Author(s):  
Pavel Kraikivski
Keyword(s):  
Dynamical Systems ◽  
Stochastic Systems ◽  
System Size ◽  
Neural Correlates ◽  
Spectral Entropy ◽  
Modeling And Analysis ◽  
Neural Correlates Of Consciousness ◽  
Power Spectral ◽  
Neuronal Processes ◽  
Random Fluctuations

Random fluctuations in neuronal processes may contribute to variability in perception and increase the information capacity of neuronal networks. Various sources of random processes have been characterized in the nervous system on different levels. However, in the context of neural correlates of consciousness, the robustness of mechanisms of conscious perception against inherent noise in neural dynamical systems is poorly understood. In this paper, a stochastic model is developed to study the implications of noise on dynamical systems that mimic neural correlates of consciousness. We computed power spectral densities and spectral entropy values for dynamical systems that contain a number of mutually connected processes. Interestingly, we found that spectral entropy decreases linearly as the number of processes within the system doubles. Further, power spectral density frequencies shift to higher values as system size increases, revealing an increasing impact of negative feedback loops and regulations on the dynamics of larger systems. Overall, our stochastic modeling and analysis results reveal that large dynamical systems of mutually connected and negatively regulated processes are more robust against inherent noise than small systems.

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