Multicomponent signal analysis: Interwoven Fourier decomposition method

2020 ◽  
Vol 104 ◽  
pp. 102771 ◽  
Author(s):  
Mehmet Dogan Elbi ◽  
Aydin Kizilkaya
Keyword(s):  
Signal Analysis ◽  
Decomposition Method ◽  
Fourier Decomposition ◽  
Multicomponent Signal

Vibration analysis and combustion parameter evaluation of CI engine based on Fourier Decomposition Method

2021 ◽  
pp. 146808742098819
Author(s):  
Wang Yang ◽  
Cheng Yong
Keyword(s):  
Vibration Analysis ◽  
Decomposition Method ◽  
Combustion Process ◽  
Pressure Rise ◽  
Maximum Pressure ◽  
Vibration Signals ◽  
Loop Control ◽  
Fourier Decomposition ◽  
Surface Vibration ◽  
Rise Rate

As a non-intrusive method for engine working condition detection, the engine surface vibration contains rich information about the combustion process and has great potential for the closed-loop control of engines. However, the measured engine surface vibration signals are usually induced by combustion as well as non-combustion excitations and are difficult to be utilized directly. To evaluate some combustion parameters from engine surface vibration, the tests were carried out on a single-cylinder diesel engine and a new method called Fourier Decomposition Method (FDM) was used to extract combustion induced vibration. Simulated and test results verified the ability of the FDM for engine vibration analysis. Based on the extracted vibration signals, the methods for identifying start of combustion, location of maximum pressure rise rate, and location of peak pressure were proposed. The cycle-by-cycle analysis of the results show that the parameters identified based on vibration and in-cylinder pressure have the similar trends, and it suggests that the proposed FDM-based methods can be used for extracting combustion induced vibrations and identifying the combustion parameters.

Adaptive power spectrum Fourier decomposition method with application in fault diagnosis for rolling bearing

Measurement ◽  
2021 ◽  
pp. 109837
Author(s):  
Jinde Zheng ◽  
Siqi Huang ◽  
Haiyang Pan ◽  
Jinyu Tong ◽  
Chengjun Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Decomposition Method ◽  
Rolling Bearing ◽  
Fourier Decomposition ◽  
Adaptive Power

Phase-Lagged Boundary Condition Methods for Aeroelastic Analysis of Turbomachines: A Comparative Study

1999 ◽  
Author(s):  
R. Srivastava ◽  
Milind A. Bakhle ◽  
Theo G. Keith ◽  
G. L. Stefko
Keyword(s):  
Boundary Condition ◽  
Comparative Study ◽  
Decomposition Method ◽  
Decomposition Methods ◽  
Navier Stokes ◽  
Fourier Decomposition ◽  
Computational Costs ◽  
Aeroelastic Analysis ◽  
Study Results ◽  
Time Marching

In the present work a comparative study of phase-lagged boundary condition methods is carried out. The relative merits and advantages of time-shifted and the Fourier decomposition methods are compared. Both methods are implemented in a time marching Euler/Navier-Stokes solver and are applied to a flat plate helical fan with harmonically oscillating blades to perform the study. Results were obtained for subsonic as well as supersonic inflows. Results for subsonic inflow showed good comparisons with published results and between the two methods along with comparable computational costs. For the supersonic inflow, despite the presence of shocks at the periodic boundary results from both the methods compared well, however, Fourier decomposition method was computationally more expensive. For linear flowfield Fourier decomposition method is best suited, especially for work-station environment. The time-shifted method is better suited for CRAY category of computers where fast input-output devices are available.

scholarly journals Various Networks Used for ECG Signals, Heart Beats and ECG Feature’s Classification

2020 ◽  
Vol 9 (3) ◽  
pp. 3291-3297
Keyword(s):  
Signal Analysis ◽  
Decomposition Method ◽  
Graphical Representation ◽  
Optimization Technique ◽  
Svm Classifier ◽  
Human Beings ◽  
Important Process ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Non Stationary Signal

ECG is a graphical representation of heart’s electrical activity such as electrical reploarization and depolarization of heart. It is an important non- stationary signal which contains the necessary information about the heart functioning so that it can be used to identify different abnormalities in heart beats and also to identify different diseases of human beings. Classification is an important process in ECG signal analysis and cardiac diseases diagnosis process. Different ECG signals as well as ECG parameters such as heart beats, features can be classified according to requirement. In this paper different classification networks have studied. SVM classifier with empirical mode decomposition represented the maximum accuracy of 99.54%. Any optimization technique can be used to increase the accuracy of SVM classifier with suitable decomposition method such as variatinal mode decomposition.

Resonance-based bandwidth Fourier decomposition method for gearbox fault diagnosis

2020 ◽  
Vol 32 (3) ◽  
pp. 035003
Author(s):  
Minqiang Deng ◽  
Aidong Deng ◽  
Jing Zhu ◽  
Yaowei Shi ◽  
Yang Liu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Decomposition Method ◽  
Fourier Decomposition

Detection of apnea events from ECG segments using Fourier decomposition method

2020 ◽  
Vol 61 ◽  
pp. 102005 ◽  
Author(s):  
Binish Fatimah ◽  
Pushpendra Singh ◽  
Amit Singhal ◽  
Ram Bilas Pachori
Keyword(s):  
Decomposition Method ◽  
Fourier Decomposition

Mental Arithmetic Task Classification using Fourier Decomposition Method

2020 ◽  
Author(s):  
Binish Fatimah ◽  
Abhishek Javali ◽  
Haaris Ansar ◽  
B G Harshitha ◽  
Hemant Kumar
Keyword(s):  
Decomposition Method ◽  
Mental Arithmetic ◽  
Arithmetic Task ◽  
Fourier Decomposition ◽  
Task Classification ◽  
Mental Arithmetic Task

scholarly journals A Generalized Demodulation and Hilbert Transform Based Signal Decomposition Method

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Zhi-Xiang Hu ◽  
Wei-Xin Ren ◽  
Zuo-Cai Wang ◽  
Yue-Ling Jing ◽  
Xia Yang
Keyword(s):  
Hilbert Transform ◽  
Decomposition Method ◽  
Signal Decomposition ◽  
Dynamic Strain ◽  
Cable Stayed Bridge ◽  
Multicomponent Signal ◽  
Frequency Components ◽  
Echolocation Signal ◽  
The Hilbert Transform ◽  
Strain Signal

This paper proposes a new signal decomposition method that aims to decompose a multicomponent signal into monocomponent signal. The main procedure is to extract the components with frequencies higher than a given bisecting frequency by three steps: (1) the generalized demodulation is used to project the components with lower frequencies onto negative frequency domain, (2) the Hilbert transform is performed to eliminate the negative frequency components, and (3) the inverse generalized demodulation is used to obtain the signal which contains components with higher frequencies only. By running the procedure recursively, all monocomponent signals can be extracted efficiently. A comprehensive derivation of the decomposition method is provided. The validity of the proposed method has been demonstrated by extensive numerical analysis. The proposed method is also applied to decompose the dynamic strain signal of a cable-stayed bridge and the echolocation signal of a bat.

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