scholarly journals Cutting-edge Mathematical Tools in Processing and Analysis of Signals in Marine and Navy

2012 ◽  
Vol 1 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Igor Vujović ◽  
Joško Šoda ◽  
Ivica Kuzmanić
Keyword(s):  
Decision Making ◽  
Signal Processing ◽  
Fourier Transform ◽  
Video Stream ◽  
Information Gathering ◽  
Pivotal Role ◽  
Surveillance Video ◽  
Time Frequency ◽  
Processing Techniques ◽  
Signal Processing Techniques

Signal processing plays a pivotal role in information gathering and decision making. This paper presents and compares different signal processing techniques used in marine and navy applications, primarily based on using wavelets as kernel. The article covers Fourier transform, time frequency wavelet based techniques such as bandelets, contourlets, curvelets, edgelets, wedgelets, shapelets, and ridgelets. In the example section of the paper, several transform techniques are presented and commented on the harbour surveillance video stream example.

A Brief Review on Fault Diagnosis of Rotating Machineries

2014 ◽  
Vol 541-542 ◽  
pp. 635-640 ◽  
Author(s):  
S.P. Mogal ◽  
D.I. Lalwani
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Response Measurement ◽  
Rotor Systems ◽  
Vibration Data ◽  
Systems Model ◽  
Processing Techniques ◽  
Signal Processing Techniques

Vibration in any rotating machines is due to faults like misalignment, unbalance, crack, mechanical looseness etc. Identification of these faults in rotor systems, model and vibration signal based methods are used. Signal processing techniques such as Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Wigner-Ville Distribution (WVD) and Wavelet Transform (WT) are applied to vibration data for faults identification. The intent of the paper is to present a review and summarize the recent research and developments performed in condition monitoring of rotor system with the purpose of rotor faults detection. In present paper discuss the different signal processing techniques applied for fault diagnosis. Vibration response measurement has given information concerning any fault within a rotating machine and many of the methods utilizing this technique are reviewed. A detail review of the subject of fault diagnosis in rotating machinery is presented.

Vibration Analyses of the Bearing Using the Time Frequency Domain Technique

2014 ◽  
Vol 592-594 ◽  
pp. 2091-2096
Author(s):  
H.N. Sharma ◽  
Santosh Verma
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Energy Levels ◽  
Vibration Signal ◽  
Test Rig ◽  
Time Frequency ◽  
Bearing Vibration ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Bearing System

This work employs the wavelet transform for reading the fault diagnosis in a rotor-bearing system. Initiating with literature review with some relevant studies of bearing fault and the signal processing techniques used followed by the theory of wavelet transform. A bearing test rig is shown which is used for implementing wavelet transform. A faulty bearing vibration signal is measured from the test rig; thereafter the fast Fourier transform is plotted to show the critical frequencies, bearing characteristics frequency and its harmonics. A scalogram showing the energy levels of signal is plotted as result. Faulty signal is analyzed using wavelet transform.

POWER DISTURBANCES PATTERN RECOGNITION USING SUPPORT VECTOR MACHINE

2011 ◽  
Vol 08 (01) ◽  
pp. 53-64
Author(s):  
K. MANIMALA ◽  
K. SELVI ◽  
R. AHILA
Keyword(s):  
Support Vector Machine ◽  
Signal Processing ◽  
Fourier Transform ◽  
Power Quality ◽  
Hilbert Transform ◽  
Noise Analysis ◽  
Support Vector ◽  
Classification Problems ◽  
Processing Techniques ◽  
Signal Processing Techniques

Recently, many signal processing techniques, such as fast Fourier transform, short-time Fourier transform, wavelet transform (WT), and wavelet packet transform (WPT), have been applied to detect, identify, and classify power quality (PQ) disturbances. For research on PQ analysis, it is critical to apply the appropriate signal processing techniques and classifier to solve PQ problems. The aim of this paper is to develop a classification method based on the combination of Hilbert transform (HT) and support vector machine (SVM) for the assessment of power quality events. Recent data mining literature has shown that support vector machine methods generally outperform traditional statistical and neural methods in classification problems involving power disturbance signals. The features obtained from the Hilbert transform are distinct, understandable and immune to noise. Analysis is presented to verify that the merits of HT and SVM combination make it adequate for PQ analysis when compared with the existing techniques in the literature.

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