scholarly journals Application of Time-Frequency Analysis to Transient Data from Centrifuge Earthquake Testing

2000 ◽  
Vol 7 (4) ◽  
pp. 195-202 ◽  
Author(s):  
David E. Newland ◽  
Gary D. Butler
Keyword(s):  
Frequency Analysis ◽  
Good Method ◽  
Centrifuge Modeling ◽  
Transient Vibration ◽  
Time Frequency Analysis ◽  
Harmonic Wavelets ◽  
Time Frequency ◽  
Vibration Data ◽  
Harmonic Wavelet ◽  
Detailed Interpretation

Centrifuge model experiments have generated complex transient vibration data. New algorithms for time-frequency analysis using harmonic wavelets provide a good method of analyzing these data. We describe how the experimental data have been collected and show typical time-frequency maps obtained by the harmonic wavelet algorithm. Some preliminary comments on the interpretation of these maps are given in terms of the physics of the underlying model. Important features of the motion that are not otherwise apparent emerge from the analysis. Later papers will deal with their more detailed interpretation and their implications for centrifuge modeling.

scholarly journals Time-Frequency Analysis of Nonlinear Systems: The Skeleton Linear Model and the Skeleton Curves

2003 ◽  
Vol 125 (2) ◽  
pp. 170-177 ◽  
Author(s):  
Lili Wang ◽  
Jinghui Zhang ◽  
Chao Wang ◽  
Shiyue Hu
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Linear Model ◽  
Frequency Analysis ◽  
Nonlinear Behavior ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Vibration Data ◽  
Identification Technique ◽  
Stiffness And Damping

The joint time-frequency analysis method is adopted to study the nonlinear behavior varying with the instantaneous response for a class of S.D.O.F nonlinear system. A time-frequency masking operator, together with the conception of effective time-frequency region of the asymptotic signal are defined here. Based on these mathematical foundations, a so-called skeleton linear model (SLM) is constructed which has similar nonlinear characteristics with the nonlinear system. Two skeleton curves are deduced which can indicate the stiffness and damping in the nonlinear system. The relationship between the SLM and the nonlinear system, both parameters and solutions, is clarified. Based on this work a new identification technique of nonlinear systems using the nonstationary vibration data will be proposed through time-frequency filtering technique and wavelet transform in the following paper.

Time-Frequency Analysis of Transient Vibrations to Improve Turbo-Compressor Start-Up

2008 ◽  
Author(s):  
Juan C. Jauregui ◽  
Oscar Gonzalez ◽  
Eduardo Rubio
Keyword(s):  
Frequency Analysis ◽  
Fourier Transforms ◽  
Transient Vibration ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Start Up ◽  
Rotational Systems ◽  
Turbo Compressor ◽  
Different Sources

Diagnosis of turbo-compressors during start-up is a particularly challenging task. One of the reason is the reduced set of instruments that monitor this procedure. It is cumbersome to adjust lubrication and steam valves while controlling the speed and dynamic stability. In order to get the turbo-compressor out of a high vibration zone, it is important to be able to predict instabilities associated to the start-up process. Thus, it is necessary to have a measurement system with the ability of fault detection, especially at early stages of fault appearance. In this way, the start-up time can be significantly reduced. Although recent developed diagnosis methods use information from different sources and measurements, data structures are not designed to carry predictive information related to the turbo-compressor health. Therefore, it is important to extract early warning signals related to instability conditions. Vibration signals during machine start-up are non-stationary in nature, and conventional techniques, such as Fourier transforms and time series analysis, have difficulties to extract the full features of the vibrations signature. In this paper, the features of start-up vibrations in rotational systems like those found in turbo compressors are investigated by time-frequency analysis, and appropriate analysis of the transient vibration during compressor start-up is presented.

Extracting of Micro Doppler Parameter of Helicopter Rotor Based on Time-Frequency Analysis

2011 ◽  
Vol 130-134 ◽  
pp. 2696-2700 ◽  
Author(s):  
Lei Zhang ◽  
Guo Qing Huang
Keyword(s):  
Frequency Domain ◽  
Frequency Analysis ◽  
Time Domain ◽  
Good Method ◽  
Helicopter Rotor ◽  
Echo Signal ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Radar Echo ◽  
Doppler Parameter

The micro Doppler effect of the radar echo signal of helicopter rotor is studied, and the formula of helicopter rotor echo is obtained. Then the received echo signal of helicopter rotor simulated is analyzed in time domain, frequency domain and time-frequency domain respectively, the analysis results show that it is a good method to extract micro Doppler of helicopter rotor echo by time-frequency analysis. According to analysis results, obtained a method to determine parity of blades and velocity of helicopter rotor, these methods can be used to identify helicopter.

A Novelty Detection Approach to Compressor Valve Fault Detection and Localization

2020 ◽  
Author(s):  
Colton M. Scott ◽  
Jason R. Kolodziej
Keyword(s):  
Frequency Analysis ◽  
Learning Algorithm ◽  
Novelty Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Flow Profile ◽  
Shape Factors ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Vibration Data

Abstract Presented in this paper is the development of a vibration-based novelty detection algorithm for locating and identifying valve wear within industrial reciprocating compressors through the combined use of time-frequency analysis, image-based pattern recognition, and one-class support vector machines. A commonly reported cause of valve wear-related machine downtime is wear in the valve seat, causing a change in flow profile into and out of the compression chamber. Seeded faults are introduced into the valve manifolds of the ESH-1 industrial compressor and vibration data collected and separated into individual crank cycles before being analyzed using time-frequency analysis. The result is processed as an image and features used for classification are extracted using 1st and 2nd order images statistics and shape factors. A one-class support vector machine learning algorithm is then trained using data collected during healthy operation and then used to both detect and locate anomalous valve behavior with a greater than 82% success rate.

Ridge and Phase Identification in the Frequency Analysis of Transient Signals by Harmonic Wavelets

1999 ◽  
Vol 121 (2) ◽  
pp. 149-155 ◽  
Author(s):  
D. E. Newland
Keyword(s):  
Frequency Analysis ◽  
Phase Identification ◽  
Computational Algorithms ◽  
High Definition ◽  
Diagnostic Features ◽  
Transient Signals ◽  
Phase Gradient ◽  
Harmonic Wavelets ◽  
Time Frequency ◽  
Harmonic Wavelet

It is difficult to generate high-definition time-frequency maps for rapidly changing transient signals. New details of the theory of harmonic wavelet analysis are described which provide the basis for computational algorithms designed to improve map definition. Features of these algorithms include the use of ridge identification and phase gradient as diagnostic features.

Joint Time-Frequency Analysis of Small Scale Ocean Storms via the Harmonic Wavelet Transform

2017 ◽  
Author(s):  
Valentina Laface ◽  
Felice Arena ◽  
Ioannis A. Kougioumtzoglou ◽  
Ketson Roberto Maximiano dos Santos
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Sea Surface ◽  
Surface Elevation ◽  
Scale Model ◽  
Small Scale ◽  
Ocean Engineering ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Harmonic Wavelet

The paper focuses on utilizing the Harmonic Wavelet Transform (HWT) for estimating the evolutionary power spectrum (EPS) of sea storms. A sea storm is considered herein as a non-stationary stochastic process with a time duration of the order of days. The storm evolution can be represented in three stages: the growth, the peak and the decay. Specifically, during growth the intensity of the wave increases with time until reaching the apex, and then decreases. The analysis is carried out by processing the time series of the free surface elevation recorded at the Natural Ocean Engineering Laboratory of Reggio Calabria, Italy. A peculiarity of the NOEL lab is that a local wind from NNW often generates sea states consisting of pure wind waves that represent a small scale model, in Froude similarity, of ocean storms (www.noel.unirc.it). The main focus of the paper is, first, to acquire a joint time-frequency representation of the storm via estimating the associated EPS, and second, to explore the variability in time of the spectrum and of the dominant frequencies of the storm. The EPS is estimated by utilizing a non-stationary record of the sea surface elevation during a storm recorded at NOEL lab. Further, in this paper, the standard representation of sea storms is also considered. That is, the non-stationary process is represented as a sequence of stationary processes (sea states or buoy records), each of them characterized by an intensity defined by a significant wave height Hs and by a duration Δt. During the time interval Δt the sea surface elevation is considered stationary and the frequency spectrum may be computed via the Fast Fourier Transform (FFT). Results obtained following this procedure, which can be considered essentially as a brute-force application of the short-time FT, are compared with those obtained via a HWT based joint time-frequency analysis.

Sign in / Sign up

Export Citation Format

Share Document