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Chris K. Mechefske - One of the best experts on this subject based on the ideXlab platform.
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Adaptive modelling of Transient Vibration signals
Mechanical Systems and Signal Processing, 2006Co-Authors: Fenglin Wang, Chris K. MechefskeAbstract:Abstract Conventional Vibration signal processing techniques are most suitable for stationary processes. However, most mechanical faults in machinery reveal themselves through Transient events in Vibration signals. Time-series modelling, including autoregressive moving average (ARMA) modelling and autoregressive (AR) modelling, is an efficient approach for Transient signal analysis. Based on the adaptive prediction technique, this paper applies the principle of the adaptive line enhancer (ALE) to the modelling of Transient Vibration signals. The time-series models, adaptive algorithms and the rational time–frequency transfer function are investigated in the paper. Simulation and experimental studies with different time–frequency–amplitude distributions and Transient Vibration responses are described. The results show that the adaptive modelling method can trace the time–frequency signal and extract dynamic features such as time–frequency distributions and time–amplitude distributions from sample signals. Given the simple programming and potentially easy implementation in on-line applications, this method should have application in machine monitoring and fault diagnosis.
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Diagnosis of Machinery Fault Status using Transient Vibration Signal Parameters
Journal of Vibration and Control, 2002Co-Authors: Zhidong Chen, Chris K. MechefskeAbstract:This paper reports the results of an investigation in which a Prony model based method is developed. The method shows potential for analysing Transient Vibration signals. An example is included that shows how the procedure was employed to analyse the Transient Vibration signals created from faulty low speed rolling element bearings. Spectral plots generated by applying the procedure to very short data samples, as well as trending parameters based on these spectral estimations and Prony parameters, are presented. An equation was also derived to quantitatively determine the fault status. It is shown that application of the Prony model based method has the potential to be an effective as well as efficient machine condition monitoring and diagnostic tool where short duration Transient Vibration signals are being generated.
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Machine Condition Monitoring Based on Transient Vibration Signal Analysis
International Journal of Acoustics and Vibration, 2000Co-Authors: Zhidong Chen, Chris K. MechefskeAbstract:Over the past several decades a significant amount of research activity has been devoted to Machine Signature Analysis (MSA). The major tool used for MSA has been spectral analysis. Classical spectral estimation techniques include Periodogram, Averaged Periodogram and BlackmanTukey spectral estimation. Newer approaches to spectral analysis include a variety of Parametric modeling techniques. Within this category are the Rational Transfer Function Modelling Method, Autoregressive (AR) Power Spectral Density (PSD) Estimation, Moving Average (MA) PSD Estimation, Autoregressive Moving Average (ARMA) PSD Estimation, Prony Spectral Density Estimation and Maximum Likelihood Method (MLM). The majority of the research and development work carried out to date with regard to signal processing strategies for machine condition monitoring and diagnostics applications has focused on signals generated from stationary processes. Non-stationary processes have been left relatively unstudied. Examples of methods that are applicable to non-stationary processes are the Wavelet and the Short Time Fast Fourier Transforms. The Prony method, originated by the French scientist Baron de Prony in 1795, is also capable of analysing non-stationary processes and is inherently suitable for the study of exponentially decaying dynamic signals, such as those that develop as a result of many different types of machinery condition deterioration, for instance, induction motor current waveforms, power transformer tank Vibrations and fluorescence decay behaviour for optical temperature sensing. This paper explores the use of the Prony method to study such Transient signals. The Prony method was originally used for interpolation between available data points. The method and its modified versions are techniques used for modelling data of equally spaced data samples. The Prony model is similar to the wellknown system identification techniques such as the AR and ARMA models. The Prony model seeks to fit an exponential model, which is a linear combination of a series of exponential functions, to sampled data. The Prony method first determines the linear prediction parameters that fit the sampled data. Such linear prediction parameters are then used as coefficients to form a polynomial. The roots of this polynomial are finally employed to estimate the damping coefficients, the sinusoidal frequencies, the exponential amplitude and the sinusoidal initial phase of each of the exponential terms.
Zhongwei Jiang - One of the best experts on this subject based on the ideXlab platform.
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frequency slice wavelet transform for Transient Vibration response analysis
Mechanical Systems and Signal Processing, 2009Co-Authors: Ayaho Miyamoto, Zhongwei JiangAbstract:Abstract This paper introduces a new kind of time–frequency signal analysis method, called frequency slice wavelet transform (FSWT), by means of extension of short-time Fourier transform (STFT) defined directly in frequency domain. The original signal can be decomposed by frequency slice function (FSF), which is similar with the wavelet base but can be designed very freely. At the same time, the original signal can be reconstructed by a FSWT representation in an easy way without the strict limitation of wavelet theory. Some new characteristics of its time–frequency window will be shown. Due to these features, FSWT is more flexible to fit ever-changing signals, and convenient to analyze and control in application. Next, the frequency resolution ratio of signal and Dirac function, etc., are employed to study FSWT, and to select a new scale parameter. The new scale is a good balance factor between time and frequency resolution. Moreover a fast discrete algorithm of FSWT is completed. Its application is focused on Transient Vibration signal analysis in this paper. FSWT can not only individually represent each modal signal in frequency domain, but also correctly show its details in time domain. FSWT helps to discover some new features of the experimental signal obtained from a small laboratory bridge monitoring system. By using FSWT, the filtering under high noise, and the segmenting of signal with high damping and close modes of frequency, will be discussed. Finally, the summary shows that this paper will be able to provide a more available tool for signal analyzing simultaneously in time–frequency domain, and further to refine the wavelet theory.
Cheuk Ming Mak - One of the best experts on this subject based on the ideXlab platform.
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A Review of Prediction Methods for the Transient Vibration and Sound Radiation of Plates
Journal of Low Frequency Noise Vibration and Active Control, 2013Co-Authors: Cheuk Ming MakAbstract:Transient noise is a typical real-life noise problem. In order to develop efficient methods of dealing with it, the generating and transmitting mechanisms of Transient noise radiating from a vibrating structure have been widely studied. The analytical methods used differ according to the properties of the excitation, structure, and acoustic environment. Focusing on plate-like structures, this paper provides an overview of commonly-used approaches to the analysis of structural Transient Vibration and sound radiation (TVSR). A general process for solving this type of problem is presented, together with suggestions for future work in this field.
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An indicator for the assessment of isolation performance of Transient Vibration
Journal of Vibration and Control, 2012Co-Authors: Junfang Wang, Cheuk Ming MakAbstract:It is known that the commonly used performance indicator for Vibration isolation − force transmissibility − over-simplifies the Vibration problem. Therefore, Mak and Su propose a power transmissibility approach that includes the effect of floor dynamics and the interactions of the mounting points between machine and floor. However, their model does not consider Transient Vibration excitation. The question motivating this study is the occasional problem which arises due to the sudden or frequent starting and stopping of vibratory machines. In this paper, a Transient power transmissibility approach is proposed to assess the performance of isolators in a Transient Vibration excitation. A spring-mass-movable floor system is considered in the simulation, and the spring isolator is first selected using the steady-state power transmissibility approach of Mak and Su. A system disturbed by two Transient excitations as typically experienced by building services equipment is then analyzed. The results indicate the n...
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Transient Vibration and sound radiation of a stiffened plate
Journal of Vibration and Control, 2012Co-Authors: Cheuk Ming MakAbstract:The Transient Vibration and sound radiation (TVSR) of a stiffened plate was investigated using the time-domain finite element method and time-domain boundary element method. A four-noded stiffened-...
Nga Hoang - One of the best experts on this subject based on the ideXlab platform.
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an adaptive tunable Vibration absorber using a new magnetorheological elastomer for vehicular powertrain Transient Vibration reduction
Smart Materials and Structures, 2011Co-Authors: Nga Hoang, Nong Zhang, Haiping DuAbstract:During the Transient stage of acceleration, the powertrain experiences a period of high level Vibration because the engine speed passes through one or several powertrain natural frequencies. This paper presents a concept design of an adaptive tuned Vibration absorber (ATVA) using a new magnetorheological elastomer (MRE) for powertrain Transient Vibration reduction. The MRE material used to develop the ATVA is a new one, which is synthesized from a highly elastic polymer and carbonyl iron particles of 3–5 and 40–50 µm. Under a magnetic field of 0.3 T, the MRE material has a giant increase, which is more than two orders, in both the storage and loss moduli. To facilitate the ATVA design, effective formulae for the storage modulus and loss factor were derived as explicit functions of the applied magnetic field density. With the derived formulae, ATVA parameters such as the stiffness and damping coefficients were converted effectively from the magnetic field density. Thus, the ATVA frequency can be tuned properly according to the excitation frequency. Numerical simulations of a powertrain system fitted with the ATVA were conducted to examine the ATVA proposed design. By using the MRE-based ATVA, the powertrain natural frequencies can be actively tuned far away from the resonant area of excitation frequency. Also, the time histories of powertrain frequencies depending on the magnetic field density before and after installing the ATVA have been compared to show that the resonant phenomena have been dealt with completely. As a result, the powertrain Transient Vibration response is significantly suppressed. In addition, the effect of the ATVA's moment of inertia, stiffness and damping on the ATVA's effectiveness during the Transient stage was investigated to choose the ATVA's optimal parameters. The MRE-based ATVA will be a novel device for powertrain Vibration control not only for the steady stage but also for Transient Vibration.
Zhidong Chen - One of the best experts on this subject based on the ideXlab platform.
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Diagnosis of Machinery Fault Status using Transient Vibration Signal Parameters
Journal of Vibration and Control, 2002Co-Authors: Zhidong Chen, Chris K. MechefskeAbstract:This paper reports the results of an investigation in which a Prony model based method is developed. The method shows potential for analysing Transient Vibration signals. An example is included that shows how the procedure was employed to analyse the Transient Vibration signals created from faulty low speed rolling element bearings. Spectral plots generated by applying the procedure to very short data samples, as well as trending parameters based on these spectral estimations and Prony parameters, are presented. An equation was also derived to quantitatively determine the fault status. It is shown that application of the Prony model based method has the potential to be an effective as well as efficient machine condition monitoring and diagnostic tool where short duration Transient Vibration signals are being generated.
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Machine Condition Monitoring Based on Transient Vibration Signal Analysis
International Journal of Acoustics and Vibration, 2000Co-Authors: Zhidong Chen, Chris K. MechefskeAbstract:Over the past several decades a significant amount of research activity has been devoted to Machine Signature Analysis (MSA). The major tool used for MSA has been spectral analysis. Classical spectral estimation techniques include Periodogram, Averaged Periodogram and BlackmanTukey spectral estimation. Newer approaches to spectral analysis include a variety of Parametric modeling techniques. Within this category are the Rational Transfer Function Modelling Method, Autoregressive (AR) Power Spectral Density (PSD) Estimation, Moving Average (MA) PSD Estimation, Autoregressive Moving Average (ARMA) PSD Estimation, Prony Spectral Density Estimation and Maximum Likelihood Method (MLM). The majority of the research and development work carried out to date with regard to signal processing strategies for machine condition monitoring and diagnostics applications has focused on signals generated from stationary processes. Non-stationary processes have been left relatively unstudied. Examples of methods that are applicable to non-stationary processes are the Wavelet and the Short Time Fast Fourier Transforms. The Prony method, originated by the French scientist Baron de Prony in 1795, is also capable of analysing non-stationary processes and is inherently suitable for the study of exponentially decaying dynamic signals, such as those that develop as a result of many different types of machinery condition deterioration, for instance, induction motor current waveforms, power transformer tank Vibrations and fluorescence decay behaviour for optical temperature sensing. This paper explores the use of the Prony method to study such Transient signals. The Prony method was originally used for interpolation between available data points. The method and its modified versions are techniques used for modelling data of equally spaced data samples. The Prony model is similar to the wellknown system identification techniques such as the AR and ARMA models. The Prony model seeks to fit an exponential model, which is a linear combination of a series of exponential functions, to sampled data. The Prony method first determines the linear prediction parameters that fit the sampled data. Such linear prediction parameters are then used as coefficients to form a polynomial. The roots of this polynomial are finally employed to estimate the damping coefficients, the sinusoidal frequencies, the exponential amplitude and the sinusoidal initial phase of each of the exponential terms.