A novel method called Impact-synchronous modal analysis (ISMA) was proposed previously which allows modal testing to be performed during operation. This technique focuses on signal processing of the upstream data to p...
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A novel method called Impact-synchronous modal analysis (ISMA) was proposed previously which allows modal testing to be performed during operation. This technique focuses on signal processing of the upstream data to provide cleaner frequencyresponsefunction (FRF) estimation prior to modal extraction. Two important parameters, i.e., windowing function and impact force level were identified and their effect on the effectiveness of this technique were experimentally investigated. When performing modal testing during running condition, the cyclic loads signals are dominant in the measured response for the entire time history. Exponential window is effectively in minimizing leakage and attenuating signals of non-synchronous running speed, its harmonics and noises to zero at the end of each time record window block. Besides, with the information of the calculated cyclic force, suitable amount of impact force to be applied on the system could be decided prior to performing ISMA. Maximum allowable impact force could be determined from nonlinearity test using coherence function. By applying higher impact forces than the cyclic loads along with an ideal decay rate in ISMA, harmonic reduction is significantly achieved in FRF estimation. Subsequently, the dynamic characteristics of the system are successfully extracted from a cleaner FRF and the results obtained are comparable with Experimental modal analysis (EMA).
This paper considers the applications of principal component analysis (PCA) for signal-based linear system identification. Linear time-invariant (LTI) single-input-single-output (SISO) and multi-input-multi-output (MI...
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This paper considers the applications of principal component analysis (PCA) for signal-based linear system identification. Linear time-invariant (LTI) single-input-single-output (SISO) and multi-input-multi-output (MIMO) system frequencyresponsefunction (FRF) estimation problems are formulated on the basis of the eigen-value decomposition (EVD) of the input-output measurement spectral correlation matrix. It is demonstrated that resulting algorithms for the SISO and MIMO cases are equivalent to that of the maximum likelihood (ML) and the total least squares (TLS) approaches respectively. Originating from the proposed FRF estimation scheme, a moving-segment EVD procedure is developed for SISO time-varying transfer functionestimation. Based on the sensitivity of the time-domain PCA to delays/shifts between signals, an extended lagged-covariance-matrix approach is introduced for delay detection from time series. (c) 2006 Elsevier Ltd. All rights reserved.
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