Coherence analysis of multichannel time series applying conditioned multivariate autoregressive spectra

Väätäjä, Heli; Suoranta, R.; Rantala, Seppo

doi:10.1109/icassp.1994.389801

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…In an early study, Akaike 3 presented one of the first computations of the power spectra from the autoregressive model. Since then, Vaataja and Suoranta 4 have continued to develop the multichannel spectra by introducing the spectral matrix for coherence analysis of multichannel time series. Quirk and Liu 5 have improved the resolution of the autoregressive spectrum by decimation, a method to expand the frequency scale through a factor in order to separate the neighbor frequencies and the spectral peaks.…”

Section: Introductionmentioning

confidence: 99%

Modal analysis of a light-weight robot with a rotating tool installed at the end effector

Liu

Thomas

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper investigates vibration of a moving flexible robot through modal analysis and by constructing vibration spectra of operational signals. A vector autoregressive model combined with a sliding window technique is used for signal processing in order to take into account system nonstationarity. Modal decomposition is conducted on the state matrix constructed from the appropriate vector autoregressive model parameters. A complete modal decomposition and spectrum construction algorithm able of highlighting the structural modes and harmonic excitations is presented. Through accurate identification from the vector autoregressive model, the method presented is able to discriminate, display and monitor the harmonics and structural modes during the processes investigated. This method is validated first by numerical simulation and then experimentally with a flexible robot performing three processes: moving a manipulator through the workspace, steady rotation of a grinder on the end effector and moving the manipulator combined with rotating the grinder. It is found on the operating robot that participation of the first structural mode is negligible when rotating the grinder but must be taken into account when moving the manipulator. The analysis presented and results obtained provide a sound basis for further investigation of vibroimpact behaviour in a robotic grinding process.

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Section: Introductionmentioning

confidence: 99%

Modal analysis of a light-weight robot with a rotating tool installed at the end effector

Liu

Thomas

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Akaike [3] early computed the power spectra through an autoregressive modelling. Since then, several techniques have been developed on the multichannel spectrum [4] or for improving the resolution of the spectra of the autoregressive models [5,6]. The most advanced developments on modal identification of non-stationary vibrations can be found in the recent collection on the identification of time varying structures and systems [7].…”

Section: Introductionmentioning

confidence: 99%

Spectrum construction for non stationary vibration: Application to a moving flexible robot

Thomas

Liu

et al. 2015

MATEC Web of Conferences

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Abstract. This paper presents a method for constructing spectrum in non-stationary vibration using time series vector autoregressive model. A modal classification criterion and a modal amplification factor are introduced based on the eigendecomposition of the block data matrix. The classical spectrum computation can be therefore modified to amplify only the physical modes and lighten the computation induced modes. In combination with a sliding window technique, it is shown that the method provides a clear, lightweight spectral representation of non stationary vibration. Application on the vibration signals measured from a moving flexible robot along a given trajectory shows the effectiveness and applicability of the method. The technique is integrated in the online modal surveillance software call STAR (Short Time AutoRegressive).

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COPD severity classification using principal component and cluster analysis on HRV parameters

Newandee

Reisman

Bartels

et al.

2003 IEEE 29th Annual Proceedings of Bioengineering Conference

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Coherence analysis of multichannel time series applying conditioned multivariate autoregressive spectra

Cited by 3 publications

References 8 publications

Modal analysis of a light-weight robot with a rotating tool installed at the end effector

Modal analysis of a light-weight robot with a rotating tool installed at the end effector

Spectrum construction for non stationary vibration: Application to a moving flexible robot

COPD severity classification using principal component and cluster analysis on HRV parameters

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