Arma Models for Modal Analysis: Effect of Model Orders and Sampling Frequency

Smail, Mostafa Kamel; Thomas, Marc; Lakis, A. A.

doi:10.1006/mssp.1999.1235

Cited by 20 publications

(9 citation statements)

References 8 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to better illustrate the inner workings of this proposed method, a sample three-degree-of-freedom (3DOF) mixed system was created using undamped sinusoidal oscillators. The general form of the source signals for this system are shown in Equation (13).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…OMA may be performed in the state–space domain using methods based on stochastic subspace identification . In addition, other well‐known methods for OMA such as Hilbert transforms , the natural excitation technique , the eigensystem realization algorithm , and auto‐regressive and auto‐regressive moving average models are used for modal parameter identification.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Increasing the efficiency and efficacy of second-order blind identification (SOBI) methods

Brewick

Smyth

2016

Struct. Control Health Monit.

View full text Add to dashboard Cite

SUMMARYThis paper proposes a new technique to increase the efficiency and effectiveness of second-order blind identification (SOBI) methods by reducing the number of time-lagged covariance matrices required to produce highly accurate mixing matrix estimates. The technique is based on randomly selecting the time-lagged covariance matrices as opposed to choosing them sequentially, which takes advantage of a property of independence with regard to the selection of time-lagged covariance matrices, while simultaneously reducing the correlation between selected covariance matrices. The proposed randomized approach is first applied to undamped and damped sinusoids to demonstrate its effectiveness. The randomized method is then applied to a three-degree-of-freedom linear structure subjected to random excitation. Finally, the proposed method is incorporated into a modified version of the SOBI method and applied to perform identification of the UCLA Factor building from recorded earthquake responses. In each case, the performance of the randomized approach is compared with the traditional sequential selection of time-lagged covariance matrices, and the randomized approach consistently demonstrates superior performance both in terms of accuracy and efficiency. Additionally, the proposed randomized SOBI approach was easily inserted into the modified SOBI method demonstrating its malleability for SOBI variants and other blind source separation methods.

show abstract

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Increasing the efficiency and efficacy of second-order blind identification (SOBI) methods

Brewick

Smyth

2016

Struct. Control Health Monit.

View full text Add to dashboard Cite

show abstract

“…The AR model spectra are first constructed in Figure 4 at order 3 using four well-known methods: Yule, Burg, covariance and modified covariance. [14][15][16][17] At such a low model order, none of those methods are able to reveal all three peaks. Figure 5 shows the spectrum achieved by the proposed method, again at order 3.…”

Section: Modal and Spectral Analysis By Var Modelmentioning

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:

View full text Add to dashboard Cite

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.

show abstract

“…The spectra of the AR model are first constructed on Fig. 4 at the order 3 by using some well-known methods such as the Yule, Burg, Covariance and Modified Covariance [11][12][13][14]. It is seen that at low model order, none of those methods are able to show all the three peaks.…”

Section: -P3mentioning

confidence: 99%

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

Thomas

Liu

et al. 2015

MATEC Web of Conferences

View full text Add to dashboard Cite

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).

show abstract

Arma Models for Modal Analysis: Effect of Model Orders and Sampling Frequency

Cited by 20 publications

References 8 publications

Increasing the efficiency and efficacy of second-order blind identification (SOBI) methods

Increasing the efficiency and efficacy of second-order blind identification (SOBI) methods

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

Contact Info

Product

Resources

About