Gain and phase calibration of sensor arrays from ambient noise by cross-spectral measurements fitting

We address the problem of blind gain and phase calibration of a sensor array from ambient noise. The key motivation is to ease the calibration process by avoiding a complex procedure setup. We show that computing the sample covariance matrix in a diffuse field is sufficient to recover the complex gains. To do so, we formulate a non-convex least-square problem based on sample and model covariances. We propose to obtain a solution by low-rank matrix approximation, and two efficient proximal algorithms are derived accordingly. The first one solves the problem modified with a convex relaxation to guarantee that the solution is a global minimizer, and the second one directly solves the initial non-convex problem. We investigate the efficiency of the proposed algorithms by both numerical and experimental results according to different sensing configurations. These show that efficient calibration highly depends on how the measurements are correlated. That is, estimation is achieved more accurately when the field is spatially over-sampled.Comment: submitted to the Journal of the Acoustical Society of Americ

Blind identification using inverse Patch Transfer Function (iPTF) method

International audienceAn important object of vibration and noise reduction and control is to identify and localize the vibration and noise sources. Many inverse methods, like Nearfield Acoustic Holography, have been developed in acoustics in the last decades. Among others, the iPTF method, allows the reconstruction of the acoustic fields (pressure, velocity, intensity) directly on the vibrating structure surface even when it has a complex shape. In addition, measurements can be done in non-controlled acoustic environments. The concept of iPTF is based on the application of Green's identity on any closed virtual volume defined around the source. The reconstruction of sound source fields combines discrete acoustic measurements performed at accessible positions around the source with acoustic impedance matrices. In the present work, blind identification of the vibratory fields is proposed. The "blindness" has here two meanings:-the identification of the velocity field of a vibrating structure can be blind if obstacles mask parts of the structure to characterize.-the identification can be blind if the velocity field is the result of the combination of several unknown sources and if one wants to separate the contribution of each source. Some numerical and experimental results will be shown to illustrate both aspects of the blind identification

Repeated exponential sine sweeps for the autonomous estimation of nonlinearities and bootstrap assessment of uncertainties

Measurements on vibrating structures has been a topic of interest for decades. Vibrating structures are however generally assumed to behave linearly and in a noise-free environment, which is not the case in practice. This paper provides a methodology that allows for the autonomous estimation of nonlinearities and assessment of uncertainties by bootstrap on a given vibrating structure. Nonlinearities are estimated by means of a block-oriented nonlinear model approach based on parallel Hammerstein models and on exponential sine sweeps. Estimation uncertainties are simultaneously assessed using repetitions of the input signal (multi-sine sweeps) as the input of a bootstrap procedure. Mathematical foundations and a practical implementation of the method are discussed using an experimental example. The experiment chosen here consists in exciting a steel plate under various boundary conditions with exponential sine sweeps and at different levels in order to assess the evolution of nonlinearities and uncertainties over a wide range of frequencies and input amplitudes

Repeated exponential sine sweeps for the autonomous estimation of nonlinearities and bootstrap assessment of uncertainties

Systems and structures are generally assumed to behave linearly and in a noise-free environment. This is in practice not perfectly the case. First, nonlinear phenomena can appear and second, the presence of noise is unavoidable for all experimental measurements. Nonlinearities can be considered as a deterministic process in the sense that in the absence of noise the output signal depends only on the input signal. Noise is purely stochastic: in the absence of an input signal, the output signal is not null and cannot be predicted at any arbitrary instant. It turns out that these two issues are coupled: all the noise that is not correctly removed from the measurements could be misinterpreted as nonlinearities, and if nonlinearities are not accurately estimated, they will end up within the noise signal and information about the system under study will be lost. The underlying idea consists here in extracting the maximum of available linear and nonlinear deterministic information from measurements without misinterpreting noise

A multi-sine sweep method for the characterization of weak non-linearities ; plant noise and variability estimation.

Weak non-linearities in vibrating structures can be characterized by a signal-model approach based on cascade of Hammerstein models. The experiment consists in exciting a device with a sine sweep at different levels, in order to assess the evolutions of non linearities on a wide frequency range. A method developed previously, based on exponential sine sweep, is able to give an approximative identification of the Hammerstein models, but cannot make the distinction between nonlinear distortion and stationary plant noise. Therefore, this paper proposes improvements on the method that provide a more precise estimation of the Hammerstein models through the cancellation of the plant noise: it relies on the repetition of the signal on a certain amount of periods (multi-sine sweeps) and then on the consideration of the synchronous average out of the different periods from the resulting signal. Mathematical foundations and practical implementation of the method are discussed. The second main point of improvement concerning the study of the vibrating device is the use of the Bootstrap analysis. By considering some periods randomly chosen among the multisine sweep, one can study the variability of the experiments. The method becomes more robust

A multi-sine sweep method for the characterization of weak non-linearities ; plant noise and variability estimation.

Weak non-linearities in vibrating structures can be characterized by a signal-model approach based on cascade of Hammerstein models. The experiment consists in exciting a device with a sine sweep at different levels, in order to assess the evolutions of non linearities on a wide frequency range. A method developed previously, based on exponential sine sweep, is able to give an approximative identification of the Hammerstein models, but cannot make the distinction between nonlinear distortion and stationary plant noise. Therefore, this paper proposes improvements on the method that provide a more precise estimation of the Hammerstein models through the cancellation of the plant noise: it relies on the repetition of the signal on a certain amount of periods (multi-sine sweeps) and then on the consideration of the synchronous average out of the different periods from the resulting signal. Mathematical foundations and practical implementation of the method are discussed. The second main point of improvement concerning the study of the vibrating device is the use of the Bootstrap analysis. By considering some periods randomly chosen among the multisine sweep, one can study the variability of the experiments. The method becomes more robust

New Separation Techniques for Output-Only Modal Analysis

The paper is devoted to the problem of discriminating between operational and natural modes of structures excited by generic inputs. This case often occurs when the system under analysis holds rotating parts and is contemporary excited by ambient noise; in this case the output-only techniques may fail being easily trapped in a misinterpretation of the system eigenvalues. A survey of the methods available in literature is given, together with the explanation of their failures. To solve this problem, two new techniques are introduced and their capabilities are checked with numerical and experimental data from a paper machine

L'imagerie acoustique appliquée au diagnostic et à la détection de défauts sur machine tournante

La détection de défauts sur les machines tournantes est usuellement apparentée à l'analyse de signaux vibratoires. Néanmoins, la corrélation entre le bruit émis par une machine et ses défections est assez étroite et montre l'apport des mesures acoustiques pour l'optimisation du diagnostic. L'imagerie acoustique, très utilisée pour détecter des sources dans le domaine du transport, peut être un moyen de remonter aux défauts mécaniques. Les méthodes de formation de voies (beamforming) et d'holographie acoustique en champ proche sont ici utilisées pour détecter un défaut sur une machine tournante

New Separation Techniques for Output-Only Modal Analysis

The paper is devoted to the problem of discriminating between operational and natural modes of structures excited by generic inputs. This case often occurs when the system under analysis holds rotating parts and is contemporary excited by ambient noise; in this case the output-only techniques may fail being easily trapped in a misinterpretation of the system eigenvalues. A survey of the methods available in literature is given, together with the explanation of their failures. To solve this problem, two new techniques are introduced and their capabilities are checked with numerical and experimental data from a paper machine

Limiting distributions for explosive PAR(1) time series with strongly mixing innovation

This work deals with the limiting distribution of the least squares estimators of the coefficients a r of an explosive periodic autoregressive of order 1 (PAR(1)) time series X r = a r X r--1 +u r when the innovation {u k } is strongly mixing. More precisely {a r } is a periodic sequence of real numbers with period P \textgreater{} 0 and such that P r=1 |a r | \textgreater{} 1. The time series {u r } is periodically distributed with the same period P and satisfies the strong mixing property, so the random variables u r can be correlated