Additive regularizations, such as Tikhonov-like approaches, are certainly the most popular methods for reconstructing forces acting on a structure. These approaches require, however, the knowledge of a regularization parameter, that can be numerically computed using specific procedures. Unfortunately, these procedures are generally computationally intensive. For this particular reason, it could be of primary interest to propose a method able to proceed without defining any regularization parameter beforehand. In this paper, a multiplicative regularization is introduced for this purpose. By construction, the regularized solution has to be calculated in an iterative manner. In doing so, the amount of regularization is automatically adjusted throughout the resolution process. Validations using synthetic and experimental data highlight the ability of the proposed approach in providing consistent reconstructions.

a b s t r a c tThis paper is concerned with the development of a general methodology for identifying mechanical sources from prior local information on both their nature and location over the studied structure. For this purpose, the formulation of the identification problem is derived from the Bayesian statistics, that provides a flexible way to account for local a priori on the distribution of sources. Practically, the resulting optimization problem can be seen as a group generalized Tikhonov regularization, that is solved in an iterative manner. The main features of the proposed identification method are illustrated with both numerical and experimental examples. In particular, it is shown that properly exploiting the local spatial information drastically improves the quality of the source identification.

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Dynamic forces reconstruction from vibration data is an ill-posed inverse problem. A standard approach to stabilize the reconstruction consists in using some prior information on the quantities to identify. This is generally done by including in the formulation of the inverse problem a regularization term as an additive or a multiplicative constraint. In the present article, a space-frequency multiplicative regularization is developed to identify mechanical forces acting on a structure. The proposed regularization strategy takes advantage of one's prior knowledge of the nature and the location of excitation sources, as well as that of their spectral contents. Furthermore, it has the merit to be free from the preliminary definition of any regularization parameter. The validity of the proposed regularization procedure is assessed numerically and experimentally. It is more particularly pointed out that properly exploiting the space-frequency characteristics of the excitation field to identify can improve the quality of the force reconstruction.

To cite this version:M. Aucejo, Olivier de Smet. On a full Bayesian inference for force reconstruction problems. Mechanical Systems and Signal Processing, Elsevier, 2018, 104, pp. AbstractIn a previous paper, the authors introduced a flexible methodology for reconstructing mechanical sources in the frequency domain from prior local information on both their nature and location over a linear and time invariant structure. The proposed approach was derived from Bayesian statistics, because of its ability in mathematically accounting for experimenter's prior knowledge. However, since only the Maximum a Posteriori estimate was computed, the posterior uncertainty about the regularized solution given the measured vibration field, the mechanical model and the regularization parameter was not assessed. To answer this legitimate question, this paper fully exploits the Bayesian framework to provide, from a Markov Chain Monte Carlo algorithm, credible intervals and other statistical measures (mean, median, mode) for all the parameters of the force reconstruction problem.

This paper proposes a formal representation of logic controllers programs that is aiming at improving scalability of model-checking techniques, when verifying controllers extrinsic properties. This representation includes only the states which are meaningful for properties proof and minimizes the number of variables that feature each state. Comparison with previously proposed representations, on the basis of three increasing complexity examples, validates this representation and quantifies its efficiency.

Abstract-This paper addresses scalability of model-checking using the NuSMV model-checker. To avoid or at least limit combinatory explosion, an efficient representation of PLC programs is proposed. This representation includes only the states that are meaningful for properties proof. A method to translate PLC programs developed in Structured Text into NuSMV models based on this representation is described and exemplified on several examples. The results, state space size and verification time, obtained with models constructed using this method are compared to those obtained with previously published methods so as to assess efficiency of the proposed representation.

Kalman-type filtering tends to become one of the favorite approaches for solving joint input-state estimation problems in the structural dynamics community. This article focuses on the applicability of the Augmented Kalman Filter (AKF) for reconstructing mechanical sources, addressing a set of practical issues that are frequently encountered in the engineering practice. In particular, this paper aims to help the reader to better apprehend some of the advantages and limitations of the application of the AKF in the context of purely input estimation problems. The present paper is not a simple collection of test cases, since it introduces a novel state-space representation of dynamical systems, based on the generalized-α method, as well as further insights in the tuning of Kalman filters from the Bayesian perspective. In this work, the various practical situations considered lead us to recommend to employ collocated acceleration measurements, when reconstructing excitation sources from the AKF. It is also demonstrated that the violation of some of the feasibility conditions proposed in the literature doesn't necessar