Alexandre Girard

A Source Separation Technique for Processing of Thermometric Data From Fiber-Optic DTS Measurements for Water Leakage Identification in Dikes

Distributed temperature sensors (DTSs) show real advantages over conventional temperature sensing technology such as low cost for long-range measurement, durability, stability, insensitivity to external perturbations, etc. They are particularly interesting for long-term health assessment of civil engineering structures such as dikes. In this paper, we address the problem of identification of leakage in dikes based on real thermometric data recorded by DTS. Formulating this task as a source separation problem, we propose a methodology based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). As the first PCA estimated source extracts an energetic subspace, other PCA sources allow to access the leakages. The energy of a leakage being very low compared to the entire data, a temporal windowing approach guarantees the presence of the leakages on these other PCA sources. However, on these sources, the leakages are not well separated from other factors like drains. An ICA processing, providing independent sources, is thus proposed to achieve better identification of the leakages. The study of different preprocessing steps such as normalization, spatial gradient, and transposition allows to propose a final scheme that represents a first step towards the automation of the leakage identification problem.

Automatic Monitoring System for Singularity Detection in Dikes By DTS Data Measurement

The development of automated monitoring systems for the detection of singularities, such as leakages in dikes, is indispensable to avoid mass disaster. An efficient solution for dike survey is the use of distributed temperature sensors (DTSs) based on optical fiber, offering a multitude of advantages such as low cost, extreme robustness, long-range measurement, etc. However, the temperature data acquired with DTSs, being not directly interpretable, require intervention of signal processing techniques. This paper addresses this signal processing aspect, exploiting the key idea that the temperature variations over the course of a day for singular zones are quite different from those for nonsingular zones. A daily reference temperature variation, which is representative of the nonsingular zones, is estimated using singular value decomposition (SVD). The residue subspace of SVD contains information linked to the deviations from this reference, thus allowing the degree of singularity to be quantified by a dissimilarity measure such as the L2-norm. To detect only the singularities in dikes, such as leakages or drains, a constant false alarm rate (CFAR) detector is proposed by modeling each daily dissimilarity measure with a mixture of Gamma and uniform distributions. The proposed automatic singularity detection system was validated under different scenarios on real data over periods from 2005 to 2007. The first scenario depicted the detection of percolation-type artificial leakages with their detection strength depending on their flow rates. Another scenario allowed detecting the presence of a real water leakage at the site, which was previously unobserved during manual inspections. The repeatability of the system was also verified by periodic analysis.

Detection of Ground Movement using the Shape of Brillouin Spectrum

Distributed Optical Fiber Sensing systems (DOFSS) are composed by optical fibers wrapped in strain sensing cables, coupled with Brillouin interrogators. DOFSS are increasingly used for Structural Health Monitoring (SHM) as they can provide continuous strain profiles along the optical fiber localized in the structure. Raw Brillouin measurements consist in gain – frequency curves with a Lorentzian shape. Strain is generally assessed thanks to the abscissa of the maximum of the gain curve. Two new factors are introduced. They are sensitive to asymmetry and broadening of the Brillouin gain curve which can highlight strain gradient within the spatial resolution of the interrogator. These parameters could be used to detect more efficiently local events and improve instrument algorithm.

Estimation of SG TSP Blockage: Innovative Monitoring Through Dynamic Behavior Analysis

EDF operates a fleet of 58 Pressurized Water Reactors (PWR). The “health” of the Steam Generators (SGs) is an essential element contributing to the overall thermal efficiency of a PWR, and finally to the availability of the unit. Among the health issues that may affect SGs, secondary-side corrosion products transport in PWRs may lead to many problems: various contaminants, both particulates and dissolved species, will unavoidably accumulate and concentrate in the Steam Generator. One consequence is the fouling of the heat transfer and support structure interfaces within the SG on the secondary side, especially the U-tubes (fouling deposits on the outer walls of the U-tubes), and the tube support plates (TSPs) that support the U-tubes. The accumulation of the corrosion products may lead to 3 main safety risks that must be monitored: fluid-elastic instability of tubes in flow-accelerated areas, a reduction in SG water mass inventory and an increase in the risk of water level oscillation. It has also significant performance issues because of the decision to power derate of some EDF PWRs. Thus, a global strategy to monitor the fouling and TSP blockage issues and to schedule preventive and curative actions has been designed and is under deployment by EDF nuclear operator. This dedicated periodic test relies on the recording of the following measurements in stabilized configuration: steam pressure, feedwater flowrate and temperature, primary circuit temperatures, SG blowdown flowrate and SG water level (wide and narrow range). A more precise monitoring of potential TSP blockage situations would be an interesting help to operation and maintenance strategies: deposit build-up in TSP foils could be minimized, preventive chemical cleaning operations could be scheduled and a more efficient fleet wide SG Management Program (SGMP) could be designed in accordance with secondary side deposit issues. Consequently, EDF R&D is experimenting a new method based on modeling dynamics behavior of SGs to assess a spatially distributed estimator of the TSP blockage ratio. This method, based on a 1D physical model of the SG that simulates the complex dynamics of the two-phase flow phenomena inside the SG, consists in computing the wide range water level responses according to various configurations during a particular transient which is particularly sensitive to this phenomenon. The TSP blockage ratio estimator is then obtained by comparing the computed response curves to those measured on-site. This new method has the potential advantages of being fully non-invasive, of providing a quarterly update of the TSPs blockage estimator, and of requiring no additional measurements by processing available plant data. It is also capable of estimating the efficiency of a chemical cleaning after restarting the plant and checking the evolution and kinetics of eventual TSP re-blockage.© 2010 ASME

Multi-lag Phase Diagram Analysis for Transient Signal Characterization

Phase diagram analysis is a potential technique that can offer interesting information regarding the signal shapes and eventually transient signal characterization. Indeed by choosing wisely the lag in phase diagram representations, it is possible to highlight mathematical properties such as time-shift and time-scale operators, as well as amplitude modifications. Therefore, this chapter develops the concept of multi-lag phase diagram analysis (MLPDA), as well as different methods aimed to extract parsimonious parameters from signal’s phase diagrams calculated for different values of lags. By combining all of them, we are then able to explore new ways of transient signal characterization.

Zernike ultrasonic tomography for fluid velocity imaging based on pipeline intrusive time-of-flight measurements

In this paper, we propose a novel ultrasonic tomography method for pipeline flow field imaging, based on the Zernike polynomial series. Having intrusive multipath time-offlight ultrasonic measurements (difference in flight time and speed of ultrasound) at the input, we provide at the output tomograms of the fluid velocity components (axial, radial, and orthoradial velocity). Principally, by representing these velocities as Zernike polynomial series, we reduce the tomography problem to an ill-posed problem of finding the coefficients of the series, relying on the acquired ultrasonic measurements. Thereupon, this problem is treated by applying and comparing Tikhonov regularization and quadratically constrained ℓ1 minimization. To enhance the comparative analysis, we additionally introduce sparsity, by employing SVD-based filtering in selecting Zernike polynomials which are to be included in the series. The first approach-Tikhonov regularization without filtering, is used because it is the most suitable method. The performances are quantitatively tested by considering a residual norm and by estimating the flow using the axial velocity tomogram. Finally, the obtained results show the relative residual norm and the error in flow estimation, respectively, ~0.3% and ~1.6% for the less turbulent flow and ~0.5% and ~1.8% for the turbulent flow. Additionally, a qualitative validation is performed by proximate matching of the derived tomograms with a flow physical model.

Enhancement of an Optical Fiber Sensor: Source Separation Based on Brillouin Spectrum

Distributed optical fiber sensors have gained an increasingly prominent role in structural-health monitoring. These are composed of an optical fiber cable in which a light impulse is launched by an opto-electronic device. The scattered light is of interest in the spectral domain: the spontaneous Brillouin spectrum is centered on the Brillouin frequency, which is related to the local strain and temperature changes in the optical fiber. When coupled with an industrial Brillouin optical time-domain analyzer (B-OTDA), an optical fiber cable can provide distributed measurements of strain and/or temperature, with a spatial resolution over kilometers of 40 cm. This paper focuses on the functioning of a B-OTDA device, where we address the problem of the improvement of spatial resolution. We model a Brillouin spectrum measured within an integration base of 1 m as the superposition of the elementary spectra contained in the base. Then, the spectral distortion phenomenon can be mathematically explained: if the strain is not constant within the integration base, the Brillouin spectrum is composed of several elementary spectra that are centered on different local Brillouin frequencies. We propose a source separation methodology approach to decompose a measured Brillouin spectrum into its spectral components. The local Brillouin frequencies and amplitudes are related to a portion of the integration base where the strain is constant. A layout algorithm allows the estimation of a strain profile with new spatial resolution chosen by the user. Numerical tests enable the finding of the optimal parameters, which provides a reduction to 1 cm of the 40-cm spatial resolution of the B-OTDA device. These parameters are highlighted during a comparison with a reference strain profile acquired by a 5-cm-resolution Rayleigh scatter analyzer under controlled conditions. In comparison with the B-OTDA strain profile, our estimated strain profile has better accuracy, with centimeter spatial resolution.

Calibration of a distributed SWE model using MODIS snow cover maps and in situ measurements

In this letter, we propose the calibration procedure for a Snow Water Equivalent (SWE) forecasting model, using Moderate-Resolution Imaging Spectroradiometer (MODIS) multi-temporal snow cover maps and in situ measurements. The presented study refers to one of the largest artificial lakes in the Western Europe – the Serre-Ponçon reservoir, on the Durance river, in the region of the French Alps. The SWE model, an integral part of the MORDOR (MOdèle à Réservoirs de Détermination Objective du Ruissellement) hydrological model, provides SWE as a function of local precipitation and temperature, as well as of accumulation and melting correction coefficients. The principal motivation for the proposed calibration method comes from the significant model sensitivity with respect to these two coefficients, which, given that they account for the influences of topology and mountain winds, ought to vary spatially. Three different optimization procedures are compared using the set of in situ measurements acquired by the EDF (Eléctricité de france) cosmic-ray snow sensors for 4 out of 36 ground stations in the regions of interest. The appropriate optimization method is selected and the corresponding representative optimal coefficients are derived for these four stations. Further, by combining the selected optimization algorithm and the continuous activation function, we propose a new method for deriving the spatially varying coefficients characterizing the entire region, using multi-temporal MODIS snow cover binary maps. When analysed with respect to the mean square error (MSE) criterion, the SWE model, calibrated in this manner, appears to be significantly more accurate than the original version (using a priori estimated, spatially fixed coefficients). Furthermore, the calibration procedure based on MODIS data is comparable and, for some ground stations, exhibits even better performances than the one based on the in situ measurements.

Unconstrained nonlinear optimization of a distributed SWE model using MODIS and in situ measurements over the French Alps

In this paper we propose the optimization of the snow sub-model of MORDOR using MODIS and in situ measurements for the case study of the Serre-Ponçon reservoir (one of the largest artificial lakes in Western Europe) on the Durance River in the French Alps. We consider the problem of optimizing the snow model as an unconstrained nonlinear optimization problem.

Maximum likelihood estimation of a low-order building model

The aim of this paper is to investigate the accuracy of the estimates learned with an open loop model of a building whereas the data is actually collected in closed loop, which corresponds to the true exploitation of buildings. We propose a simple model based on an equivalent RC network whose parameters are physically interpretable. We also describe the maximum likelihood estimation of these parameters by the EM algorithm, and derive their statistical properties. The numerical experiments clearly show the potential of the method, in terms of accuracy and robustness. We emphasize the fact that the estimations are linked to the generating process for the observations, which includes the command system. For instance, the features of the building are correctly estimated if there is a significant gap between the heating and cooling setpoint.