Marie Chabert

On-Line Monitoring of Mechanical Faults in Variable-Speed Induction Motor Drives Using the Wigner Distribution

This paper deals with the detection of mechanical load faults in induction motors during speed transients. The detection strategy is based on stator current analysis. Mechanical load faults generally lead to load torque oscillations at specific frequencies related to the mechanical rotor speed. The torque oscillations produce a characteristic sinusoidal phase modulation of the stator current. Speed transients result in time-varying supply frequencies that prevent the use of classical, Fourier transform-based spectral estimation. This paper proposes the use of a time-frequency distribution, the Wigner Distribution, for stator current analysis. Fault indicators are extracted from the distribution for on-line condition monitoring. The proposed methods are implemented on a low-cost digital signal processor. Experimental results in a steady-state and during transients with load torque oscillations and load imbalance are presented.

Ship and Oil-Spill Detection Using the Degree of Polarization in Linear and Hybrid/Compact Dual-Pol SAR

Monitoring and detection of ships and oil spills using synthetic aperture radar (SAR) have received a considerable attention over the past few years, notably due to the wide area coverage and day and night all-weather capabilities of SAR systems. Among different polarimetric SAR modes, dual-pol SAR data are widely used for monitoring large ocean and coastal areas. The degree of polarization (DoP) is a fundamental quantity characterizing a partially polarized electromagnetic field, with significantly less computational complexity, readily adaptable for on-board implementation, compared with other well-known polarimetric discriminators. The performance of the DoP is studied for joint ship and oil-spill detection under different polarizations in hybrid/compact and linear dual-pol SAR imagery. Experiments are performed on RADARSAT-2 C-band polarimetric data sets, over San Francisco Bay, and L -band NASA/JPL UAVSAR data, covering the Deepwater Horizon oil spill in the Gulf of Mexico.

Mechanical load fault detection in induction motors by stator current time-frequency analysis

This paper examines the detection of mechanical faults in induction motors by stator current analysis. Mechanical faults lead generally to periodic load torque oscillations. The influence of the torque oscillations on the induction motor stator current is studied. The mechanical fault results in a sinusoidal phase modulations of the stator current. Based on these assumptions, several signal processing methods suitable for stator current signature analysis are discussed: classical spectral analysis, instantaneous frequency estimation and time-frequency analysis using the Wigner distribution. Experimental and simulation results validate the theoretical approach

Satellite image compression by post-transforms in the wavelet domain

This paper proposes a novel compression scheme with a tunable complexity-rate-distortion trade-off. As images increase in size and resolution, more efficient compression schemes with low complexity are required on-board Earth observation satellites. The standard of the Consultative Committee for Space Data Systems (CCSDS) defines a strip-based compression scheme with the advantages of a low complexity and an easy rate control [CCSDS, Image Data Compression Recommended Standard CCSDS 122.0-B-1 Blue Book, November 2005]. However, future mission specifications expect higher performance in terms of rate-distortion. The scheme proposed in this paper intends to perform better than the CCSDS standard while preserving low complexity and easy rate control. Moreover, to comply with existing on-board devices, the proposed core compression engine still uses the wavelet transform but in association with a linear post-processing inspired from the bandelet transform. The post-transform decomposes a small block of wavelet coefficients on a particular basis. This basis is adaptively selected within a predefined dictionary by rate-distortion optimization. The computational complexity depends upon the dictionary size and of the basis structure. An extremely simple dictionary, reduced to the Hadamard basis, is proposed. The post-transform efficiency is illustrated by experiments on various Earth observation images provided by the French Space Agency (CNES).

Estimation of the Degree of Polarization for Hybrid/Compact and Linear Dual-Pol SAR Intensity Images: Principles and Applications

Analysis and comparison of linear and hybrid/compact dual-polarization (dual-pol) synthetic aperture radar (SAR) imagery have gained a wholly new importance in the last few years, in particular, with the advent of new spaceborne SARs such as the Japanese ALOS PALSAR, the Canadian RADARSAT-2, and the German TerraSAR-X. Compact polarimetry, hybrid dual-pol, and quad-pol modes are newly promoted in the literature for future SAR missions. In this paper, we investigate and compare different hybrid/compact and linear dual-pol modes in terms of the estimation of the degree of polarization (DoP). The DoP has long been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. It can be effectively used to characterize the information content of SAR data. We study and compare the information content of the intensity data provided by different hybrid/compact and linear dual-pol SAR modes. For this purpose, we derive the joint distribution of multilook SAR intensity images. We use this distribution to derive the maximum likelihood and moment-based estimators of the DoP in hybrid/compact and linear dual-pol modes. We evaluate and compare the performance of these estimators for different modes on both synthetic and real data, which are acquired by RADARSAT-2 spaceborne and NASA/JPL airborne SAR systems, over various terrain types such as urban, vegetation, and ocean.

A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors

Remote sensing images are commonly used to monitor the earth surface evolution. This surveillance can be conducted by detecting changes between images acquired at different times and possibly by different kinds of sensors. A representative case is when an optical image of a given area is available and a new image is acquired in an emergency situation (resulting from a natural disaster for instance) by a radar satellite. In such a case, images with heterogeneous properties have to be compared for change detection. This paper proposes a new approach for similarity measurement between images acquired by heterogeneous sensors. The approach exploits the considered sensor physical properties and specially the associated measurement noise models and local joint distributions. These properties are inferred through manifold learning. The resulting similarity measure has been successfully applied to detect changes between many kinds of images, including pairs of optical images and pairs of optical-radar images.

An interpolation-based watermarking scheme

Interpolation techniques are often designed to provide a good perceptual quality from known sample values. However, interpolation is essentially considered as a source of decoding errors for watermarking schemes. Conversely, this paper proposes an informed watermarking scheme based on interpolation. This scheme takes advantage of interpolation to generate imperceptible marks in the spatial domain. It can be related to random binning schemes with particular codebook and decoding rule. Theoretical performances are derived and informed embedding strategies are proposed. Two particular implementations based on bilinear and spline interpolation are then applied to image watermarking. The good robustness of these schemes to noise and valumetric attacks is confirmed by simulations. Finally, an attack is specifically designed to check the algorithm security.

Satellite image compression by directional decorrelation of wavelet coefficients

This paper presents a satellite image compression scheme based on a post-processing of the wavelet transform of images. The bandelet transform is a directional post-processing of wavelet coefficients. Thanks to a low computational complexity, this transform is a good candidate for future on-board satellite image compression systems. First, we analyze the ability of the bandelets to exploit directional correlations between wavelet coefficients. This study leads to an improved post-processing with a better decorrelation of adjacent wavelet coefficients in the vertical or in the horizontal direction taking into account the wavelet subband orientations. To perform even better decorrelation, bases are also build by principal component analysis (PCA). This results in an improved compression performance without increasing the computational complexity.

Time-scale analysis of abrupt changes corrupted by multiplicative noise

Multiplicative Abrupt Changes (ACs) have been considered in many applications. These applications include image processing (speckle) and random communication models (fading). Previous authors have shown that the Continuous Wavelet Transform (CWT) has good detection properties for ACs in additive noise. This work applies the CWT to AC detection in multiplicative noise. CWT translation invariance allows to define an AC signature. The problem then becomes signature detection in the time-scale domain. A second-order contrast criterion is defined as a measure of detection performance. This criterion depends upon the first- and second-order moments of the multiplicative processs CWT. An optimal wavelet (maximizing the contrast) is derived for an ideal step in white multiplicative noise. This wavelet is asymptotically optimal for smooth changes and can be approximated for small AC amplitudes by the Haar wavelet. Linear and quadratic suboptimal signature-based detectors are also studied. Closed-form threshold expressions are given as functions of the false alarm probability for three of the detectors. Detection performance is characterized using Receiver Operating Characteristic (ROC) curves computed from Monte-Carlo simulations.

Detection of mechanical load faults in induction motors at variable speed using stator current time-frequency analysis

This paper examines the detection of mechanical load faults in induction motors during speed transients by stator current analysis. Mechanical load faults generally lead to load torque oscillations at specific frequencies. these frequencies are related to the mechanical rotor speed. The torque oscillations produce a characteristic sinusoidal phase modulation of the stator current. Speed transients result in time-varying supply frequencies that prevent the classical, Fourier transform based spectral estimation. This problem can be overcome using time-frequency signal analysis. The methods applied in this paper are instantaneous frequency estimation and the Wigner Distribution. Furthermore, an adaptive demodulation method is proposed. The theoretical considerations are validated on signals obtained from an experimental setup.