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Improving the Estimation of the Wavenumber Spectra From Altimeter Observations

Auteurs : Mailhes Corinne, Besson Olivier, Guillot Amandine et Le Gac Sophie

IEEE Transactions on Geoscience and Remote Sensing (Early Access), Vol. 60, pp. 1-10, May 5, 2021.

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Satellite altimeters provide sea-level measurements along satellite track. A mean profile based on the measurements averaged over a time period is then subtracted to estimate the sea-level anomaly (SLA). In the spectral domain, SLA is characterized by a power spectral density (PSD) whose slope in a log-log scale is a parameter of great interest for ocean monitoring. Estimation of this spectral slope is usually done through a cumulated periodogram using a large number of signal samples. The location and dates of the data induce the spatial and temporal resolution of the slope estimates. To improve this resolution, this article studies a new parametric method based on an autoregressive model combined with a warping of the frequency scale (denoted as ARWARP). This ARWARP model provides a PSD estimate, with a lower variance than the classical Fourier-based ones and is reliable in the case of a small sample number. To give a reference in the performance of the SLA slope estimation, the corresponding Cramér-Rao bound is derived. Then, rather than performing linear regression on the spectral estimates, a new estimator of the slope is suggested, based on a model fitting of the PSD. A statistical validation is proposed on simulated SLA signals, showing the performance of slope estimation using this ARWARP spectral estimator, compared to classical Fourier-based methods. Application to Sentinel-3 real data highlights the main advantage of the ARWARP model, making possible SLA slope estimation on a short signal segment, i.e., with a high spatial and/or temporal resolution.

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Traitement du signal et des images / Observation de la Terre

Automated Machine Health Monitoring at an Expert Level

Auteurs : Martin Nadine, Mailhes Corinne et Laval Xavier

Special issue of Acoustics Australia on Machine Condition Monitoring, Springer, 2021.

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Machine health condition monitoring is evidently a crucial challenge nowadays. Unscheduled breakdowns increase operating costs due to repairs and production losses. Scheduled maintenance implies taking the risk of replacing fully operational components. Human expertise is a solution for an outstanding expertise but at a high cost and for a limited quantity of data only, the analysis being time-consuming. Industry 4.0 and digital factory offer many alternatives to human monitoring. Time, cost and skills are the real stakes. The key point is how to automate each part of the process knowing that each one is valuable. Leaving aside scheduled maintenance, this paper copes with condition-based preventive maintenance and focuses on one fundamental step : the signal processing. After a brief overview of this specific area in which numerous technologies already exist, this paper argues for an automated signal processing at an expert level. The objective is to monitor a system over days, weeks, or years with as great accuracy as a human expert, and even better in regard to data investigation and analysis efficiency. After a data validation step most often ignored, any multimodal signal (vibration, current, acoustic, ...) is processed over its entire frequency band in view of identifying all harmonic families and their sidebands. Sophisticated processing such as filtering and demodulation creates relevant features describing the fine complex structures of each spectrum. A time-frequency feature tracking constructs trends over time to not only detect a failure but also to characterize and localize it. Such an automated expert-level processing is a way to raise alarms with a reduced false alarm probability.

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Traitement du signal et des images / Autre

Improving the Estimation of the Wavenumber Spectra from Altimeter Observations

Auteurs : Mailhes Corinne, Besson Olivier, Guillot Amandine et Le Gac Sophie

IEEE Transactions on Geoscience and Remote Sensing, (under minor revision).

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Traitement du signal et des images / Observation de la Terre

Amplitude and Phase Interaction in Hilbert Demodulation of Vibration Signals : Natural Gear Wear Modeling and Time Tracking for Condition Monitoring

Auteurs : Laval Xavier, Mailhes Corinne, Martin Nadine, Bellemain Pascal et Pachaud Christian

Mechanical Systems and Signal Processing, Elsevier, vol. 150, 2021.

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In the context of automatic and preventive condition monitoring of rotating machines, this paper revisits the demodulation process essential for detecting and localizing cracks in gears and bearings. The objective of the paper is to evaluate the performance of the well-known Hilbert demodulation by providing a quantified assessment in terms of signal processing. For this purpose, vibration test signals are simulated guided by the analysis of real-world measurements. The database comes from a natural wear experimentation on a test bench at an industrial scale and without any fault initiation. In the proposed simulated model, the amplitude modulation is designed in a physical approach in order to be able to set up the number of faulty teeth and their location. The impact of a limited spectral bandwidth filtering is quantified not only for the amplitude but also for the phase modulation estimations. The interactions between the amplitude and phase estimations are discussed. A focus is made on the analytic signal ambiguity due to the non-uniqueness of the amplitude estimation. This property induces an original investigation when demodulating the residual generated after a time synchronous averaging. Finally, as the objective is a continuous surveillance of a machine, results are given for a sequence of real-world measurements in order to visualize the fault evolution through the demodulation process.

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Traitement du signal et des images / Autre

Automatic Data-Driven Spectral Analysis Based on a Multi-Estimator Approach

Auteurs : Martin Nadine et Mailhes Corinne

Elsevier, Signal Processing, vol. 146, pp. 112–125, January, 2018.

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In signal processing, spectral analysis is widely used but, whereas computing the power spectral density (PSD) by Fourier approaches is relatively easy, its analysis and reading are much more demanding espe- cially for spectrally rich signals. This paper presents an original method which automatically picks out and estimates the relevant spectral structures of an unknown random stationary process, embedded in an unknown non-white Gaussian noise. First, a statistical hypothesis test is applied to each local max- imum value of the estimated PSD to detect the potential spectral peaks of interest. Second, an original feature space is proposed for classifying and characterizing the detected structures. Then, one key idea of the proposed strategy is to use not only one spectral estimator but to combine the results of different ones, taking benefits of their good properties. Therefore the detection and classification steps are ap- plied to different spectral estimations. A last fusion step outputs a complete attribute vector, including a confidence index, for each detected structure. Another key idea of this data-driven approach is that all parameters are automatically set up without a priori knowledge. This approach is fully adapted to the preventive maintenance of complex systems, as illustrated in the paper.

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Traitement du signal et des images / Autre

Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry

Auteurs : Halimi Abderrahim, Mailhes Corinne, Tourneret Jean-Yves et Snoussi Hichem

IEEE Trans. Geosci. and Remote Sensing, vol. 54, n°4, pp. 2207-2219, April, 2016.

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This paper proposes a new Bayesian strategy for the smooth estimation of altimetric parameters. The altimetric signal is assumed to be corrupted by a thermal and speckle noise distributed according to an independent and non-identically Gaussian distribution. We introduce a prior enforcing a smooth temporal evolution of the altimetric parameters which improves their physical interpretation. The posterior distribution of the resulting model is optimized using a gradient descent algorithm which allows us to compute the maximum a posteriori estimator of the unknown model parameters. This algorithm has a low computational cost that is suitable for real-time applications. The proposed Bayesian strategy and the corresponding estimation algorithm are evaluated using both synthetic and real data associated with conventional and delay/Doppler altimetry. The analysis of real Jason-2 and CryoSat-2 waveforms shows an improvement in parameter estimation when compared to state-of-the-art estimation algorithms.

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Traitement du signal et des images / Observation de la Terre

Time-frequency tracking of spectral structures estimated by a data-driven method

Auteurs : Gerber Timothée, Mailhes Corinne et Martin Nadine

IEEE Trans. Industrial Electronics, vol. 62, n°10, pp. 6616-6626, October, 2015.

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The installation of a condition monitoring system aims to reduce the operating costs of the monitored system by applying a predictive maintenance strategy. However, a system-driven configuration of the condition monitoring system requires the knowledge of the system kinematics and could induce lots a false alarms because of predefined thresholds. The purpose of this paper is to propose a complete data-driven method to automatically generate system health indicators without any a priori on the monitored system or the acquired signals. This method is composed of two steps. First, every acquired signal is analysed: the spectral peaks are detected and then grouped in more complex structure as harmonic series or modulation sidebands. Then, a time-frequency tracking operation is applied on all available signals: the spectral peaks and the spectral structures are tracked over time and grouped in trajectories, which will be used to generate the system health indicators. The proposed method is tested on real-world signals coming from a wind turbine test rig. The detection of a harmonic series and a modulation sideband reports the birth of a fault on the main bearing inner ring. The evolution of the fault severity is characterised by three automatically generated health indicators and is confirmed by experts.

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Traitement du signal et des images / Autre

AStrion Data Validation of Non-Stationary Wind Turbine Signals

Auteurs : Song Guanghan, Li Zhong-Yang, Bellemain Pascal, Martin Nadine et Mailhes Corinne

Insight - Non-Destructive Testing and Condition Monitoring (The Journal of The British Institute of Non-Destructive Testing), vol. 57, n° 8, pp. 457-463, August 2015.

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AStrion is an automatic spectrum analyser software, which proposes a new generic and data-driven method without any a priori information on the measured signals. In order to compute some general characteristics and derive the properties of the signal, the first step in this approach is to give some insight into the nature of the signal. This pre-analysis, the so-called data validation, contains a number of tests to reveal some of the properties and characteristics of the data, such as the acquisition validity (the absence of saturation and a posteriori in respect of the sampling theorem), the stationarity (or non-stationarity), the periodicity and the signal-to-noise ratio. Based on these characteristics, the proposed method defines indicators and alarm trigger thresholds and also categorises the signal into three classes, which helps to guide the following spectral analysis. The present paper introduces the four tests of this pre-analysis and the signal categorisation rules. Finally, the proposed approach is validated on a set of wind turbine vibration measurements to demonstrate its applicability for a long-term and continuous monitoring of real-world signals.

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Traitement du signal et des images / Autre

AStrion Strategy : From Acquisition to Diagnosis. Application to Wind Turbine Monitoring

Auteurs : Li Zhong-Yang, Gerber Timothée, Firla Marcin, Bellemain Pascal, Martin Nadine et Mailhes Corinne

Insight - Non-Destructive Testing and Condition Monitoring (The Journal of The British Institute of Non-Destructive Testing), vol. 57, n° 8, pp. 442-447, August 2015.

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This paper proposes an automatic procedure for condition monitoring. It represents a valuable tool for maintenance of expensive and spread systems such as wind turbine farms. Thanks to data-driven signal processing algorithms, the proposed solution is fully automatic for the user. The paper briefly describes all the steps of the processing, from pre-processing of acquired signal to interpretation of generated results. It starts with an angular resampling method with speed measurement correction. Then comes a data validation step, in both time/angular and frequency/order domains. After these preprocessings, the spectral components of the analyzed signal are identified and classified in several classes from sine wave to narrow band components. This spectral peak detection and classification allows extracting the harmonic and side-band series which may be part of the signal spectral content. Moreover, the detected spectral patterns are associated with the characteristic frequencies of the investigated system. Based on the detected side-band series, the full-band demodulation is performed. At each step, the diagnosis features are computed and dynamically tracked signal by signal. Finally, system health indicators are proposed to conclude about the condition of the investigated system. All mentioned steps create a self-sufficient tool for robust diagnosis of mechanical faults. The paper presents the performance of the proposed method on real-world signals from a wind turbine drive train.

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Traitement du signal et des images / Autre

Including Antenna Mispointing in a Semi-Analytical Model for Delay/Doppler Altimetry

Auteurs : Halimi Abderrahim, Mailhes Corinne, Tourneret Jean-Yves, Moreau Thomas et Boy François

IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 2, pp. 598-608, February, 2015.

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Delay/Doppler altimetry (DDA) aims at reducing the measurement noise and increasing the along-track resolution in comparison with conventional pulse-limited altimetry. In a previous paper, we have proposed a semi-analytical model for DDA, which considers some simplifications as the absence of mispointing antenna. This paper first proposes a new analytical expression for the flat surface impulse response (FSIR), considering antenna mispointing angles, a circular antenna pattern, no vertical speed effect, and uniform scattering. The 2-D delay/Doppler map is then obtained by a numerical computation of the convolution between the proposed analytical function, the probability density function of the heights of the specular scatterers, and the time/frequency point target response of the radar. The approximations used to obtain the semi-analytical model are analyzed, and the associated errors are quantified by analytical bounds for these errors. The second contribution of this paper concerns the estimation of the parameters associated with the multilook semi-analytical model. Two estimation strategies based on the least squares procedure are proposed. The proposed model and algorithms are validated on both synthetic and real waveforms. The obtained results are very promising and show the accuracy of this generalized model with respect to the previous model assuming zero antenna mispointing.

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Traitement du signal et des images / Observation de la Terre

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