Vers une Carte d'Identité Spectrale

National audienceThis paper studies a new spectral analysis strategy for detecting, characterizing and classifying the different “spectral structures” of an unknown stationary process. A “spectral structure” is defined as a sinusoidal wave, a narrow band signal or a noise peak. The spectral analysis strategy is based on the use of several successive and complementary spectral analyses. Then, the proposed methodology provides a way to calculate a “spectral identity card” of each spectral structure, similarly to a real I.D. card. This I.D. card including all information related to this structure results from the fusion of intermediate cards, which are obtained from different spectral analysis algorithms. The I.D. card permits the classification of the detected spectral structure into one of the following four classes: Pure Frequency, Narrow Band, Alarm and Reject

A Spectral Identiy Card

International audienceThis paper studies a new spectral analysis strategy for detecting, characterizing and classifying spectral structures of an unknown stationary process. The spectral structures we consider are defined as sinusoidal waves, narrow band signals or noise peaks. A sum of an unknown number of these structures is embedded in an unknown colored noise. The proposed methodology provides a way to calculate a spectral identity card, which features each of these spectral structures, similarly to a real I.D. The processing is based on a local Bayesian hypothesis testing, which is defined in frequency and which takes account of the noise spectrum estimator. Thanks to a matching with the corresponding spectral window, each I.D. card permits the classification of the associated spectral structure into one of the following four classes: Pure Frequency, Narrow Band, Alarm and Noise. Each I.D. card is actually the result of the fusion of intermediate cards, obtained from complementary spectral analysis methods

Identification of harmonics and sidebands in a finite set of spectral components

International audienceSpectral analysis along with the detection of harmonics and modulation sidebands are key elements in condition monitoring systems. Several spectral analysis tools are already able to detect spectral components present in a signal. The challenge is therefore to complete this spectral analysis with a method able to identify harmonic series and modulation sidebands. Compared to the state of the art, the method proposed takes the uncertainty of the frequency estimation into account. The identification is automatically done without any a priori, the search of harmonics is exhaustive and moreover the identification of all the modulation sidebands of each harmonic is done regardless of their energy level. The identified series are characterized by criteria which reflect their relevance and which allow the association of series in families, characteristic of a same physical process. This method is applied on real-world current and vibration data, more or less rich in their spectral content. The identification of sidebands is a strong indicator of failures in mechanical systems. The detection and tracking of these modulations from a very low energy level is an asset for earlier detection of the failure. The proposed method is validated by comparison with expert diagnosis in the concerned fields

Automatic method for spectral pattern association with characteristic frequencies

International audienceThis paper proposes an advanced signal-processing technique to improve the condition monitoring of rotating machinery. The proposed method employs the results of a blind spectrum interpretation including harmonic and sideband series detection. The contribution of this paper is an algorithm for automatic association of harmonic and sideband series with the characteristic fault frequencies listed in the kinematic configuration of the monitored system. The proposed algorithm is efficient in inspection of real-world signals, which contain a vast number of detected spectral components. The proposed approach has the advantage of taking into account a possible slip of the rolling-element bearings. The performance of the proposed algorithm is illustrated on real-world data by investigating a shaft problem of an industrial wind turbine high-speed shaft

Automatic Characteristic Frequency Association and All-Sideband Demodulation for Detection of a Bearing Fault of a Test Rig

International audienceThis paper proposes advanced signal-processing techniques to improve condition monitoring of operating machines. The proposed methods use the results of a blind spectrum interpretation that includes harmonic and sideband series detection. The rst contribution of this study is an algorithm for automatic association of harmonic and sideband series to characteristic fault frequencies according to a kinematic conguration. The approach proposed has the advantage of taking into account a possible slip of the rolling-element bearings. In the second part, we propose a full-band demodulation process from all sidebands that are relevant to the spectral estimation. To do so, a multi-rate ltering process in an iterative schema provides satisfying precision and stability over the targeted demodulation band, even for unsymmetrical and extremely narrow bands. After synchronous averaging, the ltered signal is demodulated for calculation of the amplitude and frequency modulation functions, and then any features that indicate faults. Finally, the proposed algorithms are validated on vibration signals measured on a test rig that was designed as part of the Eu-ropean Innovation Project KAStrion'. This rig simulates a wind turbine drive * Corresponding author Email address: [email protected] (Marcin Firla) Preprint submitted to Mechanical Systems and Signal Processing March 11, 2016 train at a smaller scale. The data show the robustness of the method for localizing and extracting a fault on the main bearing. The evolution of the proposed features is a good indicator of the fault severity

Traitement automatique du signal pour la surveillance vibratoire des éoliennes : recadrage temps-fréquence, association cinématique et démodulation multi-bandes

This thesis proposes a three signal-processing methods oriented towards the condition monitoring and diagnosis. In particular the proposed techniques are suited for vibration-based condition monitoring of rotating machinery which works under highly non-stationary operational condition as wind turbines, but it is not limited to such a usage. All the proposed methods are automatic and data-driven algorithms.The first proposed technique enables a selection of the most stationary part of signal by cropping time-frequency representation of the signal.The second method is an algorithm for association of spectral patterns, harmonics and sidebands series, with characteristic frequencies arising from kinematic of a system under inspection. This method features in a unique approach dedicated for rolling-element bearing which enables to overcome difficulties caused by a slippage phenomenon.The third technique is an all-sideband demodulation algorithm. It features in a multi-rate filter and proposes health indicators to facilitate an evaluation of the condition of the investigated system.In this thesis the proposed methods are validated on both, simulated and real-world signals. The presented results show good performance of all the methods.Cette thèse propose trois méthodes de traitement du signal orientées vers la surveillance d’état et le diagnostic. Les techniques proposées sont surtout adaptées pour la surveillance d’état, effectuée à la base de vibrations, des machines tournantes qui fonctionnent dans des conditions d’opération non-stationnaires comme par exemple les éoliennes mais elles ne sont pas limitées à un tel usage. Toutes les méthodes proposées sont des algorithmes automatiques et gérés par les données.La première technique proposée permet de sélectionner la partie la plus stationnaire d’un signal en cadrant la représentation temps-fréquence d’un signal.La deuxième méthode est un algorithme pour l’association des dispositions spectrales, des séries harmoniques et des séries à bandes latérales avec des fréquences caractéristiques provennant du cinématique d'un système analysé. Cette méthode propose une approche unique dédiée à l’élément roulant du roulement qui permet de surmonter les difficultés causées par le phénomène de glissement.La troisième technique est un algorithme de démodulation de bande latérale entière. Elle fonctionne à la base d’un filtre multiple et propose des indicateurs de santé pour faciliter une évaluation d'état du système sous l’analyse.Dans cette thèse, les méthodes proposées sont validées sur les signaux simulés et réels. Les résultats présentés montrent une bonne performance de toutes les méthodes