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

Time-Frequency Tracking of Spectral Structures Estimated by a Data-Driven Method

International audience—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

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

Consequences of non-respect of the Bedrosian theorem when demodulating

International audienceVibration data acquired during system monitoring periods are rich in harmonics characterizing the presence of several mechanical parts in the system. Periodic variations of the torque or of the load create modulation side-bands around those harmonics. Even if the energy impact of the side-bands is small compared to the total energy of the signal, they are strong indicators of failures in mechanical systems. Unfortunately, these effects are of little concern in most of condition monitoring systems. When considering the problem with a signal processing point of view, a demodulation of those side-bands allows a time visualization of the modulating functions which are a precise image of the torque or the load variations. This demodulation can be done on the analytical signal directly derived from the original data. But to do that, data and specifically its spectrum should respect some constraints. The purpose of this paper is to underline those constraints, often forgotten. In particular, the respect of the non-overlapping condition in the Bedrosian theorem is discussed for signals and modulation rates that can be encountered on rotating machines. The respect of the constraints depends on the monitored phenomenon (e.g., gearmesh, rotating shaft), the modulation phenomenon (e.g., belt frequency, rotor current) and the type of medium (e.g., vibrations, electrical current). In the case where the constraints are not satisfied, we explain the consequences in terms of signal processing. These results are illustrated by two industrial case studies

AStrion strategy: from acquisition to diagnosis. Application to wind turbine monitoring

International audienceThis 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 pre-processings, 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

Dynamic tracking of modulated components : application to automatic condition monitoring of failures in wind farms

La surveillance automatique consiste à vérifier le bon fonctionnement d'un système tout au long de sa durée d'utilisation et ce, sans intervention humaine. Elle permet de mettre en place une stratégie de maintenance prévisionnelle qui présente un intérêt économique majeur, en particulier dans le cas de systèmes isolés comme les éoliennes construites en pleine mer. La surveillance automatique se base sur l'acquisition plus ou moins régulière de signaux pendant le fonctionnement du système surveillé. L'analyse de ces signaux doit permettre d'établir un diagnostic et de prendre une décision sur le déclenchement des opérations de maintenance. Dans cette thèse, nous proposons une méthode d'analyse générique permettant de s'adapter à n'importe quel système surveillé. La méthode se déroule en plusieurs étapes. Premièrement, chaque signal est analysé individuellement pour en extraire son contenu spectral, c'est-à-dire identifier les pics spectraux, les séries harmoniques et les bandes de modulation présents dans sa densité spectrale. Ensuite, ce contenu spectral est suivi au cours du temps pour former des trajectoires sur l'ensemble de la séquence de signaux acquis. Ces trajectoires permettent de générer des tendances qui sont le reflet de la santé du système. Enfin, les tendances sont analysées pour identifier un changement au cœur du système qui serait synonyme d'usure ou de défaut naissant. Cette méthodologie est validée sur de nombreux signaux réels provenant de la surveillance de différents systèmes mécaniques.The automatic monitoring consists in verifying without any human intervention that a system is operating well. The monitoring allows to use a predictive maintenance strategy, which is economically interesting, especially in the case of isolated systems like off-shore wind turbines. The automatic monitoring is based on signals acquired more or less regularly while the monitored system is operating. The analysis of these signals should be sufficient to diagnose the system and to decide whether or not the maintenance operations should be done. In this thesis, we propose a generic analysis method able to adapt itself to any monitored system. This method is composed by several steps. First, each signal is analyzed individually in order to extract its spectral content, that is to identify the spectral peaks, the harmonic series and the modulation sidebands presents in the signal spectrum. Then, the spectral content is tracked through time to construct spectral trajectories in the sequence of acquired signal. These trajectories are used to generate trends which indicate the state of the system health. Finally, the trends are analyzed to identify a change in the system response which would indicate some wear or a fault in is early stage. This analysis method is validated on real world signals acquired on different mechanical systems

Suivi dynamique de composantes modulées : application à la surveillance automatique de défauts dans les éoliennes

The automatic monitoring consists in verifying without any human intervention that a system is operating well. The monitoring allows to use a predictive maintenance strategy, which is economically interesting, especially in the case of isolated systems like off-shore wind turbines. The automatic monitoring is based on signals acquired more or less regularly while the monitored system is operating. The analysis of these signals should be sufficient to diagnose the system and to decide whether or not the maintenance operations should be done. In this thesis, we propose a generic analysis method able to adapt itself to any monitored system. This method is composed by several steps. First, each signal is analyzed individually in order to extract its spectral content, that is to identify the spectral peaks, the harmonic series and the modulation sidebands presents in the signal spectrum. Then, the spectral content is tracked through time to construct spectral trajectories in the sequence of acquired signal. These trajectories are used to generate trends which indicate the state of the system health. Finally, the trends are analyzed to identify a change in the system response which would indicate some wear or a fault in is early stage. This analysis method is validated on real world signals acquired on different mechanical systems.La surveillance automatique consiste à vérifier le bon fonctionnement d'un système tout au long de sa durée d'utilisation et ce, sans intervention humaine. Elle permet de mettre en place une stratégie de maintenance prévisionnelle qui présente un intérêt économique majeur, en particulier dans le cas de systèmes isolés comme les éoliennes construites en pleine mer. La surveillance automatique se base sur l'acquisition plus ou moins régulière de signaux pendant le fonctionnement du système surveillé. L'analyse de ces signaux doit permettre d'établir un diagnostic et de prendre une décision sur le déclenchement des opérations de maintenance. Dans cette thèse, nous proposons une méthode d'analyse générique permettant de s'adapter à n'importe quel système surveillé. La méthode se déroule en plusieurs étapes. Premièrement, chaque signal est analysé individuellement pour en extraire son contenu spectral, c'est-à-dire identifier les pics spectraux, les séries harmoniques et les bandes de modulation présents dans sa densité spectrale. Ensuite, ce contenu spectral est suivi au cours du temps pour former des trajectoires sur l'ensemble de la séquence de signaux acquis. Ces trajectoires permettent de générer des tendances qui sont le reflet de la santé du système. Enfin, les tendances sont analysées pour identifier un changement au cœur du système qui serait synonyme d'usure ou de défaut naissant. Cette méthodologie est validée sur de nombreux signaux réels provenant de la surveillance de différents systèmes mécaniques

Anomaly detection system

The present disclosure relates to a method and system for detecting an anomaly in a mechanical or electro-mechanical system, and in particular to a method and system for identifying 5 harmonic and/or modulation spectral components in a signal from at least one sensor detecting vibrations and/or electrical fluctuations in the system

PROFESSIONALLY-PRODUCED MUSIC SEPARATION GUIDED BY COVERS

This paper addresses the problem of demixing professionally produced music, i.e., recovering the musical source signals that compose a (2-channel stereo) commercial mix signal. Inspired by previous studies using MIDI synthesized or hummed signals as external references, we propose to use the multitrack signals of a cover interpretation to guide the separation process with a relevant initialization. This process is carried out within the framework of the multichannel convolutive NMF model and associated EM/MU estimation algorithms. Although subject to the limitations of the convolutive assumption, our experiments confirm the potential of using multitrack cover signals for source separation of commercial music. 1