PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes

The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes

Développement de quelques outils de traitement du signal pour le diagnostic vibro-acoustique des machines aéronautiques

Le prétraitement des signaux de vibration dans des conditions difficiles comme celles de l'aéronautique semble être compliqué. Les conditions de fonctionnement sont nonstationnaires et le moteur présente au moins deux familles harmoniques non-linéaires liées à l'arbre à basse et haute pression. De plus, les contraintes de conception imposent un nombre réduit d'accéléromètres (généralement deux) qui est insuffisant pour détecter tous les phénomènes liés à l'arbre. Les signaux acoustiques ne sont pas soumis à cette dernière contrainte. Cependant, ils sont très bruyants par rapport aux signaux de vibration et peuvent ne pas détecter les problèmes de faible énergie. De plus, ils dépendent fortement de la position du microphone et de sa directivité. Ainsi, l'objectif de la thèse est de proposer/essayer des méthodes robustes pour principalement (i) l'interférence entre différents phénomènes linéaires et non linéaires, (ii) les conditions de fonctionnement non stationnaires et (iii) les phénomènes de bruit à large bande (lorsqu'ils ne sont pas d'intérêt). Ces difficultés scientifiques sont considérées à travers (1) une détection aveugle des pics spectraux, (2) l'estimation de la vitesse instantanée et (3) l'estimation de la composante déterministe/tonale.Pre-processing vibration signals in harsh conditions such as the aeronautic conditions seems a complicated task. The operating conditions are nonstationary and the motor exhibits at least two harmonic non-linear families related to low and high pressure shaft. Furthermore, the design constraints impose a reduced number of accelerometers (generally two) which is unfortunately insufficient to detect all the shaft related phenomena. The acoustic signals are not subjected to the latter constraint. However, they are very noisy in comparison to vibration signals and may not detect low energy problems and very low frequency phenomena. Besides, the obtained signals depend strongly on the microphone position and its directivity in addition to the problem of clipping with medium to high acoustic pressure values. Thus, the PhD objective is to propose methods robust to mainly (i) the interference between different linear and non-linearly related phenomena, (ii) the nonstationary operating conditions and (iii) the broadband noise phenomena. These scientific difficulties are considered through (1) a blind detection of spectral peaks, (2) the estimation of the instantaneous speed and (3) the estimation of the deterministic/tonal component

Synchronous analysis of cyclo-non-stationary signals: A comprehensive study with aeronautic applications

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On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals

International audienceThe compressive sensing (CS) of mechanical signals is an emerging research topic for remote condition monitoring. The signals generated by machines are mostly periodic due to the rotating nature of its components. Often, these vibrations witness strong interactions among two or multiple rotating sources, leading to modulation phenomena. This paper is specifically concerned with the CS of this particular class of signals using a Bayesian approach. The main contribution of this paper is to consider the particular spectral structure of these signals through two families of hierarchical models. The first one adopts a block-sparse model that jointly estimates the sparse coefficients at identical or symmetrical positions around the carrier frequencies. The second is a spike-and-slab model where the spike component takes into account the symmetrical properties of the support of non-zero-coefficients in the spectrum. The resulting posterior distribution is approximated using a Gibbs sampler. Simulations show that considering the structure in the prior model yields better noise shrinkage and better reconstruction of small side-bands. Application to condition monitoring of a gearbox through CS of vibration signals highlights the good performance of the proposed models in reconstructing the signal, offering an accurate fault detection with relatively high compression rate

PHM Survey : Implementation of Diagnostic Methods for Monitoring Industrial Systems

International audienceThe modernization of industrial sectors involves the use of complex industrial systems and therefore requires condition based maintenance. This one aims at increasing the operational availability and reducing the life-cycle while increasing the reliability and life expectancy of industrial systems. This maintenance also called predictive maintenance is a part of an emerging philosophy called PHM ‘Prognostics and Health Management’. In this paper, the PHM will be emphasized on the existing diagnostic methods used for fault isolation and identification. This depicts an important part of the PHM as it exploits the data given by the signal-processing step and its output is treated by the prognostic part. The diagnostic is mainly classified in three categories that will be highlighted in this paper