6 research outputs found
Real-time multi-features dynamic classification for bearing monitoring by vibrations analysis
La naissance dâun dĂ©faut dans un systĂšme mĂ©canique se traduit le plus souvent par un changement de comportement vibratoire dans le domaine spectral et temporel. La dĂ©tection de dĂ©faut par lâanalyse vibratoire se base sur la surveillance continue du comportement dâun composant en examinant lâĂ©volution des indicateurs de dĂ©faut en temps rĂ©el. Lâanalyse de lâĂ©volution de ces indicateurs par la mĂ©thode de classification dynamique permet une dĂ©tection prĂ©coce et fiable de dĂ©faut et un suivi continu de lâĂ©tat de dĂ©gradation du composant. La classification dynamique des indicateurs en des classes dynamiques peut fournir des informations utiles quant au comportement des roulements endommagĂ©s. Ces informations peuvent ĂȘtre Ă©galement employĂ©es pour prĂ©dire la fin de vie de composants. Contrairement Ă la classification statique, la classification dynamique introduit une nouvelle dimension (le temps) qui permet une dĂ©tection en temps rĂ©el de dĂ©faut et une meilleure visibilitĂ© du comportement de roulement rĂ©vĂ©lĂ©e par le dĂ©placement et lâĂ©volution temporelle des classes formĂ©es par les indicateurs. Ce papier a pour objectif dâintroduire la classification dynamique comme outil de dĂ©tection et de suivi dâĂ©tat dâendommagement. Cette mĂ©thode de classification regroupe les indicateurs en deux classes dynamiques ; une classe reprĂ©sente lâĂ©tat sain du roulement, et une classe reprĂ©sente lâĂ©tat dĂ©fectueux du roulement dĂ©faut. Le suivi de lâĂ©volution de la classe « dĂ©faut» permet de tirer plusieurs informations utiles, comme la vitesse de dĂ©gradation, la relation entre la trajectoire de la classe dĂ©faut et le changement des conditions de charge ... La mĂ©thode de classification dynamique choisie sera validĂ©e en analysant plusieurs dĂ©gradations de roulements issus d'un banc de fatigue de roulements Ă butĂ©es Ă billes SNR51207
Extraction des parameÌtres et classification dynamique dans le cadre de la deÌtection et du suivi de deÌfaut de roulements
Vibration analysis remains the most popular and most effective tool for monitoring the internal state of an operating machine. Through vibration analysis, fault indicators are extracted then monitored to detect/ locate the presence of a defect. However, counting solely on the evolution of these fault indicators to diagnose a machine can cause false alarms and question the reliability of the diagnosis. In this thesis, we combined vibration analysis tools with pattern recognition method to firstly improve fault detection reliability of bearings, secondly to assess the severity of degradation by closely monitor the defect growth and finally to estimate their remaining useful life. For these reasons, we have designed a pattern recognition process capable of; identifying defect even in machines running under non stationary conditions, processing evolving data and can handle an online learning. Three dynamic classification methods have been developed: Dynamic DBSCAN was developed to capitalize on the time evolution of the data and their classes, Evolving Scalable DBSCAN that was created to include the online processing and finally Dynamic Fuzzy Scalable DBSCAN a dynamic fuzzy and semi-supervised version of ESDBSCAN. With these techniques we were are able to enhance the reliability of fault detection by identifying the origin of the fault indicators evolution. The application of the designed process on real data helped us prove the legitimacy of the proposed techniques in identifying the different states of bearings over time (healthy or normal, defective) and the origin of the observations' evolution with a low error rate, a reliable diagnosis and a low memory occupation.Parmi les techniques utiliseÌes en maintenance, l'analyse vibratoire reste l'outil le plus efficace pour surveiller l'eÌtat interne des machines tournantes en fonctionnement. En et l'eÌtat de chaque composant peut eÌtre caracteÌriseÌ par un ou plusieurs indicateurs de deÌfaut issus de l'analyse vibratoire. Le suivi de ces indicateurs permet de deÌtecter le deÌfaut. Cependant, l'eÌvolution de ces indicateurs peut eÌtre influenceÌe par d'autres parameÌtres. Cela peut provoquer des fausses alarmes et remettre en question la fiabiliteÌ du diagnostic. Cette theÌse a pour objectif de combiner l'analyse vibratoire avec la meÌthode de reconnaissance des formes afin d'une part d'ameÌliorer la deÌtection de deÌfaut de roulement et d'autre part de mieux suivre l'eÌvolution de la deÌgradation dans le temps. Pour cela nous avons deÌveloppeÌ trois meÌthodes de classification dynamique : DynamicDBSCAN qui a eÌteÌ deÌveloppeÌe pour suivre les eÌvolutions des classes, Evolving scalable DBSCAN qui repreÌsente une version en ligne et eÌvolutive de DBSCAN et finalement Dynamic Fuzzy Scalabale DBSCAN qui est une version dynamique et floue ESDBSCAN adapteÌe pour un apprentissage en ligne. Ces meÌthodes distinguent les variations des observations lieÌes au changement du mode de fonctionnement de la machine (variation de charges) et les variations lieÌes au deÌfaut. Ainsi, Elles permettent de deÌtecter, de façon preÌcoce, l'apparition de deÌfaut. L'application sur des donneÌes reÌelles a permis d'identifier les diffeÌrents eÌtats du roulement au cours du temps (sain ou normal, deÌfectueux) et l'eÌvolution des observations lieÌe aÌ la variation de charges avec un taux d'erreur faible et d'eÌtablir un diagnostic fiable
Extraction of new features and integration of dynamic classification to improve bearing fault monitoring
Parmi les techniques utilisĂ©es en maintenance, l'analyse vibratoire reste l'outil le plus efficace pour surveiller l'Ă©tat interne des machines tournantes en fonctionnement. En effet l'Ă©tat de chaque composant constituant la machine peut ĂȘtre caractĂ©risĂ© par un ou plusieurs indicateurs de dĂ©faut issus de l'analyse vibratoire. Le suivi de ces indicateurs permet de dĂ©tecter la prĂ©sence d'un dĂ©faut et mĂȘme de le localiser. Cependant, l'Ă©volution de ces indicateurs peut ĂȘtre influencĂ©e par d'autres paramĂštres comme la variation de charge, la vitesse de rotation ou le remplacement d'un composant. Cela peut provoquer des fausses alarmes et remettre en question la fiabilitĂ© du diagnostic. Cette thĂšse a pour objectif de combiner l'analyse vibratoire avec la mĂ©thode de reconnaissance des formes afin d'une part d'amĂ©liorer la dĂ©tection de dĂ©faut des composants en particulier le dĂ©faut de roulement et d'autre part de mieux suivre l'Ă©volution de la dĂ©gradation pour caractĂ©riser le degrĂ© de sĂ©vĂ©ritĂ© du dĂ©faut. Pour cela nous avons dĂ©veloppĂ© des mĂ©thodes de classification dynamique pour prendre en compte l'Ă©volution du systĂšme. Les observations Ă classifier sont constituĂ©es d'indicateurs de dĂ©fauts et des combinaisons linĂ©aires de ceux-ci. La dĂ©marche de la reconnaissance des formes dynamique consiste Ă extraire, Ă sĂ©lectionner et Ă classifier ces observations de façon continue. Trois mĂ©thodes de classification dynamiques ont Ă©tĂ© dĂ©veloppĂ©es durant cette thĂšse : le « Dynamic DBSCAN » qui la premiĂšre version dynamique de DBSCAN dĂ©veloppĂ©e pour pouvoir suivre les Ă©volutions des classes, « Evolving scalable DBSCAN » ESDBSCAN qui reprĂ©sente une version en ligne et Ă©volutive de DBSCAN et finalement « Dynamic Fuzzy Scalabale DBSCAN » DFSDBSCAN qui est une version dynamique et floue de la mĂ©thode de classification ESDBSCAN adaptĂ©e pour un apprentissage en ligne. Ces mĂ©thodes distinguent les variations des observations liĂ©es au changement du mode de fonctionnement de la machine (variation de vitesse ou de charges) et les variations liĂ©es au dĂ©faut. Ainsi, Elles permettent de dĂ©tecter, de façon prĂ©coce, l'apparition d'un dĂ©faut qui se traduit par la crĂ©ation d'une nouvelle classe dite classe dĂ©gradĂ©e et de suivre l'Ă©volution de celle-ci. Cette mĂ©thodologie permettrait d'amĂ©liorer l'estimation de la durĂ©e de vie rĂ©siduelle du composant en analysant la distance sĂ©parant la classe "saine" et "dĂ©gradĂ©e". L'application sur des donnĂ©es rĂ©elles a permis d'identifier les diffĂ©rents Ă©tats du roulement au cours temps (sain ou normal, dĂ©fectueux) et l'Ă©volution des observations liĂ©e Ă la variation de vitesse et au changement de charges avec un taux d'erreur faible et d'Ă©tablir un diagnostic fiable. Afin de caractĂ©riser le degrĂ© de prĂ©cocitĂ© du diagnostic des mĂ©thodes dĂ©veloppĂ©es nous avons comparĂ© ces rĂ©sultats avec ceux Ă©tablis par des mĂ©thodes classiques de dĂ©tection. Cette comparaison nous a montrĂ© que les mĂ©thodes proposĂ©es permettent un diagnostic plus prĂ©coce et plus fiable.Mots clĂ©s : Diagnostic et suivi, roulements, mĂ©thodes de reconnaissance des formes, apprentissage en ligne, classification dynamique, analyse vibratoire, DFSDBSCAN, ESDBSCAN, DDBSCAN.Various techniques can be used in rotating machines condition based maintenance. Among which vibration analysis remains the most popular and most effective tool for monitoring the internal state of an operating machine. Through vibration analysis, the state of each component constituting the machine can be characterized by one or more fault indicators. Monitoring these indicators can be used to detect the presence of a defect or even locate it. However, the evolution of these indicators can be influenced by other parameters than defect such as the variation of load, speed or replacement of a component. So counting solely on the evolution of these fault indicators to diagnose a machine can cause false alarms and question the reliability of the diagnosis.In this thesis, we combined vibration analysis tools with pattern recognition method to firstly improve fault detection reliability of components such as bearings, secondly to assess the severity of degradation by closely monitor the defect growth and finally to estimate their remaining useful life. For these reasons, we have designed a pattern recognition process capable of; identifying defect even in machines running under non stationary conditions, processing evolving data of an evolving system and can handle an online learning. This process will have to decide the internal state of the machine using only faults indicators or linear combinations of fault indicators.The process of pattern recognition of dynamic forms consists of extracting and selecting useful information, classify these observations continuously into their right classes then decide on an action according to the observations' class.Three dynamic classification methods have been developed during this thesis: Dynamic DBSCAN that was developed to capitalize on the time evolution of the data and their classes, Evolving Scalable DBSCAN (ESDBSCAN) that was created to overcome the shortcoming of DDBSCAN in online processing and finally Dynamic Fuzzy Scalable DBSCAN (DFSDBSCAN); a dynamic fuzzy and semi-supervised version of ESDBSCAN. These methods can detect the observations evolution and identify the nature of the change causing it; either if it's a change in operating mode of the machine (speed variation or load) or a change related to the defect.With these techniques we were are able to enhance the reliability of fault detection by identifying the origin of the fault indicators evolution. An evolution caused by an alteration of the operating mode and changes caused by defect result in two different types of classes evolution (the appearance of a new class we named it 'defected' in case of defect or a drift otherwise). Not only that but these techniques helped us enhance the precocity of the fault detection and estimate the remaining useful life of the monitored component as well by analyzing the distance separating the class 'healthy' and 'defected'.The application of the designed process on real data helped us prove the legitimacy of the proposed techniques in identifying the different states of bearings over time (healthy or normal, defective) and the origin of the observations' evolution with a low error rate, a reliable diagnosis and a low memory occupation.Keywords: Diagnosis and monitoring, bearings, pattern recognition, learning, dynamic classification, Vibration Analysis, DFSDBSCAN, ESDBSCAN, DDBSCA
Online classification for spalling detection and vibratory behavior monitoring
Vibration analysis is the most widely used tool in industrial application for machineâs
health condition assessment. Bearings, however, are very sensitive and require special
attention. Vibration analysis employs various signal processing methods such as spectral
analysis, time-scale, time frequency analysis, etc. these methods are used to analyze
bearingsâ vibratory behavior by monitoring the evolution of statistical
indicators.However, diagnosing the bearing depending on traditional features only isnât
sufficient to assure effective or reliable assessment of the componentâ health condition.
This paper proposes a multi-features online dynamic classification as a new method for
fault detection and health condition monitoring for bearings; this technique uses multiple
features, including traditional features extracted from the raw signal, two special
features extracted by wavelet analysis, the spectral kurtosis, coupled with a nonlinear
principal component analysis and a dynamic classification to capitalize on the hidden
information in the time evolution of the features.Through this article, we introduce
different measures and techniques used to characterize the health state of rolling, then
we deploy a methodology using dynamic classification to detect early defect. To ensure an
almost continuous surveillance, this methodology is based on a real-time analysis, and
uses specific statistical indicators adapted to the experimental bench. Then, the
monitoring of the degradation is achieved through the resulting class of the state of
degradation. New parameters such as the speed of the class, the position of the class, the
shape of the class will be discussed to inform the state of damage. The suggested
methodology is validated by analyzing several fatigue tests from a fatigue bench bearing
thrust ball referenced SNR51207
Dynamic classification method of fault indicators for bearingsâ monitoring
This paper introduces a dynamic classification method inspired by DBSCAN clustering method for machine condition monitoring in general and for bearings in particular. This method has been developed for two purposes; first to monitor the health condition of a bearing in real time and second to study the behavior of defected rolling element bearing. To fulfill those purposes, the temporal indicator RMS (Root Mean Square) has been chosen as an indicator of the bearing health condition; this indicator has been computed from signals extracted from an experimental bench by two piezoelectric sensors placed radially and axially. The decision upon the right classification method was taken after a comparative study between two classical of the clustering methods (K-means and Density Based Spatial Clustering of Applications with Noise DBSCAN), which led to the conclusion that DBSCAN is more adapted to vibratory signals. DBSCAN was re-adapted to follow any changing in bearings behavior