Diagnosis through bilateral membership functions and pattern recognition

International audienceOn real processes many junctional states are usually observed. However some of them may represent different significant levels (or rates) of the same functional node. They do not represent by themselves a functional mode, they are only sub-modes of the same functional mode (e.g. one of the functional mode of my car is tank empty; some functional states observed may be 10% empty tank, 50% empty tank, 96% empty tank). Thus the interest is not only to diagnose the functional mode (e.g. tank empty, sleeping driver) but also to highlight the gravity level of this functional mode (e.g. the reaction in the middle of desert, confronted to a 4% empty tank is not the same as confronted to a 98% empty tank). The aim of this paper is to present a diagnosis method based on fuzzy pattern recognition. Such a method allows the diagnosis of the current functional mode of a process and its gravity level. Usually in pattern recognition area, a membership function is a monotonic decreasing function of a Euclidean distance between two objects. Those objects represent two states of the process and a distance here is a dissimilarity measure between those states. Such a distance is defined in all the fuzzy subset associated with this membership function. So that function is monotonic decreasing in all subset directions. In this paper directional membership functions are proposed. In this case the distance is defined only by reference to a path describing the evolution from one functional state to another one. Then the obtained membership function is oriented according to this path and do not decrease identically within all directions in the subset. Such membership functions are then suitable in order to diagnose the state associated with data evoluting between known functional modes. An application to the French telephone network illustrates this method

Detection et suivi d'évolutions de l'etat d'un système complexe. Application au réseau téléphonique francais

A partir des caractéristiques du réseau téléphonique Français, un système de décision basé sur une approche reconnaissance des formes floue est proposé, afin de détecter et de suivre dans le temps l'évolution de l'état d'un système complexe. L'originalité de ce système de décision consiste à suivre cette évolution parmi toutes les combinaisons de couples de classes connues. Le système de décision proposé est construit de façon à s'appliquer à n'importe quel système complexe

Thermal signatures for pattern recognition approach applied to induction motor diagnosis

International audienc

Adaptive diagnosis by pattern recognition: Application on an induction machine

International audienceIn this paper, a pattern recognition method is used to provide the tracking and the diagnosis of a system. To illustrate it, we used as application, an asynchronous motor 5.5 kW with squirrel-cage, in particular for the detection of broken bars, under any level of load. From measurements carried out on the system, parameters are calculated. These parameters are used to build up a pattern vector which is considered as the system signature. To determine this pattern vector, two methods are applied. One, well-known, sequential backward selection (SBS) and the other, which we developed, based on a genetic approach, with the advantage to determine the optimal dimension of the representation space and to give better results (value of criterion) than SBS. The determination of the decision space is carried out using a method of automatic classification called clustering. The decision phase is based on the ldquok-nearest neighborsrdquo rule, associated with an evolution tracking of system using trajectory allowing a diagnosis not only of states defined in the training set, but also of the intermediate states. The appearance of a new operating mode is taken into account in order to enrich the initial knowledge base and thus to improve the diagnosi

Diagnostic évolutif par reconnaissance des formes. Application aux machines asynchrones

Cet article présente l'utilisation d'une méthode de reconnaissance des formes pour assurer le suivi et le diagnostic d'un système. Pour l'illustrer, nous avons utilisé comme application, un moteur asynchrone 5,5 kW à cage d'écureuil, notamment pour la détection de barres cassées, en fonction du niveau de charge. A partir de mesures réalisées sur le système, des paramètres sont calculés. Ces paramètres sont utilisés pour construire un vecteur forme qui sera considéré comme la signature du système. Pour déterminer ce vecteur forme, deux méthodes sont appliquées, l'une, assez connue, Sequential Backward Selection (SBS) et l'autre, que nous avons développé, basée sur une approche génétique, présentant l'avantage de choisir la meilleure dimension de l'espace de représentation. L'algorithme de décision est basé sur la règle des k-plus proches voisins, enrichi d'un suivi de l'évolution du système à l'aide de trajectoire permettant alors un diagnostic non seulement des états définis dans l'ensemble d'apprentissage, mais aussi des états intermédiaires. In this paper, a pattern recognition method is used to provide the tracking and the diagnosis of a system. To illustrate it, we used as application, a 5.5 kW squirrel-cage asynchronous motor, in particular for the detection of broken bars, under any level of load. From measurements carried out on the system, parameters are calculated. These parameters are used to build up a pattern vector which is considered as the system signature. To determine this pattern vector, two methods are applied. One, well-known, Sequential Backward Selection (SBS) and the other, which we developed, based on a genetic approach, with the advantage to determine the optimal dimension of the representation space. The decision phase is based on the "k-nearest neighbors" rule, associated with an evolution tracking of system using trajectory allowing a diagnosis not only of states defined in the training set, but also of the intermediate states

Use of data standardization to improve inverter - induction machine fault detection

International audiencentensive research efforts have been focused on the signature analysis (SA) to detect electrical and mechanical fault condition of induction machines. Different signals can be used: voltage, current and flux. The characteristic frequency research by a current spectral analysis is a well-known method widely used. This method is valid when the motor is supplied by the three-phase main network. However nowadays, in industrials applications, the asynchronous motors are more and more supplied by converters, in particular for variable speed. The current spectral analysis is almost not exploitable because of appearance of multiple harmonics of the commutation frequency. This paper presents a diagnosis method applied to a set "converter-machine-load". This method is based on pattern recognition approach. The use of the data standardization makes it possible to free from the level of load and thus to represent an operating mode by only one class. This fact allows decreasing the number of initial data necessary to the training phase and improving the final diagnosi

Diagnosis System of a Set "Converter-Induction Machine-Load" by Pattern Recognition Approach

International audienc

Diagnosis by pattern recognition for PMSM used in more electric aircraft

International audiencePresently, condition monitoring and fault diagnostics in electric drives are essential to optimize maintenance operations and increase reliability levels. This paper presents a diagnosis method for electrical and mechanical faults detection. This method combines a detection method based on expertise with a pattern recognition approach so as to detect different faults appearing on the system but also to classify their origins and their severity by reference to an initial data base. In order to prove reliability and efficiency of this method, experimental results are presented using a permanent magnet synchronous motor (PMSM) drive

Un système adaptatif de diagnostic et de suivi d'évolution. Application aux machines asynchrones

International audienceThis paper deals with the use of pattern recognition method in order to provide thetracking and the diagnosis of an electrical system. From the measurements carried out onthe system, some parameters are calculated. These ones are used to build up the systemsignature. As the set of parameters is not necessarily relevant, a selection method based on acompacity/separability criterion is used. The determination of the decision space is carriedout by using an automatic classification method named clustering. Two decision methodsare used and compared: the “k-nearest neighbors” rule (k-ppv), easy to implement butpenalizing in term of computing time. The other method that we developed is based on aprogressive grid of the representation space. The appearance of a new operating mode istaken into account in order to enrich the initial knowledge base and thus to improve thediagnosis. To illustrate this method, we used as an application, an asynchronous motor 5.5kW with squirrel-cage supplied with a voltage converter for the detection of rotor and statorfaults.Cet article présente le suivi et le diagnostic d’un système réalisé à partir d’uneméthode de reconnaissance des formes. A partir des mesures effectuées sur le système, desparamètres sont calculés. Ces derniers sont utilisés pour construire la signature du système.L’ensemble des paramètres n’étant pas nécessairement pertinent, une méthode de sélectionbasée sur un critère de compacité/séparabilité est utilisée. La détermination de l’espace dedécision est réalisée de manière automatique à l’aide d’une méthode appelée« coalescence ». Deux méthodes de décision sont utilisées et comparées : la règle des casplus proches voisins (k-ppv), facile à mettre en œuvre mais pénalisante en terme de temps decalcul, l’autre que nous avons développé et qui s’appuie sur un maillage progressif del’espace de représentation. L’apparition d’un nouveau mode de fonctionnement est pris encompte afin d’enrichir la base de connaissance initiale et d’améliorer ainsi le diagnostic.Pour illustrer cette méthode, nous avons utilisé comme application, un moteur asynchrone5.5 kW à cage d’écureuil alimenté par un onduleur de tension pour la détection des défautsaussi bien au rotor qu’au stator

A method to detect broken bars in induction machine using pattern recognition techniques

International audienceIn this paper, a pattern recognition (PR) method is used to provide the tracking and the diagnosis of a system. First of all, from measurements carried out on the system, features are extracted from current and voltage measurements without any other sensors. These features are used to build up a pattern vector, which is considered as the system signature. Then, a feature selection method is applied in order to select the most relevant features, which define the representation space. The decision phase is based on the "k-nearest neighbors" (knn) rule, associated with an evolution tracking of system using trajectory allowing a diagnosis not only of states defined in the training set, but also of the intermediate states. The appearance of a new operating mode is taken into account in order to enrich the initial knowledge base and thus to improve the diagnosis. This approach is illustrated on asynchronous motor of 5.5 kW with squirrel cage, in order to detect broken bars under any load level. The experimental results prove the efficiency of PR methods in condition monitoring of electrical machines