Bearing Health monitoring based on Hilbert-Huang Transform, Support Vector Machine and Regression.

International audienceThe detection, diagnostic and prognostic of bearing degradation play a key role in increasing the reliability and safety of electrical machines especially in key industrial sectors. This paper presents a new approach which combines the Hilbert-Huang transform, the support vector machine and the support vector regression for the monitoring of ball bearings. The proposed approach uses the Hilbert-Huang transform to extract new heath indicators from stationary/non-stationary vibration signals able to tack the degradation of the critical components of bearings. The degradation states are detected by a supervised classification technique called support vector machine and the fault diagnostic is given by analyzing the extracted health indicators. The estimation of the remaining useful life is obtained by a one-step time series prediction based on support vector regression. A set of experimental data collected from degraded bearings is used to validate the proposed approach. Experimental results show that the use of the Hilbert-Huang transform, the support vector machine and the support vector regression is a suitable strategy to improve the detection, diagnostic and prognostic of bearing degradation

Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals

Gear reducer motors play an important role in industry due to their robustness and simplicity of construction. However, the appearance of faults in these systems can affect the quality of the product and lead to significant financial losses. Therefore, it is necessary to perform Prognostics and Health Management (PHM) for these systems. This paper aims to develop a practical and effective method allowing an early fault detection and diagnostic for critical components of the gear reducer, in particular gear and bearing defects. This method is based on a new indicator extracted from electrical signals. It allows characterizing different states of the gear reducer, such as healthy state, bearing faults, gear faults, and combined faults. The diagnostic of these states is done by the Adaptive Neuro-Fuzzy Inference System (ANFIS). The efficiency and the robustness of the proposed method are highlighted through numerous experimental tests with different levels of loads and speeds

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

From the diagnosis to the prognosis of faults in electrical drives

Le diagnostic et le pronostic de pannes des systèmes d'entraînement électriques est un enjeu majeur pour assurer la fiabilité et la sûreté de fonctionnement des outils de production notamment dans les secteurs sensibles (militaire, l'aéronautique, l'aérospatiale et nucléaire, etc.). Le travail de recherche présenté dans cette thèse vise à introduire de nouvelles méthodes de diagnostic et de pronostic des défauts d'une machine asynchrone ainsi que des roulements à rouleaux. Ces méthodes, orientées données, utilisent les données de mesure recueillies à partir de capteurs placés sur un système (machine asynchrone, roulement à rouleaux) afin de construire un vecteur de paramètres indicateur de défaut. Les méthodes de classification développées (supervisée, non supervisée) permettent de classer les observations, décrites par le vecteur de paramètres, par rapport aux différents modes de fonctionnement, connus ou inconnus du système, avec ou sans défauts. Des défauts ont été créés au rotor et aux roulements de la machine asynchrone, alimentée par le biais d'un onduleur de tension. La classification non supervisée, basée sur l'algorithme des fourmis artificielles, permet d'analyser les observations inconnues et inexplorées afin de mettre en évidence les classes regroupant les observations similaires. Cela permet d'améliorer la classification et de détecter l'apparition de nouveaux modes de fonctionnement. La classification supervisée, basée sur les modèles de Markov cachés, permet d'associer un degré d'appartenance (sous forme d'une probabilité) lors de l'affectation d'une observation à une ou plusieurs classes. Cela permet de définir un indice de fiabilité à l'affectation réalisée mais aussi de détecter l'apparition de nouveaux modes de fonctionnement. Ces méthodes ne se limitent pas qu'à diagnostiquer les défauts, elles peuvent aussi contribuer au pronostic des défauts. En effet, le pronostic peut être défini comme une extension du problème de diagnostic. La prédiction d'un défaut est réalisée par trois méthodes basées sur les modèles de Markov cachés pour la détection de l'imminence d'un défaut ainsi que par deux méthodes basées sur le système neuro-flou (ANFIS pour Adaptive Neuro Fuzzy Inference System et le neurone neuro-flou) pour estimer le temps restant avant son apparition. Des données de vieillissement d'un ensemble de roulements à rouleaux ont été utilisées afin de tester les méthodes proposées. Les résultats obtenus montrent l'efficacité de ces méthodes pour le diagnostic et le pronostic des défauts dans les entraînements électriquesFaults diagnosis and prognosis of electrical drives play a key role in the reliability and safety of production tools especially in key sectors (military, aviation, aerospace and nuclear, etc.). The research presented in this thesis aims to introduce new methods for faults diagnosis and prognosis of an induction motors and roller bearings. These methods use measured data collected from sensors placed on the system (induction motor, roller) in order to construct a feature vector which indicates the state of the system. Supervised and unsupervised classification methods are developed to classify measurements (observations) described by the feature vector compared to known or unknown operating modes, with or without failures. Defects were created in the rotor and the bearing of the induction motor, fed by a voltage inverter. The unsupervised classification technique, based on artificial ant-clustering, allows analyzing the unknown and unexplored observations to highlight classes with similar observations. This allows improving the classification and the detection of new operating modes. The supervised classification, based on hidden Markov models, allows associating a degree of similarity when we affect an observation to one or more classes. This defines a reliability index which allows the detection of new operating modes. These methods are not limited to diagnose faults; they can also contribute to the prognosis of faults. Indeed, the prognosis can be defined as an extension of the problem of diagnosis. The prognosis of faults is carried out by three methods based on hidden Markov models for the detection of an impending failure and by two methods based on the neuro-fuzzy system (ANFIS for Adaptive Neuro fuzzy Inference System and the neo-fuzzy neuron) to estimate the remaining time before its appearance. A set of historical data collected on roller bearings is used to validate the proposed methods. The obtained results show the effectiveness of the proposed methods for faults diagnosis and prognosis of electrical drive

Du diagnostic au pronostic de pannes des entraînements électriques

Faults diagnosis and prognosis of electrical drives play a key role in the reliability and safety of production tools especially in key sectors (military, aviation, aerospace and nuclear, etc.). The research presented in this thesis aims to introduce new methods for faults diagnosis and prognosis of an induction motors and roller bearings. These methods use measured data collected from sensors placed on the system (induction motor, roller) in order to construct a feature vector which indicates the state of the system. Supervised and unsupervised classification methods are developed to classify measurements (observations) described by the feature vector compared to known or unknown operating modes, with or without failures. Defects were created in the rotor and the bearing of the induction motor, fed by a voltage inverter. The unsupervised classification technique, based on artificial ant-clustering, allows analyzing the unknown and unexplored observations to highlight classes with similar observations. This allows improving the classification and the detection of new operating modes. The supervised classification, based on hidden Markov models, allows associating a degree of similarity when we affect an observation to one or more classes. This defines a reliability index which allows the detection of new operating modes. These methods are not limited to diagnose faults; they can also contribute to the prognosis of faults. Indeed, the prognosis can be defined as an extension of the problem of diagnosis. The prognosis of faults is carried out by three methods based on hidden Markov models for the detection of an impending failure and by two methods based on the neuro-fuzzy system (ANFIS for Adaptive Neuro fuzzy Inference System and the neo-fuzzy neuron) to estimate the remaining time before its appearance. A set of historical data collected on roller bearings is used to validate the proposed methods. The obtained results show the effectiveness of the proposed methods for faults diagnosis and prognosis of electrical drivesLe diagnostic et le pronostic de pannes des systèmes d'entraînement électriques est un enjeu majeur pour assurer la fiabilité et la sûreté de fonctionnement des outils de production notamment dans les secteurs sensibles (militaire, l'aéronautique, l'aérospatiale et nucléaire, etc.). Le travail de recherche présenté dans cette thèse vise à introduire de nouvelles méthodes de diagnostic et de pronostic des défauts d'une machine asynchrone ainsi que des roulements à rouleaux. Ces méthodes, orientées données, utilisent les données de mesure recueillies à partir de capteurs placés sur un système (machine asynchrone, roulement à rouleaux) afin de construire un vecteur de paramètres indicateur de défaut. Les méthodes de classification développées (supervisée, non supervisée) permettent de classer les observations, décrites par le vecteur de paramètres, par rapport aux différents modes de fonctionnement, connus ou inconnus du système, avec ou sans défauts. Des défauts ont été créés au rotor et aux roulements de la machine asynchrone, alimentée par le biais d'un onduleur de tension. La classification non supervisée, basée sur l'algorithme des fourmis artificielles, permet d'analyser les observations inconnues et inexplorées afin de mettre en évidence les classes regroupant les observations similaires. Cela permet d'améliorer la classification et de détecter l'apparition de nouveaux modes de fonctionnement. La classification supervisée, basée sur les modèles de Markov cachés, permet d'associer un degré d'appartenance (sous forme d'une probabilité) lors de l'affectation d'une observation à une ou plusieurs classes. Cela permet de définir un indice de fiabilité à l'affectation réalisée mais aussi de détecter l'apparition de nouveaux modes de fonctionnement. Ces méthodes ne se limitent pas qu'à diagnostiquer les défauts, elles peuvent aussi contribuer au pronostic des défauts. En effet, le pronostic peut être défini comme une extension du problème de diagnostic. La prédiction d'un défaut est réalisée par trois méthodes basées sur les modèles de Markov cachés pour la détection de l'imminence d'un défaut ainsi que par deux méthodes basées sur le système neuro-flou (ANFIS pour Adaptive Neuro Fuzzy Inference System et le neurone neuro-flou) pour estimer le temps restant avant son apparition. Des données de vieillissement d'un ensemble de roulements à rouleaux ont été utilisées afin de tester les méthodes proposées. Les résultats obtenus montrent l'efficacité de ces méthodes pour le diagnostic et le pronostic des défauts dans les entraînements électrique

Diagnostic Methods for the Health Monitoring of Gearboxes

International audienc

Pronostic and health managment: state of art on the monitoring of gears and bearing

International audienc

DescriptionMethods of diagnosis and prognosis play a key role in the reliability and safety of industrial systems.Failure diagnosis requires the use of suitable sensors, which provide signals that are processed to monitor features (health indicators) for defects. These features are required to distinguish between operating states, in order to inform the operator of the severity level, or even the type, of a failure.Prognosis is defined as the estimation of a system’s lifespan, including how long remains and how long has passed. It also encompasses the prediction of impending failures. This is a challenge that many researchers are currently trying to address.Electrical Systems, a book in two volumes, informs readers of the theoretical solutions to this problem, and the results obtained in several laboratories in France, Spain and further afield. To this end, many researchers from the scientific community have contributed to this book to share their research results.Contents1. Diagnosis of Electrical Machines by External Field Measurement, Remus Pusca, Eric Lefevre, David Mercier, Raphael Romary and Miftah Irhoumah.2. Signal Processing Techniques for Transient Fault Diagnosis, José Alfonso Antoino Daviu and Roque Alfredo Osornio Rios.3. Accurate Stator Fault Detection in an Induction Motor Using the Symmetrical Current Components, Monia Bouzid and Gérard Champenois.4. Bearing Fault Diagnosis in Rotating Machines, Claude Delpha, Demba Diallo, Jinane Harmouche,Mohamed Benzoubid, Yassine Amirat and Elhoussin Elbouchikhi.5. Diagnosis and Prognosis of Proton Exchange Membrane Fuel Cells, Zhongliang Li, Zhixue Zheng and Fei Gao.

International audienc

Techniques for Predicting Defects in Bearings and Gears

International audienc

Detection and Diagnostics of Bearing and Gear Fault under Variable Speed and Load Conditions Using Heterogeneous Signals

International audienceIn industrial applications, rotating machines operate under real-time variable speed and load regimes. In the presence of faults, the degradation of critical components is accelerated significantly. Therefore, robust monitoring algorithms able to identify these faults become crucial. In the literature, it is hard to find comprehensive monitoring systems that include variable speed and load regimes with combined gearbox faults using electrical and vibration signals. For this purpose, a novel signal processing methodology including a geometric classification technique is proposed. This methodology is based on using different types of sensors such as current, voltage and vibration sensors with a regime normalization, which allows the grouping of different regimes belonging to the same health state. It consists of reducing dispersion between the class observations and separating other classes representing different health states including the variation in speed and load. Then, a peripheral threshold is proposed in our classifier to diagnose new health states. To verify the effectiveness of the methodology, current, voltage and vibration data from a gearbox system are collected under variable speed and load levels