Simulation et analyse paramétrique de méthodes de prise de décision dans le cadre de la maintenance conditionnelle

Conception des modèles de simulation des méthodes de prise de décision -- Choix des niveaux des paramètres et plan d'expériences -- Analyse statistique des effets du changement des niveaux des paramètres sur les erreurs des méthodes de prise de décision

Développement d'une méthodologie statistique pour la détection de l'usure d'un outil avec des cartes de contrôle

Cas unidimensionnel -- Détermination des observations qui serviront pour construire les cartes de contrôle -- Traitement de la corrélation -- Construction des cartes de contrôle -- Détection de l'usure -- Cas multidimensionnel -- Cartes de contrôle multidimensionnelles -- Préparation des données -- Carte de contrôle de hotelling -- Carte de contrôle pour la dispersion, n=1 -- Résultats sur la détection de l'usure

Role of On-Board Sensors in Remaining Life Prognostic Algorithm Development for Selected Assemblies as Input to a Health and Usage Monitoring System for Military Ground Vehicles

Improved reliability of military ground vehicle systems is often in direct conflict with increased functionality and performance. Health and Usage Monitoring Systems or HUMS are being developed to address this issue. HUMS can be practically defined as a system of sensors, processors and algorithms that give an indication of remaining component life. Fatigue of metal components is a common failure mode on military vehicles, and failures of this type have a major effect on vehicle reliability and availability. The purpose of this research is to develop the methods and algorithms necessary for applying HUMS and remaining life prognostics to metal fatigue on a military wheeled vehicle. A range of models were developed and fidelity of the models was shown to be correlated with computational complexity. Simplistic models based on feature recognition had the least potential for accurate fatigue damage predictions while high fidelity physics-based models had the most potential. Recommendations for the information needed to select the most appropriate model for a component and optimize the effect on vehicle reliability and availability were discussed. Methods for identifying the set of instrumentation that could reasonably be used as part of a HUMS and techniques for selecting the instrumentation that provides inputs for metal fatigue damage models were evaluated. Techniques for identifying critical data and instrumentation were also described. The methods and algorithms developed were demonstrated for a variety of components on a military wheeled vehicle, and validation was performed by comparing the results of the remaining life prognostics with those from high fidelity physics of failure models. The processes developed could be easily adapted to other platforms including commercial fleets of vehicles or aircraft. These algorithms and techniques provide potential for improving reliability and availability, but it should be noted that other methods may be more appropriate depending on the specific vehicle and failure mode. Significant work remains to implement HUMS technologies on a military wheeled vehicle, but increasing reliability and availability is a worthy goal

A statistical monitoring approach for automotive on-board diagnostic systems

The current generation of vehicle models are increasingly being equipped with on-board diagnostic (OBD) systems aimed at assessing the ‘state of health’ of important anti-pollution subsystems and components. In order to promptly diagnose and fix quality and reliability problems that may potentially affect such complex diagnostic systems, even during advanced development prior to mass production, some vehicle prototypes undergo a testing phase under realistic conditions of use (a mileage accumulation campaign). The aim of this work is to set up a statistical tool for improving the reliability of the OBD system by monitoring its operation during the mileage accumulation campaign of a new vehicle model. A dedicated software program was developed by the authors to filter the large experimental database recorded during the mileage accumulation campaign and to extract the time series of the diagnostic indices to be analysed. A model-based monitoring approach, using continuous time autoregressive (CAR) models for the time-series structure and traditional control charts for the estimated residuals, is adopted. A Kalman recursion procedure for the estimation of the unknown CAR model parameters is described. An application of the proposed approach is presented for a diagnostic index related to the state of health of the oxygen sensor. Copyright © 2006 John Wiley & Sons, Ltd