A Novel test bed and stochastic vibration diagnostics for assessing the condition of constant velocity joints

N/A||In this thesis, the design of a novel test bench for quickly and repeatedly testing CV joints under torque is provided along with vibration diagnostics software, developed in Matlab, for assessing the condition of a particular joint

GT2007-27862 REAL-TIME HEALTH ESTIMATION AND AUTOMATED FAULT ACCOMMODATION FOR PROPULSION SYSTEMS

ABSTRACT This paper presents the development of innovative realtime health estimation and automated fault accommodation techniques for advanced propulsion systems within a Dynamic Decision Support (D 2 S) framework. The proposed approach uses dynamic models in a real-time computing environment to not only diagnose system degradation and faults, but also to determine "on the fly" how to accommodate for them. The realtime health estimation modules enhance on-board PHM (Prognosis & Health Management) capabilities with a dynamic system identification algorithm that is capable of detecting faults with a continuously updated dynamic model. In addition, a real-time, self-tuning Kalman filter and fault classification algorithm are combined to provide accurate health estimation. Based on the inferred health condition, mission requirements and flight regime information, the automated fault accommodation module automatically makes decisions regarding control reconfiguration and change of control strategies. The presented techniques have been applied to a generic turbofan engine model with simulated engine component faults and degradation and simulation results are presented. To further raise the technological readiness level, select algorithms have been implemented and evaluated on a PC104 embedded platform. The dynamic modeling capabilities, techniques and tools sets will not only improve the reliability of the propulsion systems, but also greatly enhance maintenance decision support and contingency planning concepts

Upgrading Engine Test Cells for Improved Troubleshooting and

Abstract Upgrading military engine test cells with advanced diagnostic and troubleshooting capabilities will play a critical role in increasing aircraft availability and test cell effectiveness while simultaneously reducing engine operating and maintenance costs. Sophisticated performance and mechanical anomaly detection and fault classification algorithms utilizing thermodynamic, statistical, and empirical engine models are now being implemented as part of a United States Air Force Advanced Test Cell Upgrade Initiative. Under this program, a comprehensive set of real-time and post-test diagnostic software modules, including sensor validation algorithms, performance fault classification techniques and vibration feature analysis are being developed. An automated troubleshooting guide is also being implemented to streamline the troubleshooting process for both inexperienced and experienced technicians. This artificial intelligence based tool enhances the conventional troubleshooting tree architecture by incorporating probability of occurrence statistics to optimize the troubleshooting path. This paper describes the development and implementation of the F404 engine test cell upgrade at the Jacksonville Naval Air Station

A hybrid prognostic methodology for tidal turbine gearboxes

Tidal energy is one of promising solutions for reducing greenhouse gas emissions and it is estimated that 100 TWh of electricity could be produced every year from suitable sites around the world. Although premature gearbox failures have plagued the wind turbine industry, and considerable research efforts continue to address this challenge, tidal turbine gearboxes are expected to experience higher mechanical failure rates given they will experience higher torque and thrust forces. In order to minimize the maintenance cost and prevent unexpected failures there exists a fundamental need for prognostic tools that can reliably estimate the current health and predict the future condition of the gearbox.This paper presents a life assessment methodology for tidal turbine gearboxes which was developed with synthetic data generated using a blade element momentum theory (BEMT) model. The latter has been used extensively for performance and load modelling of tidal turbines. The prognostic model developed was validated using experimental data