Improving Aircraft Engines Prognostics and Health Management via Anticipated Model-Based Validation of Health Indicators

Abstract Electromechanical brake (EMB) is a novel braking mode for railway trains. The reliability of the braking system is important for railway system safety. According to the RAMS (Reliability, Availability, Maintainability and Safety) requirements for railway applications, the key issues of prognostics and health management (PHM) for EMB systems are discussed at first. Consequently, the dominant tasks of the PHM system are confirmed, containing the battery State-of-Charge (SOC) and State-of-Health (SOH) estimation, electric components condition monitor, and mechanical crack prediction. Then the critical failure modes of the EMB system and their failure mechanisms are analyzed. Based on the above analysis, a PHM system developed for EMB systems and its working flow are introduced. The vehicle operation parameters, the brake control commands, and the sensor signals are inputs of the PHM system. These inputs are processed and gathered as health indicators. Then the PHM system adopts the physical model or the hybrid algorithms to track the failure mode and components. Finally, the PHM system locates the health stage of the EMB system. The primary health indicators for EMB systems are the braking distance and emergency battery capacity. And the health indicators for components are mapped with the corresponding failure modes. The estimation for the battery SOC and SOH is established based on the test results of battery properties. The model-based and data-driven hybrid method is utilized to detect the crack growth of mechanical components and the degradation in electric properties. The PHM system is useful for condition-based maintenance. And it is meaningful for the reliability and safety improvement of the EMB systems.

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Prognostics and Health Management Software for Gas Turbine Engine Bearings

Volume 1: Turbo Expo 2007 ◽

10.1115/gt2007-27984 ◽

2007 ◽

Cited By ~ 2

Author(s):

Michael J. Roemer ◽

Carl S. Byington

Keyword(s):

Gas Turbine ◽

High Speed ◽

Health Management ◽

Gas Turbine Engine ◽

Remaining Useful Life ◽

Prognostics And Health Management ◽

Turbine Engine ◽

Model Based ◽

Useful Life ◽

Specific Implementation

Based on the results of a successful Phase I and II SBIR program performed by Impact Technologies, a suite of Prognostics and Health Management (PHM) algorithms have been developed for detecting incipient faults in the critical bearings associated with aircraft gas turbine engines. The component-level prognostic approach is presented that utilizes available sensor information from vibration transducers, along with material-level component fatigue models to calculate remaining useful life for the engine’s critical components. Specifically, correlation between the sensed data and fatigue-based damage accumulation models were developed to provide remaining useful life assessments for life limited components. The combination of health monitoring data and model-based techniques provides a unique and knowledge rich capability that can be utilized throughout the bearings’s entire life, using model-based estimates when no diagnostic indicators are present and using the monitored vibration features at later stages when incipient failure indications are detectable, thus reducing the uncertainty in model-based predictions. A description and specific implementation of this prognosis approach with application to high speed bearings is illustrated herein, using gas turbine engine and bearing test rig data as validation for the methods.

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