GARTEUR Helicopter Cooperative Research

This paper starts with an overview about the general structure of the Group for Aeronautical Research and Technology in EURope (GARTEUR). The focus is on the activities related to rotorcraft which are managed in the GARTEUR Helicopter Group of Responsables (HC GoR). The research activities are carried out in so-called Action Groups. Out of the 5 Action Groups which ended within the last four years results generated in the Helicopter Action Groups HC(AG14) “Methods for Refinement of Structural Dynamic Finite Element Models”, HC(AG15) “Improvement of SPH methods for application to helicopter ditching” and HC(AG16) “Rigid Body and Aeroelastic Rotorcraft-Pilot Coupling” are briefly summarized

Rotorcraft Health Management Issues and Challenges

This paper presents an overview of health management issues and challenges that are specific to rotorcraft. Rotorcraft form a unique subset of air vehicles in that their propulsion system is used not only for propulsion, but also serves as the primary source of lift and maneuvering of the vehicle. No other air vehicle relies on the propulsion system to provide these functions through a transmission system with single critical load paths without duplication or redundancy. As such, health management of the power train is a critical and unique part of any rotorcraft health management system. This paper focuses specifically on the issues and challenges related to the dynamic mechanical components in the main power train. This includes the transmission and main rotor mechanisms. This paper will review standard practices used for rotorcraft health management, lessons learned from fielded trials, and future challenges

A Survey of Current Rotorcraft Propulsion Health Monitoring Technologies

A brief review is presented on the state-of-the-art in rotorcraft engine health monitoring technologies including summaries on current practices in the area of sensors, data acquisition, monitoring and analysis. Also, presented are guidelines for verification and validation of Health Usage Monitoring System (HUMS) and specifically for maintenance credits to extend part life. Finally, a number of new efforts in HUMS are summarized as well as lessons learned and future challenges. In particular, gaps are identified to supporting maintenance credits to extend rotorcraft engine part life. A number of data sources were consulted and include results from a survey from the HUMS community, Society of Automotive Engineers (SAE) documents, American Helicopter Society (AHS) papers, as well as references from Defence Science & Technology Organization (DSTO), Civil Aviation Authority (CAA), and Federal Aviation Administration (FAA)

Application of Kalman Filtering to Real-time Flight Regime Recognition Algorithms in a Helicopter Health and Usage Monitoring System

The purpose of this study is the application of Kalman filters to real-time Flight Regime Recognition (FRR) algorithms to identify the regime flown and observe transitions between flight regimes. Rotor fault identification, a technique that is somewhat similar to flight regime recognition, successfully used Kalman filters to determine fault types and damage locations. Recently developed FRR algorithms successfully applied Hidden Markov Models, which are similar to Kalman filters. The selected regime set for this study derives from a study performed by Bell Helicopter Textron, Inc. The selected parameter set for this study is modified from the Schweizer 300 Flight Test Program performed by Embry-Riddle Aeronautical University. The FRR algorithms developed will use the recorded flight parameters to identify a flight regime. A graphical interface allows the user to observe the real-time FRR and transitions between regimes. This research aims to bridge the gap between the application of mathematical models for damage identification and regime recognition. Multiple mathematical models developed for rotor blade fault and damage identification include neural networks, fuzzy logic systems, and Kalman filters. Recent research indicates that only the neural network approach has been applied to FRR algorithms, and that a Hidden Markov Model (HMM) approach outperformed the neural network. Additionally, public domain regime recognition research focuses on post processing algorithms rather than real-time regime recognition. The post processing codes appear to use discrete algorithms, which do not clearly identify transitions between regimes

Investigation of Current Methods to Identify Helicopter Gear Health

This paper provides an overview of current vibration methods used to identify the health of helicopter transmission gears. The gears are critical to the transmission system that provides propulsion, lift and maneuvering of the helicopter. This paper reviews techniques used to process vibration data to calculate conditions indicators (CI's), guidelines used by the government aviation authorities in developing and certifying the Health and Usage Monitoring System (HUMS), condition and health indicators used in commercial HUMS, and different methods used to set thresholds to detect damage. Initial assessment of a method to set thresholds for vibration based condition indicators applied to flight and test rig data by evaluating differences in distributions between comparable transmissions are also discussed. Gear condition indicator FM4 values are compared on an OH58 helicopter during 14 maneuvers and an OH58 transmission test stand during crack propagation tests. Preliminary results show the distributions between healthy helicopter and rig data are comparable and distributions between healthy and damaged gears show significant differences

Current Research Activities in Drive System Technology in Support of the NASA Rotorcraft Program

Drive system technology is a key area for improving rotorcraft performance, noise/vibration reduction, and reducing operational and manufacturing costs. An overview of current research areas that support the NASA Rotorcraft Program will be provided. Work in drive system technology is mainly focused within three research areas: advanced components, thermal behavior/emergency lubrication system operation, and diagnostics/prognostics (also known as Health and Usage Monitoring Systems (HUMS)). Current research activities in each of these activities will be presented. Also, an overview of the conceptual drive system requirements and possible arrangements for the Heavy Lift Rotorcraft program will be reviewed

Validation of Helicopter Gear Condition Indicators Using Seeded Fault Tests

A "seeded fault test" in support of a rotorcraft condition based maintenance program (CBM), is an experiment in which a component is tested with a known fault while health monitoring data is collected. These tests are performed at operating conditions comparable to operating conditions the component would be exposed to while installed on the aircraft. Performance of seeded fault tests is one method used to provide evidence that a Health Usage Monitoring System (HUMS) can replace current maintenance practices required for aircraft airworthiness. Actual in-service experience of the HUMS detecting a component fault is another validation method. This paper will discuss a hybrid validation approach that combines in service-data with seeded fault tests. For this approach, existing in-service HUMS flight data from a naturally occurring component fault will be used to define a component seeded fault test. An example, using spiral bevel gears as the targeted component, will be presented. Since the U.S. Army has begun to develop standards for using seeded fault tests for HUMS validation, the hybrid approach will be mapped to the steps defined within their Aeronautical Design Standard Handbook for CBM. This paper will step through their defined processes, and identify additional steps that may be required when using component test rig fault tests to demonstrate helicopter CI performance. The discussion within this paper will provide the reader with a better appreciation for the challenges faced when defining a seeded fault test for HUMS validation

Integrating Condition Indicators and Usage Parameters for Improved Spiral Bevel Gear Health Monitoring

The objective of this study was to illustrate the importance of combining Health Usage Monitoring Systems (HUMS) data with usage monitoring system data when detecting rotorcraft transmission health. Three gear sets were tested in the NASA Glenn Spiral Bevel Gear Fatigue Rig. Damage was initiated and progressed on the gear and pinion teeth. Damage progression was measured by debris generation and documented with inspection photos at varying torque values. A contact fatigue analysis was applied to the gear design indicating the effect temperature, load and reliability had on gear life. Results of this study illustrated the benefits of combining HUMS data and actual usage data to indicate progression of damage for spiral bevel gears