Today’s modern aircraft have strict Prognostics and Health Management (PHM) requirements for each module and line replaceable component (LRC). Despite these requirements, there is limited on-board processing capability for health monitoring of even the major safety critical engine components. Additionally, loss of central processing and monitoring may lead to loss of the functionality or even the aircraft. Therefore, instead of locating all component PHM algorithms in a single box (i.e., FADEC), the authors have developed a ‘Distributed PHM’ (DPHM) architecture that distributes the PHM algorithms onto processing hardware that is embedded within the component. This approach would allow the component to be aware of its own current and future health state by monitoring and processing its own sensor data. Furthermore, aircraft engine distributed control systems (DCS) offer numerous advantages to the aviation industry, including: weight reduction through simplified power harness and minimization of a centralized controller, a faster and cheaper certification process, and implementation of advanced multivariable controls and active component control that will maximize the performance and efficiency of the engine. Integration of the DPHM and DCS philosophies would result in self-diagnosing components with integrated control capabilities that would enable concepts such as adaptive fault tolerant control. This distributed, nodal approach would greatly reduce the aircraft level data communication and process burden. Instead of needing to transfer and process high bandwidth data, the aircraft level computer simply receives low bandwidth health indicators from all smart components. As such, this solution has the potential to address some of the processing limitations experienced by current ‘Centralized’ PHM and control approaches. Key to this integration is the implementation of an open-systems architecture that would allow seamless integration of components manufactured by different vendors into a common architecture. In this work, the authors explore the implementation of such an approach on engine accessory components.
[1]
O. V. Lazorenko.
Ultrawideband Signals and Choi-Williams Transform
,
2006,
2006 3rd International Conference on Ultrawideband and Ultrashort Impulse Signals.
[2]
T.G. Habetler,et al.
Nonstationary Motor Fault Detection Using Recent Quadratic Time–Frequency Representations
,
2006,
IEEE Transactions on Industry Applications.
[3]
Alireza Behbahani,et al.
Communication Needs Assessment for Distributed Turbine Engine Control
,
2008
.
[4]
Khaled H. Hamed,et al.
Time-frequency analysis
,
2003
.
[5]
Rohit K. Belapurkar,et al.
Stability Analysis of Distributed Engine Control Systems Under Communication Packet Drop (Postprint)
,
2008
.
[6]
P. D. McFadden,et al.
APPLICATION OF WAVELETS TO GEARBOX VIBRATION SIGNALS FOR FAULT DETECTION
,
1996
.
[7]
Y. Gao,et al.
A Comparison of Time-frequency Distributions Applied to Bearing Diagnostics
,
1996
.
[8]
Jonathan A. DeCastro,et al.
Analysis of Decentralization and Fault-Tolerance Concepts for Distributed Engine Control
,
2009
.
[9]
Phillip L. Shaffer.
Distributed Control System for Turbine Engines
,
1999
.
[10]
Rohit K. Belapurkar,et al.
Stability of Fiber Optic Networked Decentralized Distributed Engine Control under Time Delays
,
2009
.
[11]
A Bruce Richter,et al.
AIRCRAFT TURBINE ENGINE RELIABILITY AND INSPECTION INVESTIGATIONS.
,
1993
.