A.4
NonlinearOptimization......................134
A.4.1
TrustRegion
.......................134
A.4.2
EvolutionaryOptimization................135
A.5
Classi.cationSystems
......................136
B
IT
Notations
B.1
Databases
.............................139
B.2
ObjectOrientedProgramming..................140
B.2.1
InterfaceProgramming..................140
B.2.2
Java
............................141
B.2.3
ComponentObjectModel
................141
B.2.4
.net.............................143
Acronyms
ARMS Aircraft Recording and Monitoring System CAA Civil Aviation Authority (UK) CBM Condition Based Maintenance CG Center of Gravity COTS Commercial O. The Shelf CVR Cockpit Voice Reorder DFT Discrete Fourier Transform EuroARMS Eurocopter Aircraft Recording and Monitoring System EVM Engine Vibration Monitoring FAA Federal Aviation Authority (USA) FDR Flight Data Recorder HARP Helicopter Airworthiness Requirements Panel HUMS Health and Usage Monitoring System IAS Indicated Airspeed IGB Intermediate Gear Box IT Information Technology KTS Knots LPC Linear Predictive Coding LCC Life Cycle Cost
9
MARMS Modular Aircraft Recording and Monitoring System MGB Main Gear Box MMH/FH Mean Man Hours / Flight Hours (maintenance) MTBF Mean Time Between Failure NF Engine turbine speed NG Engine generator speed OEM Original Equipment Manufacturer PAC Power Assurance Check RTB Rotor Track and Balance SHL Steward Hughes Limited TBM Time Based Maintenance TBO Time Between Overhaul TDS Tail Drive Shaft TGB Tail Gear Box VMS Vibration Monitoring System VPN Virtual Private Network
Chapter 1
Introduction
Increasing demand for both reduced rotorcraft maintenance cost and im-proved operational safety has paved the way for the Health and usage Mon-itoring System (HUMS). These systems emerged in the early nineties as a response to the relatively high accident rate experienced by o.shore shuttle helicopters tra.cking the petrol installations in the North Sea. However, it soon became clear that these systems, in addition to reducing accident rates, had a potential for maintenance cost reduction. Research and development into HUMS technologies over the years has kept a focus on both aspects by working toward better safety. At the same time, e.orts have been made to exploit the increased situation awareness given by the HUMS in order to help the operators better organize their maintenance tasks.
A HUMS deploys both proactive and reactive methods to anticipate drive-train failure. Proactive methods include usage spectrum analysis such as load cycle calculation, allowing remaining component safe life to be estimated based on the actual stress a component has been under for the duration of its service. The reactive approach is based on detecting propagating component failure at an early stage, before seizure occurs. This method relies on a sensor network covering engines and transmission system. For the current generation HUMS, this sensor network is mainly limited to vibration sensors and angular shaft speed sensors.
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