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During operation, the HUMS airborne segment gathers data from its sensor network. Some HUMS performs diagnosis real time in .ight, providing the pilots with instant warning of any suspected problems. However, most HUMS perform diagnosis and reporting between .ights. This is achieved by transferring the data, by means of a data cartridge, to a stationary computer. The stationary computer, known as a ground station, analyzes the recorded data and produces a discrepancy report for the maintenance crew.
This study is concerned with methods to interpret the vibratory data as
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accurately as possible. The motivation for this is twofold; increased safety and reduced maintenance cost. By improving the detection capabilities of the system, the risk of in-.ight mechanical failure is reduced. As a rotorcraft drive-train is largely non-redundant, failure can have serious consequences. Further, all HUMS, like any automated fault detection system, are prone to produce unjusti.ed alerts from time to time. This has implications both on the operational availability as well as on the maintenance cost of the ro-torcraft, as false alarms often results in unnecessary aircraft grounding and maintenance. The methods described in this study are designed to produce vibration-base diagnosis accurately as possible, so that the fault detection rate is maximized and the false alarm rate minimized. An additional objec-tive is removing any aircraft speci.c con.guration of the HUMS. The need for con.guring, or training, the HUMS for each aircraft, and retrain it after major overhauls, is a weak-spot on most commercial HUMS. This imposes a signi.cant workload on the operator, and renders the HUMS vulnerable miss-training. Both of which detract from the system’s usefulness by increased operating cost and reduced fault detection capabilities.
This report is organized in 8 chapters. Chapter 1 contains the general introduction to the subject. More detail on HUMS is given in chapter 2, with chapter 3 detailing the state of the art for the technologies deployed in a HUMS. Practical issues concerning data transfer and storage are elabo-rated in chapter 4. Chapter 5 treats validation and pre-processing of HUMS vibration data. New methods for feature extraction are developed in chapter 6, and fault detection in chapter 7. Finally, concluding remarks are presented in chapter 8. In order to keep the report as brief and clear as possible, details on mathematical tools are kept in the appendixes.
Chapter 2

Problem Statement
2.1 Background
2.1.1 History
The history of Health and Usage Monitoring System (HUMS) dates back as far as the mid eighties. At this time, it became clear that helicopters operated in the North Sea where overrepresented in the accident statistics, compared to equal size turbo prop airplanes. The UK Civil Aviation Au-thority (UK) (CAA) Helicopter Airworthiness Requirements Panel (HARP) submitted a report in 1986, concluding that the risk level in North Sea he-licopter
　
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