4.6 Bene.ts
This data storage facility is meant to bene.t both researchers and support personnel. The key advantage for support personnel is the speed and .exibil-
4.7. CONCLUSION
ity of the discrepancy reporting system, allowing faster response to operator requests. This is supplemented by the common interface which gives instant access operators’ HUMS data. Instant access means that support personnel can view an operator’s HUMS data directly, rather than explicitly requesting the operator to send the necessary data (Fig. 4.6).
This
results
in
reduced
workload for the operator and shorter response time for support personnel. An additional bene.t is that the periodic data transfer from operator to the HUMS OEM no longer requires the intervention of the operator, again reducing operator workload.

The motivation for providing researchers with this tool is, as already men-tioned, to facilitate research into the correlation between vibration signatures and drive-train failure modes. Further, the discrepancy database permits data mining operations like calculating fault detection frequencies and false alarm frequencies. Such frequencies can be calculated across indicators, com-ponents and rotorcraft models. This helps the HUMS OEM gain a better understanding of the e.ectiveness of each HUMS function, so that research can be focused on the most signi.cant weak-points. It also gives support services a better situation awareness concerning problem distribution across operators, aircraft and aircraft models.

4.7 Conclusion
Most in-service di.culties associated with Health and Usage Monitoring Sys-tems can be attributed to their complexity. The number of components mon-itored and the number of failure modes these systems are designed to detect are immense. This makes it highly challenging to monitor the performance and reliability of each sub-function, and poses the risk of HUMS OEMs be-ing buried in discrepancy reports and raw HUMS data without being able to extract the knowledge contained within.
The data migration solutions developed in this thesis are an attempt to counter these problems by aiding the HUMS OEM in organizing the incoming data, and extracting its essence. This is done by addressing the problem of accumulating data extracted from di.erent systems, correlating this data with discrepancy logs, and exporting it to third party numerical analysis tools. Although the two latter points are already addressed by certain OEMs in some form or another, data fusion across systems is an area which so far has received little attention. This is however a vital point, given the number of solutions in service today. For the continued evolutions of HUMS, it is essential to be able to exploit the knowledge accumulated by older systems when developing the next generation technology.
The tradeo.s from better data handling are both short and long term. On a short horizon, these methods provide better support services for the HUMS OEM by reducing response time for the support personnel as well as reducing operator workload. In the long run, better data handling will result in better situation awareness for the HUMS OEM, making it easier to adapt the systems to customer demand.
　
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