曝光臺 注意防騙 網曝天貓店富美金盛家居專營店坑蒙拐騙欺詐消費者

To avoid the current problems of individual indicator thresholds for each aircraft, two methods for indicator trend analysis were developed. These methods start by calculating traditional indicators from a batch of vibration acquisitions. Once an indicator series is obtained, trend analysis is used for analyzing how the indicator series behaves over time. The .rst method uses a .exible parametric model to approximate indicator behavior over time. Indicator behavior along the time line is then identi.ed by evaluating the .rst derivative of this model. The second method applies a set of wavelet .lter banks to the indicators separating regions representing maintenance actions, normality and mechanical degradation. The wavelet coe.cients are then passed through a threshold system or a radial basis network to .ag regions of maintenance actions and mechanical degradation.
Finally, a framework for HUMS data migration is developed. This frame-work is designed to facilitate transport of data between the operator and the HUMS OEM, and to help the HUMS OEM fuse together data recorded by di.erent systems. The data migration framework is completed with a sys-tem for online registration of HUMS alarms and mechanical problems. This facilitates the communication between the operator and the HUMS OEM customer support, and greatly improves the response time for customer sup-port as well as reducing operator workload.
Eurocopter France owns a patent, currently pending, on the work con-cerning contextual normalization and non-parametric trend analysis.
Chapter 4

Data Migration
4.1 Introduction
For the continued evolution of the Health and Usage Monitoring Systems, it is of vital importance to be able to aggregate the experience already obtained by the systems currently in service. For an airframe OEM, this involves collecting data at regular intervals from all of its .eet, typically involving multiple HUMS models and versions produced by delivered HUMS OEMs. Collecting and fusing data from di.erent HUMS models and versions poses several technical challenges, as each system organizes its data storage in di.erent ways. This chapter presents a data handling system which has been developed as part of this PhD study. The data handling system is designed to fuse the data from di.erent systems and system versions into a common database, so that this data can be accessed through a single interface. Such a tool is essential for extracting and aggregating the experience obtained through
all
HUMS
solutions
currently
and
formerly
in
use
[49].

4.2 Analysis Process
Although each HUMS solution on the market does things slightly di.erent, the
overall
process
is
more
or
less
the
same
(Fig.
4.1).
This
includes
the
datatypes that are recorded or derived by the HUMS. The fundamental data sources for usage monitoring are the .ight data parameters, i.e. informa-tion like airspeed, altitude and engine torque. These are used for generating event markers like engine overheat and rotor overspeed. The usage moni-toring function also calculates usage cycles, and generates event markers for components reaching their retirement age in terms of accumulated cycles. Each marker is stored with the contextual information relevant to its type.
　
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