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A method better suited for detecting these failure modes is Acoustic Emis-sion (AE) monitoring. Acoustic emissions are ultrasonic energy emissions created in response to metal-to-metal contact and metal deformation. This information is normally recorded through acoustic sensors or wide band ac-celerometers, in an asynchronous manner. AE monitoring systems are very sensitive to early signs of gear / bearing failure, mainly metal deformation and direct metal-to-metal contact. Thus, it should have the properties nec-essary to detect both bearing corrosion and gear fretting. In addition, this technology might be able to detect fretting between statically assembled com-ponents. Examples of problem areas are loosening of shaft splines, gearbox housing joints, and gear fastenings bolts. The latter is a known problem on the Super Puma LH ancillary gearbox, where the intermediate gear fastening bolts tend to loose torque. Being able to detect this phenomenon before the entire gear start to loosen would of course be a bene.t.
Adding new sensor technologies to the HUMS will require a profound redesign of the airborne segment. These are thus considerations which should be kept in mind for the long term system evolution.
Indicator Processing and Classi.cation
The current classi.cation methods evaluate the input of each indicator against an individual threshold. There is no evaluation of indicator trends over time, and no testing for parallel drift in indicators.
Most faults tend to cause gradual increase in indicator values, creating trends of a more or less clear nature. Further, most faults tend to a.ect more than one indicator, causing parallel trends on several indicators. For some faults, the indicators tend to rise also on the adjacent components. These are correlations that could be exploited to improve diagnosis results.
Given N indicators calculated at M acquisitions, the most general way of evaluating this information is an N by M feature matrix containing all information ever recorded. A single instance of this matrix will contain all information necessary to detect faults on all components on the aircraft. For simpli.cation purposes, this operation can be split up in several steps, each step covering one component. N will then be replaced by a subset of N, N’, containing all indicators for the component in question. N’ might also contain indicators from adjacent components. M can be replaced by a subset of M, M’, containing the last few acquisitions or all acquisitions since last overhaul.
Evaluation of these feature matrices can be performed by classi.cation systems such as clustering, arti.cial neural networks, fuzzy logic, or polyno-mial approximation. As any component state will not have an unambiguous signature in such a matrix, it is necessary to perform a second level param-eterization. This can be achieved by for instance calculating the indicator derivative or developing a parametric indicator progression model.
HUMS support personnel are capable of detecting faults more precisely
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OPTIMIZATION OF FAULT DIAGNOSIS IN HELICOPTER HEALTH AND USA(32)
