This paper describes the current state of development of a prototype mechanical diagnostic system being developed for application to helicopter drive train components. The system will detect structure borne, high frequency, acoustic date, and process it with feature extraction and polynomial network artificial intelligence software. Data for network training and evaluation has been acquired from both healthy and discrepant components, operated over a full range of loads, in a test cell. Stress Wave Analysis (SWAN) is a high frequency acoustic sensing and signal conditioning technology, which provides an analog signal that is a time history of friction and shock events in a machine. This 'Stress Wave Pulse Train' (SWPT) is independent of background level of vibration and audible noise. The SWPT is digitized and used to compute a set of features that characterize the 'friction signature'. Fault Detection Networks of polynomial equations are used to automatically classify SWPT features as being representative of either healthy or discrepant mechanical components. The application of these techniques for automatic classification of friction signatures advances current technology to achieve real time diagnostic capability at all flight power levels.
Predictive maintenance using stress wave analysis
Vorbeugende Wartung durch Spannungswellenanalyse
1998
22 Seiten, 10 Bilder, 1 Tabelle
Aufsatz (Konferenz)
Englisch
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