Implementation of the health monitoring data for rotorcraft fatigue spectrum
Implementation of the health monitoring data for rotorcraft fatigue spectrum
dc.contributor.author | Rustici, S. | |
dc.contributor.author | Guadalupi, G. | |
dc.contributor.author | Mariani, U. | |
dc.date.accessioned | 2020-11-19T15:40:40Z | |
dc.date.available | 2020-11-19T15:40:40Z | |
dc.date.issued | 2017 | |
dc.description.abstract | The typical approach for the usage spectrum definition for the purpose of fatigue analysis in rotorcraft industry is currently based on the analysis of theoretical mission profiles and pilots’ experience feedback. The Health and Usage Monitoring System (HUMS) installed on Leonardo Helicopters (LH) rotorcraft represents the alternative approach for the fleet usage definition, thanks to the Flight Condition Recognition tool (FCR). The HUMS data is recorded and processed to determine the main parameters of usage, such as Take-off weight and centre of gravity position, Density Altitudes, Start and Stop events (SS) and Ground-Air-Ground cycles (GAG). Furthermore, the FCR routine allows determining the type of manoeuvres, monitoring the variation of some key parameters, like speed, acceleration, body angles, for similarity with the load survey flight tests. Recently, an entire AW101 military fleet deployed in mixed SAR and Utility missions, that was assessed in terms of fatigue life limitation using two theoretical pre-assigned usage spectrums, has been analysed to identify in-service usage spectrum through FCR tool. A unique usage spectrum has been determined on the basis of the HUMS data, complemented with pilots’ information when FCR could not provide sufficient details. Based on this analysis, an updated set of fatigue lives has been evaluated, leading to a beneficial impact on the maintenance limits and on the aircraft safety. Results described above have brought to an improved version of the HUMS software, now installed on latest AW101 variants with continuous data recording function, to obtain a more precise recognition of the manoeuvres and, hence, more precise fatigue analysis. | |
dc.identifier.other | 528_ERF2017 | |
dc.identifier.uri | http://hdl.handle.net/20.500.11881/3793 | |
dc.language.iso | en | |
dc.title | Implementation of the health monitoring data for rotorcraft fatigue spectrum |
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