Dataset: Rotor health monitoring combining spin tests and data-driven anomaly detection methods
Provided by National Aeronautics and Space Administration
Updated 12 Nov 2020
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The dataset on this page is a cache of the dataset https://catalog.data.gov/dataset/rotor-health-monitoring-combining-spin-tests-and-data-driven-anomaly-detection-methods. It is a snapshot of the dataset as it appeared on 06 Jun 2021 22:29 GMT. The current dataset could have changed in the meantime. Attribution: Contains data retrieved from Data.gov.
Health monitoring is highly dependent on sensor systems that are capable of performing in various engine environmental conditions and able to transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall performance of the engine system. Efforts are under way at NASA Glenn Research Center through support of the Intelligent Vehicle Health Management Project (IVHM) to develop and implement such sensor technology for a wide variety of applications. These efforts are focused on developing high temperature, wireless, low cost, and durable products. In an effort to address technical issues concerning health monitoring, this article considers data collected from an experimental study using high frequency capacitive sensor technology to capture blade tip clearance and tip timing measurements in a rotating turbine engine-like-disk to detect the disk faults and assess its structural integrity. The experimental results composed at a range of rotational speed...
|Dataset:||Rotor health monitoring combining spin tests and data-driven anomaly detection methods|
|Provider:||National Aeronautics and Space Administration|
|Licence:||License information is derived automatically. Refer to source for license information before using this data.|
License not specified
|Country:||United States of America|
"Rotor health monitoring combining spin tests and data-driven anomaly detection methods" contains the following resources:
- pdf - SHM2012.pdf
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