Trends in Asset Monitoring and Prognostics |
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| Wenyu Zhao, Edzel Lapira, Lodovico Menozzi |
- Abstract:
- This presentation introduces signal processing techniques as applied to specific machine components with a focus on the output and use with prognostics technologies. With properly organized outputs, prognostics algorithms transform the fleet condition and health management challenge into a deployable fleet health management solution. This approach includes data and model driven failure patterns, sensory data connectivity from deployed assets, prognostics analytical applications, and advisory generation outputs which guide the asset owners and maintainers. To illustrate the concepts, a Wind power asset monitoring case study is presented.
A data-driven approach is applied to model power curve continuously, which is entailed within a two-tier framework that employs Prognostics and Health Management (PHM) techniques for wind turbine monitoring. A set of measurements during a known good condition is utilized to setup a baseline model. Regular power curve measurements arc then coinpared while taking into account the multi-regime dynamics of the turbine.
The approach was implemented using NI LabVIEW's Watchdog Agent (R) Toolkit and was successfully validated using actual SCADA data collected from an on-shore wind turbine. - Download:
- IMEKO-TC10-2013-KN-002.pdf
- DOI:
- -
- Event details
- IMEKO TC:
- TC10
- Event name:
- TC10 Workshop 2013
- Title:
12th IMEKO TC10 Workshop "New Perspectives in Measurements, Tools and Techniques for Industrial Applications"
- Place:
- Florence, ITALY
- Time:
- 06 June 2013 - 07 June 2013