English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Condition monitoring of complex systems is usually based on various sensors to gain information on the system status and identify potential problems at an early stage. However, evaluation and interpretation of multiple sensor data is often a major problem due to complex interdependencies between measured sensor data and actual system condition. We have addressed this problem for the example of a fluid condition monitoring system in wind turbines, i.e. an oil filter and cooling subsystem equipped with various sensors to monitor relevant temperatures, differential and absolute pressure drop across the filter as well as specific oil conditions such as dielectric constant and viscosity to monitor chemical oil degradation. Data were recorded from two wind turbines in a field test over more than two years. Data evaluation was performed with both statistical as well as semantic analysis methods with the goal of predicting the remaining filter lifetime and identifying unusual data patterns indicating potential problems for the overall system. Results indicate a large potential for the hybrid combination of statistical and semantic sensor data analysis for condition monitoring and have especially indicated the potential for learning data patterns with one specific system and transferring the results to other systems. This would allow using operational experience in larger networks such as offshore wind parks to improve the performance and reliability of intelligent condition monitoring systems.

D8.1 - Statistical and Semantic Multisensor Data Evaluation for Fluid Condition Monitoring in Wind Turbines