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Tobias Hoinka: Predictive Maintenance and Anomaly Detection for Wind Energy
Discover how predictive maintenance and anomaly detection drive wind energy efficiency with Tobias Hoinka's insights on EMBW Asset Radar, a cutting-edge application for monitoring and predicting wind turbine defects.
- Predictive maintenance and anomaly detection are crucial for wind energy efficiency.
- EMBW Asset Radar: a proprietary application to monitor and predict wind turbine defects.
- Multivariate data analysis is challenging due to completeness, noise, and statistical complexity.
- Incomplete labels and heterogeneity in data skew the analysis.
- Regression models and autoencoders are used for predictive maintenance.
- Anomalies are difficult to identify due to patterns and trends in data.
- Asset Radar aims to minimize on-site maintenance and facilitate diagnosis.
- Correlation and cognition are essential in predictive modeling.
- Interpretable models are crucial for anomaly detection.
- Keeping track of signals and signals’ meaning is crucial.
- EMBW Asset Radar aims to minimize on-site maintenance.