We are proud to announce the publication of our latest research on the application of Topological Data Analysis (TDA) for Condition-Based Monitoring (CBM) of wind turbines. This new study will be presented next week at an international conference focused on equipment health and prognostics in Prague https://phm-europe.org/.
Abstract: Our research investigates how TDA, a sophisticated branch of data analysis, can enhance the monitoring and maintenance of wind turbines. By analyzing complex datasets obtained from standard vibration sensors in turbine gearboxes, we identify patterns, anomalies, and trends that are often undetectable using traditional methods.
Key Highlights:
- Data Source: gearbox vibration data, collected from a wind park in Norway, data contextualised in CDF.
- Methodology: Conversion of time series data into multi-dimensional point clouds through time-delay embedding
- Analysis Tools: Utilization of topological methods, including persistent homology
- Indicators: Key health indicators derived from Betti numbers, information entropy, and signal persistence for effective fault detection and diagnosis
Benefits:
- Improved Fault Detection: Early and accurate identification of gear-tooth and ball-bearing failures
- Enhanced Maintenance: Proactive and data-driven maintenance strategies
- Increased Reliability: Higher operational efficiency and reduced downtime for wind turbines
Our topological indicators represent a breakthrough in failure identification and diagnosis, offering a compelling alternative to traditional signal processing techniques. We believe this innovative approach can benefit our customers in the energy sector, ensuring the operational health and efficiency of their wind turbine assets.
For more details and to explore potential collaborations, feel free to reach out or access the full paper through this link: https://arxiv.org/abs/2406.16380