About this Research Topic
To remain competitive, wind turbines must be reliable machines with efficient and effective maintenance strategies. Thus, it is essential to develop robust and cost-effective prognostic and health management strategies both in terms of their structure and their components.
On the one hand, the purpose of wind turbine (WT) structural health monitoring (SHM) is to detect, locate, and characterize structural damage, so that maintenance operations can be performed in due time. SHM has been widely applied in various engineering sectors due to its ability to respond to adverse structural changes, improving structural reliability and life cycle management. In the near future, SHM has the potential to be a wind energy harvester, in particular for offshore wind turbines. On the other hand, fault diagnosis and prognosis strategies based on condition based maintenance for the different systems (gearbox, main bearing, generator, etc.) of the wind turbine are crucial. Vibration-based condition monitoring is a well-established strategy but it usually relies on high-sampled data (>10 kHz) leading to a large amount of data from a large number of sensors. Finding patterns in such multivariable datasets is a challenge under the variety of operational modes and environmental conditions that wind turbines are subject to.
This Research Topic invites contributions that address wind turbine fault and damage prognosis and diagnosis. In particular, submitted papers should clearly show novel contributions and innovative applications covering, but not limited to, any of the following topics around wind turbines:
- Condition Monitoring,
- Structural Health Monitoring,
- Fault diagnosis,
- Damage diagnosis,
- Machine learning,
- Deep learning,
- Model-based.
On the one hand, the purpose of wind turbine (WT) structural health monitoring (SHM) is to detect, locate, and characterize structural damage, so that maintenance operations can be performed in due time. SHM has been widely applied in various engineering sectors due to its ability to respond to adverse structural changes, improving structural reliability and life cycle management. In the near future, SHM has the potential to be a wind energy harvester, in particular for offshore wind turbines. On the other hand, fault diagnosis and prognosis strategies based on condition based maintenance for the different systems (gearbox, main bearing, generator, etc.) of the wind turbine are crucial. Vibration-based condition monitoring is a well-established strategy but it usually relies on high-sampled data (>10 kHz) leading to a large amount of data from a large number of sensors. Finding patterns in such multivariable datasets is a challenge under the variety of operational modes and environmental conditions that wind turbines are subject to.
This Research Topic invites contributions that address wind turbine fault and damage prognosis and diagnosis. In particular, submitted papers should clearly show novel contributions and innovative applications covering, but not limited to, any of the following topics around wind turbines:
- Condition Monitoring,
- Structural Health Monitoring,
- Fault diagnosis,
- Damage diagnosis,
- Machine learning,
- Deep learning,
- Model-based.
Keywords: Structural Health Monitoring, Condition Monitoring, Damage, Fault, Data-Driven, Model-Based, Prognosis, Diagnosis, Machine Learning, Deep Learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
