Selective Feature Reinforcement Network for Robust Remote Fault Diagnosis of Wind Turbine Bearing Under Non-Ideal Sensor Data

In the wind turbine remote fault diagnosis, sensor data is susceptible to low-quality phenomena such as missing and damaged data due to communication delays, environmental noise, and sensor faults. These issues decrease the accuracy of fault diagnostic models (FDMs), necessitating a solution to enha...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2024, Vol.73, p.1-11
Main Authors: Tan, Jinbiao, Wan, Jiafu, Chen, Baotong, Safran, Mejdl, AlQahtani, Salman A., Zhang, Rui
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Background noise
Data models
Deep learning
Diagnostic systems
Fault diagnosis
Interference
Mapping
Remote sensors
Robustness
Robustness (mathematics)
sensor failure
Sensors
Training
wind turbine
Wind turbines
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Summary:In the wind turbine remote fault diagnosis, sensor data is susceptible to low-quality phenomena such as missing and damaged data due to communication delays, environmental noise, and sensor faults. These issues decrease the accuracy of fault diagnostic models (FDMs), necessitating a solution to enhance model robustness under non-ideal sensor data conditions. Hence, a robust fault diagnostic scheme based on adaptive noise filtering and useful feature-domain enhancement (UFDE) is proposed in this article to improve the stability of fault diagnostic performance. An interference identification branch (IIB) is designed to analyze sensor data from a high-dimensional and multilevel perspective, automatically identifying and localizing feature noise during training. Subsequently, a UFDE mechanism containing three feature mapping modes is created, using adaptive mapping and filling of fault features in the neighborhood to eliminate feature noise and enhance the useful feature domain. This process improves the representation of fault features under non-ideal sensor data conditions, such as noise interference and data defects, thereby enhancing the FDMs robustness. Finally, under non-ideal sensor data conditions, comparative experiments with advanced fault diagnostic methods demonstrate that the proposed method exhibits minimal fluctuations in diagnostic accuracy and achieves the highest correctness rate, validating its robustness.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3375958