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Title: Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
Authors: Wang, T
Meng, H
Qin, R
Zhang, F
Nandi, AK
Keywords: wind turbines;neural network;real-time implementation;bearing fault detection
Issue Date: 8-Apr-2024
Publisher: MDPI
Citation: Wang, T. et al. (2024) 'Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi', Applied Sciences, 14 (7), 3129, pp. 1 - 14. doi: 10.3390/app14073129.
Abstract: Wind turbines are a crucial part of renewable energy generation, and their reliable and efficient operation is paramount in ensuring clean energy availability. However, the bearings in wind turbines are subjected to high stress and loads, resulting in faults that can lead to costly downtime and repairs. Fault detection in real time is critical to minimize downtime and reduce maintenance costs. In this work, a simple neural network model was designed and implemented on a Raspberry Pi for the real-time detection of wind turbine bearing faults. The model was trained to accurately identify complex patterns in raw sensor data of healthy and faulty bearings. By splitting the data into smaller segments, the model can quickly analyze each segment and generate predictions at high speed. Additionally, simplified algorithms were developed to analyze the segments with minimum latency. The proposed system can efficiently process the sensor data and performs rapid analysis and prediction within 0.06 milliseconds per data segment. The experimental results demonstrate that the model achieves a 99.8% accuracy in detecting wind turbine bearing faults within milliseconds of their occurrence. The model’s ability to generate real-time predictions and to provide an overall assessment of the bearing’s health can significantly reduce maintenance costs and increase the availability and efficiency of wind turbines.
Description: Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to commercial privacy.
Other Identifiers: ORCiD: Tianhao Wang
ORCiD: Hongying Meng
ORCiD: Fan Zhang
ORCiD: Asoke Kumar Nandi
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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