# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 1 Introduction  
Wind energy has now become a component of the energy mix, significantly bolstering its role in renewable energy sources. According to the International Energy Agency (IEA), in 2020, the global installed capacity for wind power amounted to 721 GW [1]. Wind power plays a role in minimizing impact and fostering the adoption of low carbon alternatives over traditional fossil fuels. The growing demand for energy has driven the acceptance of wind turbines as a pivotal technology for generating renewable energy, which is essential for our worldwide shift towards cleaner and more sustainable energy sources. This transition is essential in reducing our reliance on fossil fuels, curbing carbon emissions and promoting progress. The reliability of wind turbines largely determines the consistency of the electricity supply. When wind turbines are operating, they experience loads and stress in key components like bearings. Bearings are essential in converting wind energy into power. However, these components are susceptible to wear and damage, especially when exposed to conditions such as high winds, temperature fluctuations, or salty offshore atmospheres. Any defects in the bearings possibly affect the performance of wind turbines and may lead to unforeseen unexpected downtime and substantial maintenance costs. This ultimately impacts the viability and overall energy output of wind farms. One of the most common reasons for wind turbine downtime is generator failure, which accounts for 37% of all failure downtimes [2]. Real-time generator defect detection can aid in preventing system shutdowns and mitigating their effects. There are a number of defect detection systems for wind turbine generators. These methods include spatiotemporal attention-based long short-term memory auto-encoder networks [2], marker-tracking for immediate rotational speed measurement [3], chaotic system and extension neural network fault diagnostics [4], time-varying models with augmented observers [5], deep learning approaches for sensor data prediction and fault diagnosis [6], sensor selection algorithms for real-time fault detection [7], enhanced variational mode algorithm fault diagnosis [8], image texture analysis for fault detection and classification [9], and cost-sensitive algorithms for online fault detection [10]. Wind turbine bearings are one of the key components, and the performance and lifespan of the device are significantly influenced by their normal operation. However, wind turbine bearings frequently sustain damage and malfunction in long-term operation due to high loads and harsh weather conditions. These flaws, which pose major risks to the wind turbine's ability to operate safely, may include rolling bead fatigue, insufficient lubrication, and an unbalanced load [11]. For prompt maintenance actions, shorter stop times, and lower maintenance costs, the real-time detection of wind turbine bearing failures is crucial. Preventive maintenance can be accomplished through real-time monitoring, quickly and precisely detecting bearings, preventing further degradation of faults, and correcting faults to maintain wind turbines' continuous functioning and dependability. Currently, the detection of bearing faults mainly relies on traditional vibration analysis and oil analysis [11]. These techniques often involve processing amounts of raw data, which makes real-time detection and prediction more complicated. Recently, deep learning techniques such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) have also been used in bearing fault detection [12; 13; 14]. However, their complex model structures and numerous parameters make it challenging to deploy them in real time on edge devices. With variational mode decomposition with deep convolutional neural networks (VMD-DCNNs) [15], it is possible to diagnose rolling bearing faults that do not require manual labor or human experience.