# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 1 Introduction
The performance variations of existing approaches in various contexts and situations are obtained by extracting characteristics from each inherent mode function (IMF). The multi-scale convolutional neural network with bidirectional long short-term memory (MSCNN-BiLSTM) model [16], using a weighted majority voting rule, enhance the intelligent fault diagnosis of bearings in wind turbines under complex working and testing environments, achieving improved diagnostic performance compared to existing methods. The deep bi-directional long short-term memory (DB-LSTM) [17] method circumvents feature selection challenges, enhances computational efficiency, and generates simulated data that closely resemble real-world settings, hence improving the approach's applicability. Supervisory Control And Data Acquisition (SCADA) is ensemble method that consists of XGBoost [18], a framework based on ensemble learning and genetic algorithms, utilizing SCADA data to detect faults in wind turbines. The deep convolutional neural network (DCNN) and Synchro Squeezing Transform (SST) [19], compared to traditional spectral analysis methods, can automatically identify fault features, avoiding misdiagnosis and missed diagnosis that may be caused by manual identification, and its excellent classification effect has been verified through experiments. The Self-Adaptive Teaching-Learning-Based Optimization Multi-Layer Perceptron (SATLBO-MLP) [20] is a data-based wind turbine bearing fault diagnosis method that is applied to the SATLBO algorithm optimization MLP model. The related experimental results demonstrate the effectiveness of the method. Some studies have attempted to address this issue by training models in the cloud [21] and then transferring them to edge devices. However, this method relies on a network connection and has challenges in meeting real-time performance demands in practical applications. Currently, research mostly concentrates on the impact of models, while giving less consideration to the practical viability of models, such as optimizing model size and predictive latency. This aspect requires further enhancement. Overall, real-time defect detection research on wind turbine bearing failure has not been conducted, and most methods need to process raw data, which will cost time in practical engineering applications, resulting in the inability to make strategic adjustments and take appropriate measures in a timely manner, which may lead to irreparable losses. In general, the existing approaches for detecting bearing faults still have limitations in terms of real-time capability, user-friendliness, and other factors, making it challenging to fulfill the requirements of real-time monitoring and prediction in industrial settings. In spite of that, deep learning exhibits significant promise for utilization in wind turbine health monitoring, particularly in the detection of bearing faults. Compared to traditional machine learning techniques, the main benefits of deep learning include the following: Unsupervised learning has the capacity to extract intricate and non-linear patterns from data without the need for manual configuration, making it particularly well suited for complicated datasets [22]. Deep learning acquires intricate functional connections by utilizing multi-layer network architectures with strong fitting capabilities [23]. Finally, it implements end-to-end learning, autonomously capturing the geographical and temporal relationships within the data [24]. Due to the growing processing power and data volume, performance is consistently enhancing and has surpassed manually built algorithms in numerous tasks [25]. Furthermore, the operational data of bearings contain abundant information in both the time and frequency domains. The neural network model has the capability to automatically acquire these intricate characteristics by utilizing its multi-layer network architecture. Furthermore, it can proficiently address the difficulties posed by substantial data quantities and the fusion of multiple sensors.