Artificial neural network based electrical machine fault classification on FPGA

Abstract

Electrical machines and drives play a critical role in modern society, spanning industrial equipment to renewable energy production. The reliability and efficiency of these systems depend on continuous performance monitoring and the early detection of potential issues. Condition monitoring systems are essential for evaluating the performance of machines and detecting potential faults early, thereby reducing unexpected downtimes and maintenance costs. Traditional fault detection methods include vibration analysis, thermography, and oil analysis. Vibration analysis is used to identify mechanical issues, while thermography can detect overheating that may indicate electrical or mechanical problems. The integration of artificial intelligence (AI) and machine learning techniques has significantly enhanced fault detection capabilities. AI techniques such as artificial neural networks (ANN) and support vector machines (SVM) can process large datasets to predict potential faults in advance. This thesis aims to develop and implement an ANN-based fault diagnosis system for electrical machines on an FPGA platform. The system will leverage the high-speed processing and parallel computing capabilities of FPGAs to achieve real-time fault detection and diagnosis. Additionally, this method will be tested with different datasets to evaluate its generalizability. The thesis comprises two main sections: deep learning and FPGA applications. The first section describes the dataset used, data processing, and the development of the CNN architecture. The second section discusses the implementation of the CNN model on FPGA using VHDL. Experimental results are presented in the final section. In the deep learning section, a CNN-based fault detection model has been developed for ensuring the reliable operation of electrical machines. The dataset used consists of vibration signals obtained from the MAFAULDA database. The data were used to train and test the CNN model, with performance evaluated based on accuracy, precision, recall, and F1 score. The ability of CNNs to learn local patterns and features makes them particularly effective for fault detection. The FPGA application section covers the implementation of the CNN-based fault detection system on FPGA. Convolution_1D, Convolution_1D_Middle, Convolution_1D_No_MP, and Dense layers were developed using VHDL. Real-time fault detection was performed using the Nexys A7 development board. The parallel processing capabilities of the FPGA allowed for high-speed computations, making the system suitable for real-time applications. This implementation efficiently handles large volumes of data, ensures low latency, and maintains high accuracy. When developing a CNN-based fault detection system using VHDL, optimizing the layers and processes is crucial. The Convolution_1D, Convolution_1D_No_MP, and xx Convolution_1D_Middle layers perform 1D convolution operations on the input data using predefined kernels. The Convolution_1D layer extracts fundamental features from raw sensor data, while the Convolution_1D_No_MP layer maintains higher resolution for detailed analysis. The Convolution_1D_Middle layer further refines the features, ensuring high accuracy in fault detection. The Dense layer processes these features into the final classification result. The TOP_CNN module integrates all layers, managing the data flow and producing the final fault classification result. In conclusion, a CNN-based fault detection system has been developed and implemented on FPGA. The system significantly enhances the reliability and efficiency of electrical machines. Future research will involve testing this method with broader datasets and different application areas to further evaluate and improve its effectiveness. This work contributes to the implementation of proactive maintenance strategies in industrial processes, ensuring operational excellence of machinery