Improving Signal Monitoring and Fault Diagnosis of a Shaft System Using Convolution Neural Network (CNN)

Signal monitoring and fault diagnosis of shaft systems are essential for maintaining the performance, safety, and efficiency of mechanical equipment in various industrial applications. Traditional methods of fault diagnosis, such as vibration analysis and signal processing, often rely on manual feature extraction and domain-specific expertise, limiting their effectiveness in complex or high-dimensional systems. This study explores the application of Convolutional Neural Networks (CNNs) to improve signal monitoring and fault diagnosis in shaft systems. CNNs, known for their ability to automatically extract relevant features from raw data, are applied to vibration signals and spectrograms to identify and classify faults in shaft systems. By utilizing deep learning, this approach reduces the need for manual intervention, enhances the accuracy of fault detection, and facilitates real-time monitoring. The results demonstrate that CNN-based systems significantly outperform traditional diagnostic methods, offering higher reliability, faster detection, and better adaptability to various fault types. This research highlights the potential of integrating CNNs into industrial diagnostic systems, enabling predictive maintenance and reducing unplanned downtime. The findings suggest that CNNs can be an effective tool for enhancing the performance of shaft systems, leading to more efficient and cost-effective operations. Future work will focus on optimizing CNN architectures, expanding the dataset for training, and incorporating real-time monitoring for even greater diagnostic capabilities.