Research on Deep Learning Algorithms for Condition Monitoring of Railway Equipment

With the rapid development of the railroad transportation industry, the condition monitoring of railroad equipment has become an important link to ensure transportation safety and efficiency. Traditional monitoring methods face many challenges when dealing with complex working conditions and massive data, and deep learning, as a technology that can automatically extract features and perform complex pattern recognition, provides a new solution for railroad equipment condition monitoring. In this paper, we systematically study the current status and cutting-edge progress of the application of deep learning algorithms for the key problems in railroad equipment condition monitoring. By analyzing the applicable scenarios and performance of different deep learning models (e.g., convolutional neural network, recurrent neural network, etc.), this paper discusses the key technologies and methods in data preprocessing, model optimization, and practical applications. The results show that deep learning algorithms have significant advantages in improving the accuracy and real-time performance of equipment failure prediction. Finally, this paper summarizes the current challenges and looks forward to the future development trends and application prospects.