Data Analysis Algorithm Design and Implementation Based on Deep Learning

In today's manufacturing environment, troubleshooting and data analysis of industrial equipment is a challenging task. With the rapid development of industrial automation and Internet of Things (IoT) technologies, modern industrial systems generate a large amount of heterogeneous sensor data, but traditional analysis methods face three key limitations: difficulty in meeting real-time processing constraints, challenges in extracting features from multi-source data, and poor reliability under complex operating conditions.In this study, a deep learning-based data analysis algorithm designed for industrial applications is proposed to address these challenges. To achieve efficient extraction and fusion of spatio-temporal features from industrial sensor data, the algorithm introduces a novel dual-stream feature learning architecture combining CNN and LSTM networks with an attention mechanism. The algorithm has demonstrated excellent performance with 94.3% fault diagnosis accuracy, 8.3ms average processing latency and robust operation under a variety of industrial conditions when implemented in a real production environment of 12 production lines. Deployments have shown that it is a valuable solution for smart manufacturing, significantly improving the efficiency of equipment maintenance and reducing unplanned downtime.