Research on Fault Detection and Prediction Model in Information System Operation and Maintenance

This study explores the effectiveness of applying big data-driven fault detection and prediction models in information system operation and maintenance. It mainly focuses on building accurate fault prediction models through machine learning algorithms to detect potential system failures in advance and improve operation and maintenance efficiency. The study first collects and processes a large amount of operation and maintenance data in a big data environment, and combines a variety of machine learning algorithms such as decision trees, support vector machines, and neural networks to design a comprehensive fault detection and prediction model. The model cannot only classify and identify existing faults, but also predict possible future fault points based on historical data. Through system simulation, this paper verifies the performance of the model in multiple scenarios. The simulation results show that the model has achieved significant improvements in key indicators such as accuracy, recall rate, and F1 value, especially in complex and changeable system environments. It shows strong robustness. Accurate data analysis further shows that the designed model can effectively reduce system downtime and improve overall operation and maintenance efficiency.