Abstract：The petrochemical industry is characterized by complex equipment operating conditions and harsh operating envi-ronments, where equipment failure is prone to cause safety accidents and huge economic losses. Traditional maintenance models can hardly meet the high-quality development needs of the modern petrochemical industry, while big data analysis technology provides a new solution for equipment failure prediction and preventive maintenance. This paper first elaborates on the failure characteristics of petrochemical equipment and the limitations of traditional maintenance models, then constructs a big data-driv-en equipment failure prediction and preventive maintenance system, including core links such as data collection, preprocessing, feature extraction, and prediction model construction. Combined with practical cases, the application effect of the system is verified, and finally the challenges faced by the current technical application and the future development direction are analyzed. The research shows that big data analysis technology can effectively improve the accuracy of petrochemical equipment failure prediction, optimize preventive maintenance strategies, reduce maintenance costs and downtime losses, and provide technical support for the safe production of the petrochemical industry.
高永莲 (2025) studied this question.