Reformer boxes are complex, integrated, and high-temperature units, subject to various failures during continuous operations for extended time periods. Challenges in the development of high-fidelity first principle models, despite easy availability of process measurements motivated the development of data-driven, automated fault detection (FD) systems. Paucity of plant-wide implementation of FD technologies in the chemical industry, accentuates the absence of relevant practical guidelines and best practices. In this paper, a trivially replicable FD system has been developed for large-scale industrial reformer boxes of hydrogen manufacturing units. Actual process data from plant historian has been used for training and validation of a novel model, developed using a combination of partial least squares regression and principal components analysis. Abnormalities based on several important measurements around the reformer were identified. Explicit details on implementation and insights obtained during development of the expert system have been provided for ease of replication and adaptability.

重整箱是复杂，集成和高温的设备，在长时间连续运行期间会遭受各种故障。尽管过程测量容易获得，但高保真第一性原理模型的开发面临的挑战促使了数据驱动的自动故障检测（FD）系统的开发。化工行业在工厂范围内实施FD技术的缺乏，加剧了相关实践指南和最佳实践的缺乏。本文针对氢制氢装置的大型工业重整机开发了一种可复制的FD系统。来自植物历史学家的实际过程数据已用于训练和验证一种新颖的模型，该模型是使用偏最小二乘回归和主成分分析相结合而开发的。基于重整器周围的几个重要测量值，发现异常。为了简化复制和适应性，提供了有关实施的明确细节和在专家系统开发过程中获得的见解。