通过流程分解算法提升故障检测精度，适合工程人员直接部署。
Fault Detection and Identification Using a Novel Process Decomposition Algorithm for Distributed Process Monitoring
- 基于流程图与控制回路结构分解测量变量为过程块
- 在块级应用PCA并结合贡献度图实现故障定位，准确率媲美先进方法
- 提供可视化故障传播分析工具，适合工业现场工程师使用
近年来，故障检测与识别越来越多依赖于复杂的检测技术，通常采用集中式或分布式方法。本文提出一种新的过程分解算法，用于确定相互关联测量变量的过程块，并在块级别应用主成分分析（PCA）以识别故障发生。此外，定义了一种新型贡献度图，可缩放不同故障的幅值，便于直观识别异常变量并分析故障传播。贝叶斯聚合故障指数与块级故障指数随时间变化，能精确定位故障源头。所提方法在田纳西东曼工厂基准测试中，故障检测率与多数先进集中式或分布式方法相当。由于该分解算法依赖于工艺流程图和控制回路结构，实际控制工程师可轻松实现该方法。
原文摘要 · Abstract (English)
Recent progress in fault detection and identification increasingly relies on sophisticated techniques for fault detection, applied through either centralized or distributed approaches. Instead of increasing the sophistication of the fault detection method, this work introduces a novel algorithm for determining process blocks of interacting measurements and applies principal component analysis (PCA) at the block level to identify fault occurrences. Additionally, we define a novel contributions map that scales the magnitudes of disparate faults to facilitate the visual identification of abnormal values of measured variables and analysis of fault propagation. Bayesian aggregate fault index and block fault indices vs. time pinpoint origins of the fault. The proposed method yields fault detection rates on par with most sophisticated centralized or distributed methods on the Tennessee Eastman Plant benchmark. Since the decomposition algorithm relies on the process flowsheet and control loop structures, practicing control engineers can implement the proposed method in a straightforward manner.
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