As data analytic techniques evolve and the accessibility of process measurements improves, data-driven process monitoring has enjoyed a quick development in both theoretical and application perspectives recently. Although abundant process measurements will facilitate data-driven process monitoring and lead to better monitoring indexes, it becomes difficult to identify the underlying variables that are responsible for a fault directly with the monitoring indexes as the scope of measured variables is getting broader. To restrain the scope and identify the source of fault, contribution plots are commonly used in fault diagnosis in order to quantify the influence of process variables in presence of fault. Nevertheless, as sophisticated monitoring techniques become more and more complicated, deriving corresponding contribution plots is challenging. The concept of deviation contribution plots is proposed to address this issue. By extending the original definition of contribution for linear processes, the deviation contribution is defined to quantify the contribution of deviations in originally measured variables to the deviation of monitoring indexes. The ability of the proposed deviation contribution plots to identify influential variables in monitoring algorithms based on nonlinear feature extractions is verified by both numerical simulation and the Tennessee Eastman process benchmark case study.

中文翻译：

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多元统计的偏差贡献图


随着数据分析技术的发展和过程测量的可访问性的提高，数据驱动的过程监控最近在理论和应用方面都得到了快速发展。虽然丰富的过程测量将有助于数据驱动的过程监控并带来更好的监控指标，但随着测量变量的范围越来越广，直接用监控指标识别导致故障的潜在变量变得困难。为了限制故障范围并识别故障来源，故障诊断中通常使用贡献图来量化故障存在时过程变量的影响。然而，随着复杂的监测技术变得越来越复杂，得出相应的贡献图具有挑战性。提出偏差贡献图的概念来解决这个问题。通过扩展线性过程的原始贡献定义，定义偏差贡献来量化原始测量变量的偏差对监测指标偏差的贡献。数值模拟和田纳西州伊士曼过程基准案例研究验证了所提出的偏差贡献图识别基于非线性特征提取的监测算法中影响变量的能力。