A New Soft-Sensor-Based Process Monitoring Scheme Incorporating Infrequent KPI Measurements
A New Soft-Sensor-Based Process Monitoring Scheme Incorporating Infrequent KPI Measurements
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DOI：
10.1109/tie.2014.2364561
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发表时间：
2015-06-01
影响因子：
7.7
通讯作者：
Ding, Steven X.
中科院分区：
文献类型：
--
作者：
Shardt, Yuri A. W.;Hao, Haiyang;Ding, Steven X.
先进的过程监控和故障诊断技术的发展，使用基于模型和数据驱动的方法，导致了许多实际应用。在这些应用程序中尚未考虑的一个问题是处理关键业绩指标的能力，这些指标只是偶尔测量，而且有很大的时间延迟。因此，在本文中，数据驱动设计的诊断观察员为基础的过程监控计划扩展到包括检测变化的能力，很少测量的KPI。证明了扩展诊断观测器是稳定的，因此能够收敛到真值。所提出的方法进行了测试，使用Monte Carlo模拟和田纳西-伊士曼问题。结果表明，虽然时间延迟和采样时间增加的检测延迟，可以通过使用软测量减轻整体效果。此外，它示出的结果是不强烈依赖于采样时间，但依赖于时间延迟。因此，所提出的基于软测量的监测方案可以有效地检测故障，即使在没有直接的过程信息。
The development of advanced techniques for process monitoring and fault diagnosis using both model-based and data-driven approaches has led to many practical applications. One issue that has not been considered in such applications is the ability to deal with key performance indicators (KPIs) that are only sporadically measured and with significant time delay. Therefore, in this paper, the data-driven design of diagnostic-observer-based process monitoring schemes is extended to include the ability to detect changes given infrequently measured KPIs. The extended diagnostic observer is shown to be stable and hence able to converge to the true value. The proposed method is tested using both Monte Carlo simulations and the Tennessee-Eastman problem. It is shown that although time delay and sampling time increase the detection delay, the overall effect can be mitigated by using a soft sensor. Furthermore, it is shown that the results are not strongly dependent on the sampling time, but do depend on the time delay. Therefore, the proposed soft-sensor-based monitoring scheme can efficiently detect faults even in the absence of direct process information.