Identification of the coupling functions between the process and the degradation dynamics by means of the variational Bayesian inference: an application to the solid-oxide fuel cells
Identification of the coupling functions between the process and the degradation dynamics by means of the variational Bayesian inference: an application to the solid-oxide fuel cells
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DOI：
10.1098/rsta.2019.0086
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发表时间：
2019-10
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通讯作者：
Boštjan Dolenc;Đ. Juričić;P. Boškoski
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文献类型：
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作者：
Boštjan Dolenc;Đ. Juričić;P. Boškoski
了解系统部件退化与系统动态耦合的方式对于现代工程系统的监测和控制是高度相关的。本文主要研究了描述固体氧化物燃料电池(SOFC)电堆中系统动力学与退化速率之间关系的耦合函数的辨识问题。基于从在线获取的数据估计的退化动态，我们可以设计及时的缓解行动以及最佳的维护干预措施。我们介绍了一种计算上容易处理的识别方法，该方法考虑了对某一退化机制的实验发现的耦合函数形式的先验知识。利用变分贝叶斯推理对非线性耦合函数进行估计。该方法在SOFC系统的1600h记录上进行了测试。结果表明，与使用纯数据驱动的黑盒模型相比，使用耦合函数的先验形式可以得到更好的退化预测。变分贝叶斯方法的可靠收敛和实现的简单性使其成为SOFC系统现场性能监测的一种有前途的工具。本文是“耦合作用：物理、生物和社会科学中的动力相互作用机制”主题的一部分。
Understanding the way in which the degradation of a system's component is coupled to the system's dynamics is highly relevant for the monitoring and control of modern engineering systems. This paper focuses on the identification of coupling functions that describe the relationship between the system's dynamics and the degradation rate in solid-oxide fuel cell (SOFC) stacks. Based on the degradation dynamics estimated from data acquired online, we can design timely mitigation actions as well as optimal maintenance interventions. We introduce a computationally tractable identification approach that takes into account prior knowledge of the form of the coupling function that is found experimentally for a certain degradation mechanism. The nonlinear coupling function is estimated using variational Bayesian inference. The approach is tested on a 1600 h recording from a SOFC system. It is shown that the use of the prior form of the coupling function results in a superior prediction of the degradation, when compared with that obtained using purely data-driven black-box models. The reliable convergence of the variational Bayesian method and the simplicity of its implementation make it a promising tool for the in-field performance monitoring of SOFC systems. This article is part of the theme issue ‘Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences’.