Data-driven fault detection and isolation (FDI) depends on complete, comprehensive, and accurate fault information. Optimal test selection can substantially improve information achievement for FDI and reduce the detecting cost and the maintenance cost of the engineering systems. Considerable efforts have been worked to model the test selection problem (TSP), but few of them considered the impact of the measurement uncertainty and the fault occurrence. In this article, a conditional joint distribution (CJD)-based test selection method is proposed to construct an accurate TSP model. In addition, we propose a deep copula function which can describe the dependency among the tests. Afterward, an improved discrete binary particle swarm optimization (IBPSO) algorithm is proposed to deal with TSP. Then, application to an electrical circuit is used to illustrate the efficiency of the proposed method over two available methods: 1) joint distribution-based IBPSO and 2) Bernoulli distribution-based IBPSO.

中文翻译：

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用于故障检测和隔离的基于条件联合分布的测试选择


数据驱动的故障检测和隔离（FDI）依赖于完整、全面、准确的故障信息。最优的测试选择可以显着提高FDI的信息成果，降低工程系统的检测成本和维护成本。人们为模拟测试选择问题（TSP）付出了巨大的努力，但很少有人考虑测量不确定性和故障发生的影响。本文提出了一种基于条件联合分布（CJD）的测试选择方法来构建准确的 TSP 模型。此外，我们提出了一种深度连接函数，可以描述测试之间的依赖关系。随后，提出了一种改进的离散二元粒子群优化（IBPSO）算法来处理TSP。然后，通过在电路中的应用来说明所提出的方法相对于两种可用方法的效率：1）基于联合分布的 IBPSO 和 2）基于伯努利分布的 IBPSO。