ABSTRACT Finding the cheapest, or smallest, set of sensors such that a specified level of diagnosis performance is maintained is important to decrease cost while controlling performance. Algorithms have been developed to find sets of sensors that make faults detectable and isolable under ideal circumstances. However, due to model uncertainties and measurement noise, different sets of sensors result in different achievable diagnosability performance in practice. In this paper, the sensor selection problem is formulated to ensure that the set of sensors fulfils required performance specifications when model uncertainties and measurement noise are taken into consideration. However, the algorithms for finding the guaranteed global optimal solution are intractable without exhaustive search. To overcome this problem, a greedy stochastic search algorithm is proposed to solve the sensor selection problem. A case study demonstrates the effectiveness of the greedy stochastic search in finding sets close to the global optimum in short computational time.

中文翻译：

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不确定系统故障诊断的传感器选择

摘要 寻找最便宜或最小的传感器组以保持指定的诊断性能水平对于在控制性能的同时降低成本非常重要。已经开发出算法来寻找在理想情况下使故障可检测和隔离的传感器组。然而，由于模型的不确定性和测量噪声，不同的传感器组在实践中导致不同的可实现的可诊断性能。在本文中，传感器选择问题被公式化，以确保在考虑模型不确定性和测量噪声的情况下，传感器组满足所需的性能规范。然而，在没有穷举搜索的情况下，寻找有保证的全局最优解的算法是难以处理的。为了克服这个问题，提出了一种贪婪随机搜索算法来解决传感器选择问题。一个案例研究证明了贪婪随机搜索在短时间内找到接近全局最优的集合的有效性。