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Sensor selection for fault diagnosis in uncertain systems

Finding the cheapest, or smallest, set of sensors such that a specified level of di- agnosis 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 uncertain- ties and measurement noise, different sets of sensors result in different achievable diagnosability performance in practice. In this paper, a measure called distinguisha- bility, is used to quantify diagnosability performance given a model and a set of sensors. The sensor selection problem is then formulated to ensure that the set of sensors fulfills 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, and it is demonstrated why it is hard to find optimal solutions to the sensor selection problem 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.

Daniel Jung, Yi Dong, Erik Frisk, Mattias Krysander and Gautam Biswas

International Journal of Control, Taylor & Francis, 2018

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