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
Informationsansvarig: webmaster
Senast uppdaterad: 2021-11-10
LiU Homepage
