The paper proposes an innovative approach in solving the fault detection problem of
sewerage treatment plant machinery. The proposed approach treats the fault detection
data with the class decomposition problem, ensuring that a classification algorithm
overlooks no disjunct instances. As the class decomposition technique requires heavy
customization to each class of instances in every data set, Grey Wolf Optimizer is
used to determine the appropriate clustering method with the appropriate setting for
each class of instances. The proposed approach is tested on real-life sensor data
from a sewerage treatment plant, and the results show that here proposed approach
overshadows several manually proposed class decomposition methods.
