Fault Classification using Pseudomodal Energies and Neuro-fuzzy
  modelling
  This paper presents a fault classification method which makes use of a
Takagi-Sugeno neuro-fuzzy model and Pseudomodal energies calculated from the
vibration signals of cylindrical shells. The calculation of Pseudomodal
Energies, for the purposes of condition monitoring, has previously been found
to be an accurate method of extracting features from vibration signals. This
calculation is therefore used to extract features from vibration signals
obtained from a diverse population of cylindrical shells. Some of the cylinders
in the population have faults in different substructures. The pseudomodal
energies calculated from the vibration signals are then used as inputs to a
neuro-fuzzy model. A leave-one-out cross-validation process is used to test the
performance of the model. It is found that the neuro-fuzzy model is able to
classify faults with an accuracy of 91.62%, which is higher than the previously
used multilayer perceptron.
