Fault Classification using Pseudomodal Energies and Neuro-fuzzy modelling

TL;DR

This paper introduces a fault classification approach using Pseudomodal energies and a Takagi-Sugeno neuro-fuzzy model, achieving high accuracy in identifying faults in cylindrical shells from vibration signals.

Contribution

It combines Pseudomodal energy feature extraction with a neuro-fuzzy model for fault classification, demonstrating improved accuracy over previous methods.

Findings

Fault classification accuracy of 91.62%

Effective feature extraction from vibration signals

Superior performance compared to multilayer perceptron

Abstract

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.