Fault Classification using Pseudomodal Energies and Neuro-fuzzy modelling

Simple item page
dc.creatorMarwala, Tshilidzi
dc.creatorTettey, Thando
dc.creatorChakraverty, Snehashish
dc.date2007-05-15
dc.date.accessioned2026-07-07T08:01:47Z
dc.date.available2026-07-07T08:01:47Z
dc.descriptionThis 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.
dc.description8 pages, In Proceedings of the Asia-Pacific Workshop on Structural Health Monitoring, Yokohama, Japan, 2006
dc.identifierhttps://arxiv.org/abs/0705.2236
dc.identifierhttp://arxiv.org/abs/0705.2236
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/129025
dc.subjectArtificial Intelligence
dc.titleFault Classification using Pseudomodal Energies and Neuro-fuzzy modelling
dc.typetext

Files

Collections

arXiv