Probabilistic fault identification using vibration data and neural networks

T. Marwala, H. E.M. Hunt

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Bayesian formulated neural networks are implemented using hybrid Monte Carlo method for probabilistic fault identification in cylindrical shells. Each of the 20 nominally identical cylindrical shells is divided into three substructures. Holes of (12±2) mm in diameter are introduced in each of the substructures and vibration data are measured. Modal properties and the Coordinate Modal Assurance Criterion (COMAC) are utilized to train the two modal-property-neural-networks. These COMAC are calculated by taking the natural-frequency-vector to be an additional mode. Modal energies are calculated by determining the integrals of the real and imaginary components of the frequency response functions over bandwidths of 12% of the natural frequencies. The modal energies and the Coordinate Modal Energy Assurance Criterion (COMEAC) are used to train the two frequency-response-function-neural-networks. The averages of the two sets of trained-networks (COMAC and COMEAC as well as modal properties and modal energies) form two committees of networks. The COMEAC and the COMAC are found to be better identification data than using modal properties and modal energies directly. The committee approach is observed to give lower standard deviations than the individual methods. The main advantage of the Bayesian formulation is that it gives identities of damage and their respective confidence intervals.

Original languageEnglish
Pages (from-to)674-680
Number of pages7
JournalProceedings of the International Modal Analysis Conference - IMAC
Publication statusPublished - 2000
Externally publishedYes

ASJC Scopus subject areas

  • General Engineering


Dive into the research topics of 'Probabilistic fault identification using vibration data and neural networks'. Together they form a unique fingerprint.

Cite this