Article

Abstract

• This article presents a vibration-based technique for damage detection in the cylindrical equipment. First, a damage index based on the residual frequency responses is defined. This technique uses the principal component analysis for data reduction by eliminating the components that have the minimum contribution to the damage index. Then, the principal components are fed into neural networks to identify the changes in the damage pattern. Furthermore, the efficiency of this technique in the field condition is investigated by adding different noise levels to the output data. This study aims at proposing a cost-effective damage detection model using only one sensor. Therefore, the optimal location of the sensor is also discussed. A case study of capacitive voltage transformer is used for validation of finite element models. The neural networks are trained using numerical data and tested with experimental one. Several parametric analyses are performed to investigate the sensitivity of the model.

Creator

• Mirtaheri, M.
• Karami-Mohammadi, R.
• Salkhordeh, M.
• Hariri Ardebili, Mohammad Amin

Date Issued

• 2019-07-25

Academic Affiliation

• Civil, Environmental and Architectural Engineering

Journal Title

• Advances in Mechanical Engineering

Journal Issue/Number

• 10

Journal Volume

• 10

Última modificação

• 2020-03-20

Resource Type

• Article

Declaração de direitos

• In Copyright

DOI

•  https://dx.doi.org/10.1177/1687814019866942

Peer Reviewed

• Yes

ISSN

• 1687-8140

Language

• English [eng]

License

• Creative Commons BY Attribution 4.0 International

Relações

Itens

Miniatura	Título	Data de carga	Acesso	Ações
	1687814019866942.pdf	2020-03-20	Público	Baixar