1.º Prémio

Monteiro, Miguel Bento; Fandinga, Lídia Paula; Martins, Yvette; Semedo, Júlio; Ribeiro, José Cepeda; Carreiro, Luís; Pires, Jorge

doi:10.1016/s0873-2159(15)30531-6

Cited by 1 publication

(2 citation statements)

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“…Eigenvalue/eigenvector analysis has been widely used as a feature fusion method in condition monitoring [27][28][29]. Principal component analysis (PCA) is a simple eigenvectorbased multivariate analysis method.…”

Section: An Indicator Generation Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Generating an indicator for pump impeller damage using half and full spectra, fuzzy preference-based rough sets and PCA

Zhao¹,

Zuo²,

Patel³

2012

Meas. Sci. Technol.

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Parameters that vary monotonically with damage propagation are useful in condition monitoring. However, it is not easy to find such parameters especially for complex systems like pumps. A method using half and full spectra, fuzzy preference-based rough sets and principal component analysis (PCA) is proposed to generate such an indicator for tracking impeller damage in a centrifugal slurry pump. Half and full spectra are used for extracting features related to pump health status. A fuzzy preference-based rough set model is employed in the process of selecting features reflecting the damage propagation monotonically. PCA is used to condense the features and generate an indicator which represents the damage propagation. The effectiveness of the proposed method is tested using laboratory experimental data. Results show that the indicator generated by the proposed method can clearly and monotonically distinguish the health status of the pump impeller.

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Section: An Indicator Generation Methodsmentioning

confidence: 99%

“…Turhan-Sayan [27] used PCA for feature fusion and obtained a single characteristic feature vector that can effectively represent the electromagnetic target of concern. Pires et al [28] found that Evaluate Itempi with Eq. ( 8)…”

Section: An Indicator Generation Methodsmentioning

confidence: 99%