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- W2562803312 abstract "Abstract This article proposes a system health monitoring approach that detects abnormal behavior of machines. Diffusion map is used to reduce the dimensionality of training data, which facilitates the classification of newly arriving measurements. The new measurements are handled with Nystrom extension. The method is trained and tested with real gear monitoring data from several windmill parks. A machine health index is proposed, showing that data recordings can be classified as working or failing using dimensionality reduction and warning levels in the low dimensional space. The proposed approach can be used with any system that produces high-dimensional measurement data." @default.
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- W2562803312 date "2013-01-01" @default.
- W2562803312 modified "2023-09-27" @default.
- W2562803312 title "Gear classification and fault detection using a diffusion map framework" @default.
- W2562803312 hasPublicationYear "2013" @default.
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