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Gear classification and fault detection using a diffusion map framework
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DOI：10.1016/j.patrec.2014.10.019.png)
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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. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
System health monitoring
Diffusion map
Clustering
Fault detection
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