6533b7d8fe1ef96bd126a313
RESEARCH PRODUCT
Gear classification and fault detection using a diffusion map framework
Amir AverbuchTuomo SipolaTapani Ristaniemisubject
ta113Diffusion (acoustics)Training setta214Computer scienceDimensionality reductiondiffusion mapExtension (predicate logic)computer.software_genreFault detection and isolationfault detectionsystem health monitoringArtificial IntelligenceSignal ProcessingComputer Vision and Pattern RecognitionData miningCluster analysiscomputerSoftwareCurse of dimensionalityclusteringdescription
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 Nyström 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. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-02-01 |