Gear classification and fault detection using a diffusion map framework

Tuomo Sipola*, Tapani Ristaniemi, Amir Averbuch

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)53-61
Number of pages9
JournalPattern Recognition Letters
StatePublished - 1 Feb 2015


  • Clustering
  • Diffusion map
  • Fault detection
  • System health monitoring


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