Semisupervised Distance-Preserving Self-Organizing Map for Machine-Defect Detection and Classification

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Abstract
Many intelligent learning methods have been successfully applied in gearbox fault diagnosis. Among them, self-organizing maps (SOMs) have been used effectively as they preserve the topological relationships of data. However, the structures of data clusters learned by SOMs may not be apparent and their shapes are often distorted. This paper presents a semisupervised diagnosis method based on a distance-preserving SOM for machine-fault detection and classification, which can also be used to visualize the SOM learning results directly. An experimental study performed on a gearbox and bearings indicated that the developed approach is effective in detecting incipient gear-pitting failure and classifying different bearing defects and levels of ball-bearing defects.

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