Machinery Fault Detection Using Geodesic Distance Based on Genetic Clustering Algorithm

Abstract:

Aim at the problem that there is an irregular data distribution when using multi-sensor to monitor machine conditions, a genetic clustering algorithm using geodesic distance metric (GCGD) is adopted to perform machine fault detection. In GCGD, a geodesic distance based proximity measure is employed replacing Euclidean distance that cannot correctly describe the relationship between data lying in a manifold, and GCGD determines partitioning of the feature vectors from a combinatorial optimization viewpoint. Fault detection experiments of inlet valve leakage in a two-stage reciprocating compressor reveal that GCGD achieves a better performance of fault detection than the K-means algorithm and a genetic algorithm based clustering technique.

• Periodical: Advanced Materials Research (Volume 411)
• Pages: 572-575
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.411.572
• Online since: November 2011
• Authors: Gang Li, Hong Xi Wang, Jian Zhuang
• Keywords: Clustering Analysis (CA), Fault Detection, Genetic Algorithm (GA), Geodesic Distance

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