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- W2369593400 abstract "Artificial neural network (ANN), especially the self-organizing map (SOM) based on unsupervised learning is a kind of excellent method for patterns clustering and recognition. And, independent component anslysis (ICA) is a powerful tool for analyzing nongaussian data. In ICA, the JADE based on jointly approximate diagonalisation of eigen-matrices is a kind of robust and steady algorithm, which is specially appropriate to feature extraction of multivariate data. In this paper, the JADE is firstly proposed for feature extraction of different mechanical patterns (including normal and gear pitting), followed by certain a typical ANN (for example MLP, RBFN or SOM) which implements the final classification. By means of ICA and the further feature extraction strategys based on residual mutual information (RMI), higher than second order features embedded in multi-channel vibration measurements can be captured effectively. Thus, mechanical fault patterns can be recognized correctly. The results from contrast experiments showed that the compound ICA-SOM classifier can be constructed in simpler way, and classify various fault patterns at considerable accuracy, both of which imply its great potential in health condition monitoring of machines." @default.
- W2369593400 created "2016-06-24" @default.
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- W2369593400 date "2003-01-01" @default.
- W2369593400 modified "2023-09-23" @default.
- W2369593400 title "ICA Based Neural Networks for Pattern Recognition of Mechanical Faults" @default.
- W2369593400 hasPublicationYear "2003" @default.
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