Journal ArticleDOI
Modified self-organising map for automated novelty detection applied to vibration signal monitoring
TLDR
This novel MCM method is based on Kohonen's self-organising map and adopts a multidimensional dissimilarity measure for dual class classification and designed to be highly modular and scale well for a multi-sensor condition monitoring environment.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2006-04-01. It has received 102 citations till now. The article focuses on the topics: Novelty detection & Condition monitoring.read more
Citations
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Journal ArticleDOI
Fault diagnosis of rotating machinery based on a new hybrid clustering algorithm
TL;DR: A new hybrid clustering algorithm based on a three-layer feed forward neural network, a distribution density function, and a cluster validity index, is presented in this paper, and it is shown that it is a promising approach to the fault diagnosis of rotating machinery.
Journal ArticleDOI
A hybrid approach for identification of concurrent control chart patterns
TL;DR: This paper presents a hybrid approach based on independent component analysis (ICA) and decision tree (DT) to identify concurrent CCPs and shows that the proposed approach is very successful to handle most of the concurrent CCs.
Journal ArticleDOI
Ligand-Based Virtual Screening by Novelty Detection with Self-Organizing Maps
TL;DR: A novel method for ligand-based virtual screening, based on utilizing Self-Organizing Maps (SOM) as a novelty detection device, to improve the retrieval of potentially active compounds also in concert with other virtual screening methods.
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Performance Degradation Assessment for Bearing Based on Ensemble Empirical Mode Decomposition and Gaussian Mixture Model
TL;DR: A novel performance degradation assessment method for bearing based on ensemble empirical mode decomposition (EEMD), and Gaussian mixture model (GMM) to indicate the performance of the bearing.
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A discrepancy analysis methodology for rolling element bearing diagnostics under variable speed conditions
TL;DR: A rolling element bearing diagnostic methodology based on novelty detection is proposed for machines operating under variable speed conditions, which uses the wavelet packet transform, order tracking and a feature modelling approach to generate a diagnostic metric in the form of a discrepancy measure.
References
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Book
Self-Organizing Maps
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
Journal ArticleDOI
SOM-based data visualization methods
TL;DR: An overview and categorization of both old and new methods for the visualization of SOM is presented to give an idea of what kind of information can be acquired from different presentations and how the SOM can best be utilized in exploratory data visualization.
Journal ArticleDOI
Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms
L.B. Jack,Asoke K. Nandi +1 more
TL;DR: The performance of both types of classifiers in two-class fault/no-fault recognition examples are examined and the attempts to improve the overall generalisationperformance of both techniques through the use of genetic algorithm based feature selection process are examined.
Journal ArticleDOI
Analysis and visualization of gene expression data using self-organizing maps
TL;DR: It is shown that the SOM visualizes the similarity of genes in a more trustworthy way than two alternative methods, multidimensional scaling and hierarchical clustering.
Book ChapterDOI
On the Use of Self-Organizing Maps for Clustering and Visualization
TL;DR: It is demonstrated that the number of output units used in a self-organizing map (SOM) influences its applicability for either clustering or visualization, and it is shown that this flexibility comes with a price in terms of impaired performance.