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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Dissertation
Automated Fault Diagnosis in Rotating Machinery
TL;DR: A novel data-driven framework to condition monitoring of gearbox is studied and illustrated using simulated and experimental vibration signals, developed to facilitate automated monitoring of machinery in industries, thus reducing the need for manual supervision and interpretation.
Patent
Verfahren zum Überwachen von rotierenden Maschinen
TL;DR: In this paper, a verfahren zum Uberwachen einer rotierenden Maschine, with einer Mehrzahl von Sensors zum Erfassen von physikalischen Parametern versehen ist and in mehreren variablen Betriebszustanden betrieben wird, is described.
Journal ArticleDOI
Health monitoring of rolling element bearing using a spectrum searching strategy
TL;DR: In this paper, the structural information of spectrum (SIOS) on a predefined frequency grid is constructed through a searching algorithm after deriving the single-sided FFT spectrum.
Dissertation
A gaussian mixture-based approach to synthesizing nonlinear feature functions for automated object detection
TL;DR: The experimental results show that the proposed approach improves the detection accuracy and efficiency of pattern object discovery, as compared to single GP-based feature synthesis methods and also a number of other object detection systems.
Proceedings ArticleDOI
Novelty Detection in Time Series Through Self-Organizing Networks: An Empirical Evaluation of Two Different Paradigms
TL;DR: This paper addresses the issue of novelty or anomaly detection in time series data by means of two different self-organizing neural network (SONN) paradigms: one builds decision thresholds from quantization errors and the other one from prediction errors.
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.