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
A general anomaly detection framework for fleet-based condition monitoring of machines
Kilian Hendrickx,Kilian Hendrickx,Wannes Meert,Yves Mollet,Yves Mollet,Johan Gyselinck,Bram Cornelis,Konstantinos Gryllias,Jesse Davis +8 more
TL;DR: This work proposes an unsupervised, generic, anomaly detection framework for fleet-based condition monitoring that uses generic building blocks and offers three key advantages: it allows incorporating domain expertise by user-defined comparison measures, easy interpretability allows a domain expert to validate the predictions made by the framework.
Journal ArticleDOI
Fault diagnosis of rolling bearings based on Marginal Fisher analysis
TL;DR: An intelligent fault diagnosis method based on Marginal Fisher analysis (MFA) is put forward and applied to rolling bearings and the results validate the feasibility and effectiveness of the proposed Fault diagnosis method, compared with the other three similar approaches.
Book ChapterDOI
Time Series Clustering for Anomaly Detection Using Competitive Neural Networks
TL;DR: Performance comparisons among five competitive neural networks (SOM, Kangas' Model, TKM, RSOM and Fuzzy ART) on simulated and real-world time series data are carried out.
Journal ArticleDOI
Nondestructive diagnosis of flip chips based on vibration analysis using PCA-RBF
TL;DR: In this article, a non-destructive diagnosis system based on vibration analysis is proposed, where the flip chip is excited by air-coupled ultrasound and raw vibration signals are measured by a laser scanning vibrometer.
Journal ArticleDOI
Robust adaptive learning approach to self-organizing maps
TL;DR: A new intelligent adaptive learning SOM is presented that overcomes the disadvantages of the conventional SOM by deriving a new variable learning rate that can adaptively achieve the optimal weights and obtain the winner neurons with a shorter learning process time.
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.