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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Patent
Verfahren zum Überwachen von rotierenden Maschinen A method for monitoring of rotating machinery
TL;DR: In this paper, the authors present a method for monitoring a rotating machine, which is provided with a plurality of sensors for detecting physical parameters and is operated in variable operating states, wherein it is a wind power installation in the machine and data regarding time points and sensors are obtained.
Proceedings ArticleDOI
On-line Monitoring of Transportation Condition for 110kV Vehicular Mobile Transformers
TL;DR: In this article, a transportation condition monitoring system for vehicular mobile transformers is introduced and developed, where the transportation process of a 110kV/40MVA VMT is monitored from the manufacturer in Jiangsu Province to the operating unit in Guangdong Province from 19:15 to 14:00 on December 13, 2018.
A New Statistical Approach for Recognizing and Classifying Patterns of X Control Charts
M. Kabiri,M. S. Owlia +1 more
TL;DR: A statistical decision making approach to recognize and classify the patterns of control charts and shows that the proposed method has more accurate interpretable results without training requirement.
Proceedings ArticleDOI
Review of Artificial Intelligence-based Bearing Vibration Monitoring
TL;DR: In this article, a review of the development of machine learning and deep learning methods for bearing vibration monitoring is presented, in particular, several issues of current studies are addressed and future development trends are discussed.
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.