The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns

The performance degradation assessment of ball bearings is of great importance to increase the efficiency and the reliability of rotating mechanical systems. The large dimensionality of feature space introduces a lot of noise and buries the potential information about faults hidden in the feature data. This paper proposes a novel health assessment method facilitated with two compatible methods, namely curvilinear component analysis and self-organizing map network. The novelty lies in the implementation of a vector quantization approach for the sub-manifolds in the feature space and to extract the fault signatures through nonlinear mapping technique. Curvilinear component analysis is a nonlinear mapping tool that can effectively represent the average manifold of the highly folded information and further preserves the local topology of the data. To answer the complications and to accomplish reliability and accuracy in bearing performance degradation assessment, the work is carried out with following steps; first, ensemble empirical mode decomposition is used to decompose the vibration signals into useful intrinsic mode functions; second, two fault features i.e. singular values and energy entropies are extracted from the envelopes of the intrinsic mode function signals; third, the extracted feature vectors under healthy conditions, further reduced with curvilinear component analysis are used to train the self-organizing map model; finally, the reduced test feature vectors are supplied to the trained self-organizing map and the confidence value is obtained. The effectiveness of the proposed technique is validated on three run-to-failure test signals with the different type of defects. The results indicate that the proposed technique detects the weak degradation earlier than the widely used indicators such as root mean square, kurtosis, self-organizing map-based minimum quantization error, and minimum quantization error-based on the principal component analysis.

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Growing Hierarchical Self-Organizing Map Using Category Utility

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194016500108 ◽

2016 ◽

Vol 26 (02) ◽

pp. 217-237

Author(s):

Kazushi Murakoshi ◽

Satoshi Fujikawa

Keyword(s):

Knowledge Representation ◽

Unsupervised Learning ◽

Input Data ◽

Computer Experiments ◽

Quantization Error ◽

Self Organizing Map ◽

Conceptual Clustering ◽

The Hierarchical Structure ◽

Evaluation Measure ◽

Self Organizing

In order to automatically obtain hierarchical knowledge representation from a certain data, an unsupervised learning method has been developed that overcomes two problems of the growing hierarchical self-organizing map (GHSOM) method, which uses the quantization error, the deviation of the input data, as evaluation measure of the growing maps: proper control of the growth process of each map is difficult due to the use of the quantization error and the clusters in the hierarchical structure may be excessively subdivided. This improved GHSOM method uses the category utility (CU), a measure used in conceptual clustering for predicting the preferred level of categorization, instead of the quantization error. The CU is useful for organizing the clustering so that people can effortlessly understand it. The basic principle of this method is that the growth and unification processes are appropriately and autonomously controlled by the CU. Evaluation using computer experiments showed that the proposed method can automatically construct an appropriate hierarchical and topological knowledge representation for high-dimensional input data through unsupervised learning. It also showed that it is easier to use and more effective than the original conventional GHSOM method using the quantization error as an evaluation measure.

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Using the Quantization Error from Self-Organizing Map (SOM) Output for Fast Detection of Critical Variations in Image Time Series

10.20944/preprints201710.0166.v2 ◽

2018 ◽

Cited By ~ 1

Author(s):

Birgitta Dresp-Langley ◽

John Mwangi Wandeto ◽

Henry Okola Nyongesa

Keyword(s):

Time Series ◽

Linear Trend ◽

Satellite Image ◽

Quantization Error ◽

Environmental Data ◽

Self Organizing Map ◽

Statistical Decision ◽

Fast Analysis ◽

Image Series ◽

Self Organizing

The quantization error (QE) from Self-Organizing Map (SOM) output after learning is exploited in this studies. SOM learning is applied on time series of spatial contrast images with variable relative amount of white and dark pixel contents, as in monochromatic medical images or satellite images. It is proven that the QE from the SOM output after learning provides a reliable indicator of potentially critical changes in images across time. The QE increases linearly with the variability in spatial contrast contents of images across time when contrast intensity is kept constant. The hitherto unsuspected capacity of this metric to capture even the smallest changes in large bodies of image time series after using ultra-fast SOM learning is illustrated on examples from SOM learning studies on computer generated images, MRI image time series, and satellite image time series. Linear trend analysis of the changes in QE as a function of the time an image of a given series was taken gives proof of the statistical reliability of this metric as an indicator of local change. It is shown that the QE is correlated with significant clinical, demographic, and environmental data from the same reference time period during which test image series were recorded. The findings show that the QE from SOM, which is easily implemented and requires computation times no longer than a few minutes for a given image series of 20 to 25, is useful for a fast analysis of whole series of image data when the goal is to provide an instant statistical decision relative to change/no change between images.

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Improving Feature Map Quality of SOM Based on Adjusting the Neighborhood Function

Sustainability in Urban Planning and Design ◽

10.5772/intechopen.89233 ◽

2020 ◽

Author(s):

Le Anh Tu

Keyword(s):

Quantization Error ◽

The Self ◽

Self Organizing Map ◽

Relevant Research ◽

Feature Map ◽

Neighborhood Function ◽

Self Organizing

This chapter presents a study on improving the quality of the self-organizing map (SOM). We have synthesized the relevant research on assessing and improving the quality of SOM in recent years, and then proposed a solution to improve the quality of the feature map by adjusting parameters of the Gaussian neighborhood function. We have used quantization error and topographical error to evaluate the quality of the obtained feature map. The experiment was conducted on 12 published datasets and compared the obtained results with some other improving neighborhood function methods. The proposed method received the feature map with better quality than other solutions.

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