scholarly journals A Novel Clustering Method Combining ART with Yu’s Norm for Fault Diagnosis of Bearings

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Zengbing Xu ◽  
Youyong Li ◽  
Zhigang Wang ◽  
Jianping Xuan
Keyword(s):  
Fault Diagnosis ◽  
Test Time ◽  
Model Parameters ◽  
Clustering Methods ◽  
Clustering Method ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Application Range ◽  
Fuzzy Art ◽  
Related Information

Clustering methods have been widely applied to the fault diagnosis of mechanical system, but the characteristic that the number of cluster needs to be determined in advance limits the application range of the method. In this paper, a novel clustering method combining the adaptive resonance theory (ART) with the similarity measure based on the Yu’s norm is presented and applied to the fault diagnosis of rolling element bearings, which can be adaptive to generate the number of cluster by the vigilance parameter test. Time-domain features, frequency-domain features, and time series model parameters are extracted to demonstrate the fault-related information about the bearings, and then considering the irrelevance or redundancy of some features many salient features are selected by an improved distance discriminant technique and input into the proposed clustering method to diagnose the faults of bearings. The experiment results confirmed that the proposed clustering method can diagnose the fault categories accurately and has better diagnosis performance compared with fuzzy ART and Self-Organizing Feature Map (SOFM).

A Novel Fault Diagnosis Method Using PCA and ART-Similarity Classifier Based on Yu’s Norm

2009 ◽  
Vol 413-414 ◽  
pp. 569-574 ◽  
Author(s):  
Zeng Bing Xu ◽  
Jian Ping Xuan ◽  
Tie Lin Shi ◽  
Bo Wu ◽  
You Min Hu
Keyword(s):  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Principal Component ◽  
Feature Maps ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Fuzzy Art ◽  
Comparison Results ◽  
Rolling Element ◽  
Diagnosis Method

In this paper, a novel similarity classifier which synthesizes the adaptive resonance theory (ART) and the similarity classifier based on the Yu’s norm is proposed. The proposed ART-similarity classifier can not only carry out training without forgetting previously trained patterns but also be adaptive to changes in the environment. In order to test the proposed classifier, it is applied to the fault diagnosis of rolling element bearings. Before application to the fault diagnosis of bearings, considering computation burden principal component analysis (PCA) is proposed to reduce the number of features. The PCs are input the proposed classifier to diagnose the faulty bearings. The experiment results testify that the proposed classifier can identify the faults accurately. Furthermore, in order to validate the effectiveness of the proposed classifier further, it compares with other neural networks, such as the fuzzy ART, self-organising feature maps (SOFMs) and radial basis function (RBF) neural network through diagnosing the bearings under the same conditions. The comparison results confirm the superiority of the proposed method.

Gearbox Fault Diagnosis Based on Selective Integrated Soft Competitive Learning Fuzzy Adaptive Resonance Theory

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Xiao-Jin Wan ◽  
Licheng Liu ◽  
Zengbing Xu ◽  
Zhigang Xu
Keyword(s):  
Fault Diagnosis ◽  
Lateral Inhibition ◽  
Adaptive Resonance Theory ◽  
Competitive Learning ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Fuzzy Art ◽  
Diagnosis Model ◽  
Fuzzy Adaptive ◽  
Inhibition Theory

In this work, a soft competitive learning fuzzy adaptive resonance theory (SFART) diagnosis model based on multifeature domain selection for the single symptom domain and the single-target model is proposed. In order to solve the problem that the performance of traditional fuzzy ART (FART) is affected by the order of sample input, the similarity criterion of YU norm is introduced into the fuzzy ART network. In the meanwhile, the lateral inhibition theory is introduced to solve the wasteful problem of fuzzy ART mode node. By combining YU norm and lateral inhibition theory with fuzzy ART network, a soft competitive learning ART neural network diagnosis model that allows multiple mode nodes to learn simultaneously is designed. The feature parameters are extracted from the perspectives of time domain, frequency domain, time series model, wavelet analysis, and wavelet packet energy spectrum analysis, respectively. To further improve the diagnostic accuracy, the selective weighted majority voting method is integrated into the diagnosis model. Finally, the selected feature parameters are inputted to the integrated model to complete the fault classification and diagnosis. Finally, the proposed method is verified with a gearbox fault diagnosis test.

scholarly journals Minimal Structural ART Neural Network and Fault Diagnosis Application of Gas Turbine

2016 ◽  
Vol 10 (1) ◽  
pp. 13-22
Author(s):  
Qingyang Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Cognitive Process ◽  
Simple Structure ◽  
High Efficiency ◽  
Complex Structures ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Binary Input

Adaptive Resonance Theory (ART) model is a special neural network based on unsupervised learning which simulates the cognitive process of human. However, ART1 can be only used for binary input, and ART2 can be used for binary and analog vectors which have complex structures and complicated calculations. In order to improve the real-time performance of the network, a minimal structural ART is proposed which combines the merits of the two models by subsuming the bottom-up and top-down weight. The vector similarity test is used instead of vigilance test. Therefore, this algorithm has a simple structure like ART1 and good performance as ART2 which can be used for both binary and analog vector classification, and it has a high efficiency. Finally, a gas turbine fault diagnosis experiment exhibits the validity of the new network.

A Supervised Learning Network Based on Adaptive Resonance Theory

1997 ◽  
Vol 08 (02) ◽  
pp. 239-246 ◽  
Author(s):  
J. Zhou ◽  
S. Bennett
Keyword(s):  
Supervised Learning ◽  
Network Architecture ◽  
Recognition Task ◽  
Classification Problem ◽  
Spatial Distance ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Fuzzy Art ◽  
Logistic Discrimination ◽  
Network Modules

A neural network architecture, fuzzy ART with logistic discrimination (ART-LD), is introduced as a method of realising the pattern recognition task in a supervised learning manner. The system is formed by the hierarchical organisation of two network modules: a fuzzy ART and a logistic discrimination. The learning consists of two separate stages. Firstly, the fuzzy ART and a logistic discrimination. The learning consists of two separate stages. Firstly, the fuzzy ART module self-organises the input patterns into category clusters, whose operations are governed by fuzzy set theory and "competitive learning" dynamics that ensure fast and stable learning. Then the outputs, which can be interpreted as fuzzy memberships of an input pattern to the encoded categories, provide the spatial distance informtion that is generalised by the subsequent logistic discrimination to give the final prediction. Examples are presented, and the generalisation capabilities of ART-LD are demonstrated through two simulated and one real classification problem.

scholarly journals Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Dan Yang ◽  
Hailin Mu ◽  
Zengbing Xu ◽  
Zhigang Wang ◽  
Cancan Yi ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Classification Performance ◽  
Statistical Characteristics ◽  
Neural Network Ensemble ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Ensemble Technique ◽  
The Neural Network ◽  
Rolling Element

This paper presents a novel method for fault diagnosis based on an improved adaptive resonance theory (ART) neural network and ensemble technique. The method consists of three stages. Firstly, the improved ART neural network is comprised of the soft competition technique based on fuzzy competitive learning (FCL) and ART based on Yu’s norm, the neural nodes in the competition layer are trained according to the degree of membership between the mode node and the input, and then fault samples are classified in turn. Secondly, with the distance evaluation technique, the optimal features are obtained from the statistical characteristics of original signals and wavelet coefficients. Finally, the optimal features are input into the neural network ensemble (NNE) based on voting method to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the neural network ensemble can reliably classify different fault categories and the degree of faults, which has a better classification performance compared with the single neural network.

Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis

2019 ◽  
Vol 35 (18) ◽  
pp. 3339-3347 ◽  
Author(s):  
Rachel Jeitziner ◽  
Mathieu Carrière ◽  
Jacques Rougemont ◽  
Steve Oudot ◽  
Kathryn Hess ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dimensional Space ◽  
Global Gene Expression ◽  
Test Sample ◽  
Topological Data Analysis ◽  
Supplementary Information ◽  
Control Group ◽  
Clustering Methods ◽  
Clustering Method ◽  
Application Range

Abstract Motivation Unbiased clustering methods are needed to analyze growing numbers of complex datasets. Currently available clustering methods often depend on parameters that are set by the user, they lack stability, and are not applicable to small datasets. To overcome these shortcomings we used topological data analysis, an emerging field of mathematics that discerns additional feature and discovers hidden insights on datasets and has a wide application range. Results We have developed a topology-based clustering method called Two-Tier Mapper (TTMap) for enhanced analysis of global gene expression datasets. First, TTMap discerns divergent features in the control group, adjusts for them, and identifies outliers. Second, the deviation of each test sample from the control group in a high-dimensional space is computed, and the test samples are clustered using a new Mapper-based topological algorithm at two levels: a global tier and local tiers. All parameters are either carefully chosen or data-driven, avoiding any user-induced bias. The method is stable, different datasets can be combined for analysis, and significant subgroups can be identified. It outperforms current clustering methods in sensitivity and stability on synthetic and biological datasets, in particular when sample sizes are small; outcome is not affected by removal of control samples, by choice of normalization, or by subselection of data. TTMap is readily applicable to complex, highly variable biological samples and holds promise for personalized medicine. Availability and implementation TTMap is supplied as an R package in Bioconductor. Supplementary information Supplementary data are available at Bioinformatics online.

Car Styling Perceptual Modeling Based on Fuzzy Rules

2012 ◽  
Vol 201-202 ◽  
pp. 794-797 ◽  
Author(s):  
Xin Ru Wan ◽  
Jian Ming Che ◽  
Lu Han
Keyword(s):  
Statistical Methods ◽  
Fuzzy Rules ◽  
Generalized Regression Neural Network ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Perceptual Model ◽  
Automotive Market ◽  
Fuzzy Art ◽  
Analysis System ◽  
Extraction Model

Abstract. In today’s automotive market with intense competition, car styling preferences directly affect designer’s design outcome. At the meantime, there are various types of car styles, however users’ emotional evaluations of them are messy. Thus establishing a perceptual extraction model of vehicles is necessary. Under common circumstances, we quantify the emotional human perceptual through digitized human perceptual data obtained by questionnaires. However, it is emotional and thus difficult to analyze. In order to analyze perceptual data, the general method is statistical methods, such as factor analysis system. However, the use of statistical methods alone is not sufficient to handle the emotional data, because this method cannot handle non-linear intrinsic emotional data. This paper will discuss the using of fuzzy rules and through inductive data analysis to extract a common perceptual model of consumers’ emotional tendencies towards vehicles. In this paper, methods of variance predicted generalized regression neural network (VP-GRNN) and fuzzy adaptive resonance theory (fuzzy ART) were used to build a common perceptual model.

Clustering Large Data with Mixed Values Using Extended Fuzzy Adaptive Resonance Theory

2016 ◽  
Vol 4 (3) ◽  
pp. 617
Author(s):  
Asadi Srinivasulu ◽  
Gadupudi Dakshayani
Keyword(s):  
Mixed Type ◽  
Traditional Approach ◽  
Feature Learning ◽  
Large Data ◽  
Adaptive Resonance Theory ◽  
Unsupervised Feature Learning ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Fuzzy Art ◽  
Fuzzy Adaptive

<p>Clustering is one of the technique or approach in content mining and it is used for grouping similar items. Clustering software datasets with mixed values is a major challenge in clustering applications. The previous work deals with unsupervised feature learning techniques such as k-Means and C-Means which cannot be able to process the mixed type of data. There are several drawbacks in the previous work such as cluster tendency, partitioning, less accuracy and less performance. To overcome all those problems the extended fuzzy adaptive resonance theory (EFART) came into existence which indicates that the usage of fuzzy ART with some traditional approach. This work deals with mixed type of data by applying unsupervised feature learning for achieving the sparse representation to make it easier for clustering algorithms to separate the data. The advantages of extended fuzzy adaptive resonance theory are high accuracy, high performance, good partitioning, and good cluster tendency. This EFART adopts unsupervised feature learning which helps to cluster the large data sets like the teaching assistant evaluation, iris and the wine datasets. Finally, the obtained results may consist of clusters which are formed based on the similarity of their attribute type and values.</p>

Auto Tuning Algorithm for Vigilance Parameter in the Adaptive Resonance Theory Model and its Application to Fault Diagnosis System of Thermal Power Plants

2011 ◽  
Author(s):  
Takaaki Sekiai ◽  
Naohiro Kusumi ◽  
Yoshinari Hori ◽  
Satoru Shimizu ◽  
Masayuki Fukai
Keyword(s):  
Fault Diagnosis ◽  
Power Plants ◽  
Thermal Power ◽  
Parameter Tuning ◽  
Adaptive Resonance Theory ◽  
Thermal Power Plants ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Diagnosis Accuracy ◽  
Fault Diagnosis System

In order to operate thermal power plants safely, early detection of equipment failure signs is one of the most important issues. To detect the signs before an alarm is issued in the existing monitoring system, we developed a fault diagnosis system based on the Adaptive Resonance Theory (ART). The vigilance parameter, which is a design parameter in the ART model, was shown to influence the diagnosis accuracy. Fixing the value of the vigilance parameter also had problems: we needed to use time-consuming trial and error, and we needed to have empirical knowledge of the parameter tuning. In this paper, using simulations we demonstrated the relationship between the vigilance parameter and diagnosis accuracy. Furthermore, to overcome the problems of the vigilance parameter tuning, we have proposed an auto tuning algorithm to make the parameter the optimum value. The performance of the proposed algorithm was evaluated in several case studies using gas turbine plant data. The effectiveness of the proposed algorithm was confirmed by the obtained results.

scholarly journals ARTgrid: A Two-Level Learning Architecture Based on Adaptive Resonance Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Marko Švaco ◽  
Bojan Jerbić ◽  
Filip Šuligoj
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Adaptive Resonance Theory ◽  
Neural Network Architecture ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Fuzzy Art ◽  
Search Mechanism ◽  
Robotic Applications ◽  
Computation Speed

This paper proposes a novel neural network architecture based on adaptive resonance theory (ART) called ARTgrid that can perform both online and offline clustering of 2D object structures. The main novelty of the proposed architecture is a two-level categorization and search mechanism that can enhance computation speed while maintaining high performance in cases of higher vigilance values. ARTgrid is developed for specific robotic applications for work in unstructured environments with diverse work objects. For that reason simulations are conducted on random generated data which represents actual manipulation objects, that is, their respective 2D structures. ARTgrid verification is done through comparison in clustering speed with the fuzzy ART algorithm and Adaptive Fuzzy Shadow (AFS) network. Simulation results show that by applying higher vigilance values (ρ>0.85) clustering performance of ARTgrid is considerably better, while lower vigilance values produce comparable results with the original fuzzy ART algorithm.

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