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.

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>

Knowledge-based Fuzzy Adaptive Resonance Theory and Its Application to the Analysis of Gene Expression in Plants

2008 ◽  
Vol 106 (6) ◽  
pp. 587-593 ◽  
Author(s):  
Hiro Takahashi ◽  
Hidekazu Iwakawa ◽  
Sachiko Nakao ◽  
Takahiro Ojio ◽  
Ryo Morishita ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adaptive Resonance Theory ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Knowledge Based ◽  
Fuzzy Adaptive

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).

scholarly journals An Enhanced Weight Update Method for Simplified ARTMAP to Classify Groundwater Data

2021 ◽  
Vol 16 (5) ◽  
pp. 517-524
Author(s):  
Relangi Naga Durga Satya Siva Kiran ◽  
Chaparala Aparna ◽  
Sajja Radhika
Keyword(s):  
Groundwater Quality ◽  
Nonlinear Models ◽  
Theory Model ◽  
Adaptive Resonance Theory ◽  
Resonance Theory ◽  
Method Performance ◽  
Adaptive Resonance ◽  
Optimal Weights ◽  
Conventional Methods ◽  
Fuzzy Adaptive

The groundwater for aquatic purposes must be assessed prior to its consumption. Huge number of conventional methods are existing for assessing the quality of groundwater. The water quality index is one of the important conventional methods to assess the groundwater quality. But the conventional methods alone are not enough to assess groundwater quality as well as classify based on its purity. In this paper, we propose an enhanced weight update method for Simplified Fuzzy Adaptive Resonance Theory model to classify the groundwater quality depending on the relative weights of the groundwater quality parameters. Finding the optimal weights is the key to achieve better accuracy of the model, most of the nonlinear models fails to exhibit good accuracy if they fail to learn the optimal weights in the learning process. The aim of the work is to find the good fit between the predicted and the actual groundwater quality grades by identifying the optimal weights of the network by the enhanced weight update method. The Simplified Fuzzy Adaptive Resonance Theory map with the enhanced weight update method performance is justified by comparing it with the Simplified Fuzzy Adaptive Resonance Theory Map. The enhanced weight update method improves the accuracy of the Simplified Fuzzy Adaptive Resonance Theory Map in classifying and predicting the groundwater quality.

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