Car assembly line fault diagnosis based on triangular fuzzy Gaussian support vector classifier machine and modified genetic algorithm

Authors:
Qi Wu;Zhonghua Ni
Affiliations:
Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China and School of Hotel and Tourism Management, Hong Kong Polytechni ...;Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 16
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
New Support Vector Algorithms

Neural Computation
Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors

Expert Systems with Applications: An International Journal
Type-2 fuzzy logic-based classifier fusion for support vector machines

Applied Soft Computing
Classification model for product form design using fuzzy support vector machines

Computers and Industrial Engineering
Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm-support vector machines: HGASVM

Expert Systems with Applications: An International Journal
Dynamically exploring internal mechanism of stock market by fuzzy-based support vector machines with high dimension input space and genetic algorithm

Expert Systems with Applications: An International Journal
A dynamic chain-like agent genetic algorithm for global numerical optimization and feature selection

Neurocomputing
A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression

Expert Systems with Applications: An International Journal
Support vector machine with genetic algorithm for forecasting of key-gas ratios in oil-immersed transformer

Expert Systems with Applications: An International Journal
A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier

Expert Systems with Applications: An International Journal
Fault diagnosis of pneumatic systems with artificial neural network algorithms

Expert Systems with Applications: An International Journal
Fault diagnosis of power transformer based on support vector machine with genetic algorithm

Expert Systems with Applications: An International Journal
The hybrid forecasting model based on chaotic mapping, genetic algorithm and support vector machine

Expert Systems with Applications: An International Journal
Letters: A genetic algorithm for solving the inverse problem of support vector machines

Neurocomputing
System reliability forecasting by support vector machines with genetic algorithms

Mathematical and Computer Modelling: An International Journal

A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization

Applied Soft Computing

Quantified Score

Hi-index	12.05

Visualization

Abstract

This paper presents a new version of fuzzy support vector classifier machine to diagnose the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of Gaussian noises and uncertain data in complex fuzzy fault system modeling, the input and output variables are described as fuzzy numbers. Then by integrating fuzzy theory, Gaussian loss function and v-support vector classifier machine, the fuzzy Gaussian v-support vector regression machine (Fg-SVCM) is proposed. To seek the optimal parameters of Fg-SVCM, the modified genetic algorithm (GA) is also applied to optimize parameters of Fg-SVCM. A diagnosing method based on Fg-SVCM and GA is put forward. The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on Fg-SVCM and PSO is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than other v-SVCMs.