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Features selection (FS) techniques have an apparent need in many complex engineering applications especially the bearing fault diagnosis of low-speed industrial motor. The main goal of an FS algorithm is to select the most discriminant features subset from a high-dimension features vector that increases the model performance by reducing the redundant and irrelevant fault features. This paper proposes...
This paper presents an on-line fault diagnosis software in primary distribution feeders. The software is written in DELPHI and C++ languages and its interaction with the operator is made in a very friendly environment. The input data are the currents of the feeder per phase, monitored only in the substation. An artificial immune system was developed using the negative selection algorithm to detect...
In this paper, an evolutionary hybrid approach is studied for fault diagnosis and it is applied to classify the loopers faults in hot rolling process. The algorithm called evolutionary KPCA-LSSVM is the combination of genetic algorithm (GA), kernel principal component analysis (KPCA) and Least Squares Support Vector Machine (LSSVM), which can obtain better fault recognition rate. Firstly, kernel function...
To solve the problem of equipment fault diagnosis, the paper proposes a fault diagnosis model based on Support Vector Machines (SVM) and studies the parameters that influence model accuracy. On the basis of analyzing model parameters influence, A new kind of evaluation function about algorithm accuracy and the Genetic algorithm of the global optimization parameters selection are presented. According...
Failure of gearbox is very complex, so it is difficult to use the mathematical model to describe their faults. In this study, an intelligent diagnostic method based on genetic-support vector machine (GSVM) approach is presented for fault diagnosis of gearbox. The performance of the GSVM system proposed in this study is evaluated by gearbox in the wood-wool working device. The test results show that...
In the fault diagnosis based on support vector machine (SVM), SVM parameters are mostly selected artificially or obtained through experiment time after time, a certain and effective method has not been found. Aiming at this problem, a method optimizing the SVM parameters with Monkey-King genetic algorithm (MKSVM) is presented. In the built model the optimized parameters are used, and the superiority...
In the symptom feature discovery, genetic programming has the shortage of premature convergence. So a new feature generation method based on immune programming is put forward. The new features are constructed by polynomial expressions of the original features. And then, with the immune operators such as antibody representation and mutation of tree-like structure, affinity function defined by classification...
A soft fault diagnosis method for analog circuits based on support vector machine (SVM)is developed in this paper. SVM is a novel machine learning method based on the statistical learning theory, which is a powerful tool for solving the problem with small sampling, nonlinearity and high dimension. The multi-classification SVM methods including one versus rest, one versus one, and decision directed...
In this paper, non-dimensional parameter immune detectors are constructed using some non-dimensional parameter which are more hypersensitive to single fault combined with negative selection mechanism of artificial immune system. Regarding complex fault as a type new occurring fault and is gone into fault space to train, fault character can be extracted, and fault can be diagnosed. The machine unit...
A novel dynamic clustering algorithm based on artificial immune network is proposed in this paper. Firstly artificial immune network can get memory polls, which effectively represent the characteristics of fault samples, using the ability of immune memory and learning. Then genetic algorithm is used to dynamically optimize and select the best memory cells as initial clustering centers of kernel-based...
Decision tree support vector machine (DTSVM), which combines SVM and decision tree using the concept of dichotomy, is proposed to solve the multi-class fault diagnosis tasks. Since the classification performance of DTSVM is closely related to its structure, genetic algorithm is introduced into the formation of decision tree, to cluster the multi-classes with maximum distance between the clustering...
A genetic-algorithm-based selective principal component neural network method for fault diagnosis system in a multilevel inverter is proposed in this paper. Multilayer perceptron (MLP) networks are used to identify the type and location of occurring faults from inverter output voltage measurement. Principal component analysis (PCA) is utilized to reduce the neural network input size. A lower dimensional...
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