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Fault diagnosis is significant to induction motor which has been widely used as industrial power driving sources. By fault diagnosis, proper maintenance can be arranged to avoid accidents, ensure safety and reduce maintenance costs. However, variable operating conditions and background noise always reduce effectiveness of traditional fault diagnosis methods. Currently the most advanced machine learning...
Rolling element bearing is an important component. As it is usually used in a complex environment, there are many failures occur on them. How to find the fault has become a pressing problem to be solved. The vibration signals generated by bearings are usually containing a variety of noise. The general diagnosis is divided into two stages: feature extraction and classification. Unlike conventional...
The problem of determining the appropriate number of components is important in finite mixture modeling for pattern classification. This paper considers the application of an unsupervised clustering method called AutoClass to training of orthogonal Gaussian mixture models (OGMM). Actually, the number of components in OGMM of each class is selected based on AutoClass. In this way, the structures of...
The parameters optimization of the penalty constant C and the bandwidth of the radial basis function (RBF) kernel sigma is an important step in establishing an efficient and high-performance support vector machines (SVMs) model. Aiming at optimizing the parameters of SVMs, this paper presents a grid-based ant colony optimization (ACO) algorithm to choose parameters C and sigma automatically for SVMs...
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