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In view of the urgent needs of the intelligent fault diagnosis of the current mine main fan, and according to the fault type characteristic of mine main fan and analyzing to generate unavoidably the causes that depending on a single information detection conduct fault diagnosis, mine main fan intelligent fault diagnosis model based on multi-sensor information fusion is presented by this paper. It...
A novel method of milling tool wear monitoring based on B-spline Fuzzy Neural Networks was proposed, we set up an experiment system of the milling tool wear monitoring and collect a variety of fault data using vibration sensor. Better characters can be got through amplitude-frequency field analysis of the signal. At last, B-spline fuzzy neural networks are adopted to monitor the tool wear. The networks...
Although Kernel Principle Component Analysis(KPCA)has been used to monitoring nonlinear processes, it is not well suited for fault diagnosis. In order to solve this problem, a new method of fault detection and diagnosis for nonlinear processes based on KPCA and Least Squares Support Vector Machine(LSSVM) is proposed. The KPCA is used to monitor faults and extract feature and LSSVM model is used to...
Kernel-based Fisher discriminant analysis (KFDA) has been widely applied in pattern recognition and classification such as face recognition. It is proved which is a powerful method for nonlinear discriminant. In this paper, it is used for fault diagnosis. It has two aspects in this work. First, the wavelet de-noising preprocessing with KFDA scheme is proposed. Second, a geometry-based feature vector...
This paper proposes a novel method based on multiple adaptive neuro-fuzzy in combination of statistic method to detect and diagnose the faults occurring in complex dynamical systems. The basic idea is to use PCA to extract the features for reducing the complexity of the data achieved from a process. The most superior features are fed into multiple ANFIS to identify different faulty conditions in order...
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