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In this paper, we propose a novel algorithm called active learning-based nearest neighbor mean distance (ALNNMD) novelty detection method. And this method can be applied to huge collections of data. ALNNMD is based on the framework of active learning. In each iteration it can choose the instance that most optimizes the current novelty detection model from the data pool, and then remove this instance...
Modern industrial processes often have multiple operating modes because of their complexity and manufacturing strategy changes. Meanwhile, the within-mode process data can also be nonlinear and non-Gaussian distributed. To deal with the problems above, Gaussian mixture model (GMM) has recently been applied to multimode process monitoring which achieves better performance than traditional multivariate...
Interpolation is a mathematical method to obtain new data points within the range of several known data points. The basic idea of interpolation is to construct a continuous function which passes through all the known data points so that new data points can be obtained from this function. In many practical problems, the interpolation function should be monotone due to physical meanings. However, nearly...
Principle Component Analysis (PCA) has been used widely for process monitoring in industry systems. But the data drifting problem, which commonly exists in the actual process, disables the monitoring model, and subsequently makes the monitoring system come out with plenty of false alarm. Therefore the efficiency of PCA based process monitoring is degraded in practical use. This paper presents an incremental...
Optical Emission Spectra (OES) is a widely used signal in plasm etching. In this paper, an OES structural feature based fault detection method is proposed. Firstly, a template of normal OES curves is extracted via non-negative matrix factorization. Then singular points of the curves are detected by local matching based on the template. The magnitude and occurrence time of these singular points form...
A robust input-output decoupling control strategy for stator flux and torque of induction motors (IM) is proposed. In order to avoid full state measurements, robust decoupling controllers of stator flux and torque are developed. The main drawback of the proposed strategy is the requirement of stator flux measurement, and the future research is to remove it by introducing a state observer. Experimental...
A new torque direct control of induction machines is presented. The space vector modulation (SVM) technique is applied to the voltage source inverter, so that the torque, flux and stator current ripples are reduced. As compared to other SVM-based DTC (direct torque control) methods, stator flux and torque controllers of the new approach are developed on variable structure control theory and don't...
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