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In this paper, we propose a novel fault detection method for multivariate industrial processes. The method is based on AutoEncoder. AutoEncoder is a single-hidden-layer neural network that can learn low-dimensional nonlinear representations for high-dimensional data. In the proposed fault detection method, offline normal data are used to train an AutoEncoder, which is then used for online fault detection...
Blast furnace gas (BFG) system of steel enterprise generally accompanies with multi-dimension and nonlinear features. It's a hard assignment for energy scheduling operators to make real-time scheduling decision when monitoring such system. In this study, a novel dimensionality reduction method named Space Direction Neighborhood Preserving Embedding (SDNPE) is proposed for the BFG system monitoring...
It is quite challenging to monitor an ironmaking process due to some of its special characteristics such as lack of direct measurements and strong disturbances. Hence extracting robust features of the normal process from complex historical data is vitally important. Denoising autoencoder (dA), a recently developed deep learning technique, has become a popular tool to extract and compose robust features...
Due to the multi-condition characteristic of tobacco ultrahigh-speed cellophane sealing machine, the existing fault monitoring method can't meet the need of efficient cigarette production. Based on the analyzing the running characteristics, a fault monitoring and diagnosis approach method of ultrahigh-speed cellophane sealing machine was proposed to solve several key points, such as condition partition...
It is quite challenging to monitor an ironmaking process because of its special characteristics such as frequent fluctuations and lack of direct measurements. To tackle these issues, a two-stage PCA based monitoring method was proposed in our previous work. However, only one type of operating anomaly was considered and the historical data of one accident was utilized. To further evaluate the performance...
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