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To reduce data-storage costs and enhance high accuracy of industrial process fault detection, a data driven fault diagnosis method is proposed based on diffusion maps and hidden Markov model. Firstly, the correlation dimension of sample data is calculated. Secondly, the high-dimensional eigenvectors are extracted into low-dimensional manifold space by diffusion maps. Finally, the low-dimensional eigenvectors...
In modern industrial process control, most traditional fault detection and diagnosis methods have been researched and applied widely. Recently, a novel MSPC method known as DISSIM has been developed focusing on continuous processes and batch processes, the result is significant. Firstly, this paper describes a progressive multiple variables fault detection and diagnosis method based on dissimilarity...
Machine learning technology is already used in a wide range of industrial processes. For nonlinear, multimode and non-Gaussian batch process, a improved fault detection method based on kNN is proposed. Firstly KPCA is introduced to get kernel principal components (KPCs) and to form modeling data set. Secondly, k nearest neighbors of each KPC are found and the sum of k nearest neighbor squared distances...
Multiway Principal Component Analysis (MPCA) has been widely used to monitor multivariate batch process. In MPCA method, the batch data is represented as a vector in high-dimensional space, resulting in large computation, storage space and loss of important information inevitably. A new batch process fault diagnosis method based on the 2-Dimensional Principal Component Analysis (2DPCA) is presented...
This paper presents a fault diagnosis approach that is the combination with Gaussian mixture models and variable reconstruction. Usually, the traditional multivariate process monitoring techniques has the fundamental assumption that the operating data should follow a unimodal Gaussian distribution, but it often becomes invalid due to the practice different operating conditions. The Gaussian mixture...
The number of principal components (PCs) is critical parameter of principal component analysis (PCA) and its selection determines the performance of fault detection. In this paper, we pay attention to the relationship between selection of the number of PCs and sensitivity of fault detection. The fault signal-to-noise ratio (fault SNR) that depends on the number of PCs for a certain fault is presented...
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