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A new batch process monitoring based on Multilinear Principal Component Analysis (MLPCA) is proposed in this paper. In the existing vector-based method on batch process monitoring such as Multiway Principal Component Analysis (MPCA), a batch data is represented as a vector in high-dimensional space. But vectorizing the batch data will lead to large storage requirements and information loss. MLPCA...
A new fault diagnosis based on Locality Preserving Projections (LPP) is proposed in this paper. The recently developed LPP is a linear dimensionality reduction technique for preserving the neighborhood structure of the data set. It is characterized by capturing the intrinsic structure of the observed data and finding more meaningful low-dimensional information hidden in the high-dimensional observations...
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...
This paper presents a new monitoring and fault diagnosis method based on Fisher discriminant analysis (FDA). Conventional process monitoring and fault diagnosis based on principal component analysis (PCA) has been widely applied to chemical process. However, such PCA-based approach is ill-suited to fault diagnosis. The reason is that this method only build normal data model whereas does not build...
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