Abstract
In the present study, we introduce a new approach for the nonlinear monitoring process based on kernel entropy principal component analysis (KEPCA) and the notion of inertia. KEPCA plays double roles. First, it reduces the data in the high-dimensional space. Second, it constructs the model. Before data reduction, KEPCA transforms input data into high-dimensional feature space based on a nonlinear kernel function and automatically determines the number of principal components (PCs) based on the computation of the inertia. The retained PCs express the maximum inertia entropy of data in the feature space. Then, we use the Parzen window estimator to compute the upper control limit (UCL) for inertia-based KEPCA instead of the Gaussian assumption. Our second contribution concerns a new combined index based on the monitoring indices T2 and SPE in order to simplify the detection task of the fault and prevent any confusion. The proposed approaches have been applied to process fault detection and diagnosis for the well-known benchmark Tennessee Eastman process (TE). Results were performing.
Highlights
Since faults are integrated into most industrial processes’ execution, fault detection and identification is mandatory in monitoring and diagnosing different processes, in order to ensure a riskless functionality and improve the productivity of an industrial process
A data preprocessing is required to get rid of redundant data and extract only the significant information. is limitation has led to the development of multivariate statistical methods, such as principal component analysis (PCA) [1, 2], recursive PCA [3], entropy PCA [4], kernel principal component analysis (KPCA) [5,6,7], and modified kernel PCA [8]
We suggest applying our method, inertia-based KPCA combined to the Shannon entropy [16, 17], for the first time to monitor the chemical Tennessee Eastman process
Summary
Since faults are integrated into most industrial processes’ execution, fault detection and identification is mandatory in monitoring and diagnosing different processes, in order to ensure a riskless functionality and improve the productivity of an industrial process. We need to provide as an input the variance expressed by the maintained PCs. we need to provide as an input the variance expressed by the maintained PCs To eliminate this deficiency, we suggest applying our method, inertia-based KPCA combined to the Shannon entropy [16, 17], for the first time to monitor the chemical Tennessee Eastman process. We demonstrate that the proposed inertia-based KEPCA method resolves the shortcoming of the standard KPCA method, in terms of selecting the features automatically and in term of fault detection.
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