K-nearest neighbors based nonlinear process monitoring using kernel EPCA

Loubna El Fattahi; El Hassan Sbai

doi:10.1117/12.2557267

31 January 2020 K-nearest neighbors based nonlinear process monitoring using kernel EPCA

Loubna El Fattahi, El Hassan Sbai

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114330B (2020) https://doi.org/10.1117/12.2557267
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

In this paper, we introduce a new technique for nonlinear monitoring process relying on kernel entropy principal component analysis (KEPCA). KEPCA can transform input data into high-dimensional feature space using the nonlinear kernel function and determine the number of principal components (PCs) based on the computation of the entropy. The retained PCs are the ones that explain the maximum entropy of data in the feature space. Then, we introduce a new approach to calculate the upper control limits (UCLs) for the squared prediction error (SPE) and the T² Hotelling in the feature space based on the density estimation via the k-nearest neighbors (kNN) estimator. The abovementioned approaches were applied to fault detection for the benchmark Tennessee Eastman process (TE). Results were robust and supply better performance than KPCA.

Citation Download Citation

Loubna El Fattahi and El Hassan Sbai "K-nearest neighbors based nonlinear process monitoring using kernel EPCA", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114330B (31 January 2020); https://doi.org/10.1117/12.2557267

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