Riadh ToumiYahia Kourd2024-06-032024-06-032020-02-23Riadh Toumi.Yahia Kourd.Improved Process Monitoring Based on Sparse Principal Component Analysis (SPCA).International PluridisciplinaryPhD Meeting (IPPM’20). 1st Edition, February23-26, 2020. University Of Eloued. [Visited in ../../….]. Available from [copy the link here].https://dspace.univ-eloued.dz/handle/123456789/33053InterventionThe principal components analysis was (PCA) intensively developed and widely applied in industrial processes for monitoring. The purpose of using the PCA is to reduce the extractable dimension of the still valid feature space to the most information in the primary dataset. Sparse Principal Component Analysis (SPCA) is a relatively new mechanism proposed for the creation of Principal Components (PCs) with sparse loads via a variance tradeoff. Using the SPCA, some of the loads on the PCs can be limited to zero. In this article, we applied this method for the diagnosis and monitoring of a biological process. The results obtained are discussed.enProcess MonitoringPrincipal Component Analysis (PCA)Sparse Principal Component Analysis (SPCA)fault Detection and Isolation.Intervention