Authors:
Yahia Kourd
1
;
Messaoud Ramdani
2
;
Riadh Toumi
1
and
Ahmed Samet
1
Affiliations:
1
Laboratory of Electrical Engineering and Renewable Energy, Faculty of Science and Technology, Mohamed-Cherif Messaadia University, Souk Ahras, 41000, Algeria
;
2
Department of Electronics, Faculty of Engineering, Badji-Mokhtar University, Annaba, 23000, Algeria
Keyword(s):
Fault Diagnosis, Process Monitoring, Principal Component Analysis, Sparse PCA and AutoEncoder.
Abstract:
Traditional process monitoring generally assumes that process data follow a Gaussian distribution with linear correlation. Nevertheless, this sort of restriction cannot be satisfied in reality since many industrial processes are nonlinear in nature. This work provides an enhanced multivariate statistical process monitoring technique based on the Stacked Sparse AutoEncoder and K-Nearest Neighbor (SSAE-KNN). This approach consists of developing a model by using Stacked Sparse AutoEncoder (SSAE) to get the residual space, which is the main tool in detecting and reconstructing the potential missing data by residual space. The monitoring statistics in this space are constructed using KNN rules; the threshold values for SSAE-KNN process monitoring are estimated utilizing the Kernel Density PDF Estimation (KDE) method, and an enhanced Sensor Validity Index (SVI) is proposed to detect faulty data based on the reconstruction approach. The experimental results using actual data from a photovol
taic power station connected at the site of OuedKebrit, located in north-eastern Algeria, reveal the effectiveness of the proposed scheme and show its capacity to detect and identify sensor failures.
(More)