Fault Diagnosis with Stacked Sparse AutoEncoder for Multimode Process Monitoring

Yahia Kourd, Messaoud Ramdani, Riadh Toumi, Ahmed Samet

2023

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 photovoltaic 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.

Paper Citation

in Harvard Style

Kourd Y., Ramdani M., Toumi R. and Samet A. (2023). Fault Diagnosis with Stacked Sparse AutoEncoder for Multimode Process Monitoring. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 237-242. DOI: 10.5220/0012194400003543

in Bibtex Style

@conference{icinco23,
author={Yahia Kourd and Messaoud Ramdani and Riadh Toumi and Ahmed Samet},
title={Fault Diagnosis with Stacked Sparse AutoEncoder for Multimode Process Monitoring},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2023},
pages={237-242},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012194400003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Fault Diagnosis with Stacked Sparse AutoEncoder for Multimode Process Monitoring
SN - 978-989-758-670-5
AU - Kourd Y.
AU - Ramdani M.
AU - Toumi R.
AU - Samet A.
PY - 2023
SP - 237
EP - 242
DO - 10.5220/0012194400003543
PB - SciTePress