RT info:eu-repo/semantics/article
T1 Integrating PCA and structural model decomposition to improve fault monitoring and diagnosis with varying operation points
A1 García Álvarez, Diego
A1 Bregón Bregón, Aníbal
A1 Pulido Junquera, José Belarmino
A1 Alonso González, Carlos Javier
K1 inteligencia artificial
K1 Informática
K1 Multivariate Statistical Process Control
K1 Fault diagnosis
K1 Multiple Operation Points Systems
K1 Principal Component Analysis
K1 Control Estadístico de Procesos Multivariante
K1 Diagnóstico erroneo
K1 Sistemas de Puntos de Operación Múltiple
K1 Análisis de componentes principales
K1 1203.17 Informática
AB Fast and efficient fault monitoring and diagnostics methods are essential for fault diagnosis and prognosis tasks in Health Monitoring Systems. These tasks are even more complicated when facing dynamic systems with multiple operation points. This article introduces a symbiotic solution for fault detection and isolation, based on the integration of two complementary techniques: Possible Conflicts (PCs), a model-based diagnosis technique from the Artificial Intelligence (AI) community, and Principal Component Analysis (PCA), a Multivariate Statistical Process Control (MSPC) technique. Our proposal improves the PCA-based fault detection in systems with multiple operation points and transient states and provides a straightforward fault isolation stage for PCA. At the same time, the proposal increases the robustness for fault detection using PCs through the application of PCA to the residual signals. PCA has the ability to filter out residual deviations caused by model uncertainties that can lead to a high number of false positives. The proposed method has been successfully tested in a real-world plant with accurate fault detection results. The plant has noisy sensors and a system model without the same accuracy at each operation point and transient states.
PB Elsevier
SN 0952-1976
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/59068
UL https://uvadoc.uva.es/handle/10324/59068
LA eng
NO Engineering Applications of Artificial Intelligence, 2023, vol. 122, 106145
NO Producción Científica
DS UVaDOC
RD 19-ene-2025