Deep learning-based fault diagnosis of photovoltaic systems: A comprehensive review and enhancement prospects
Photovoltaic (PV) systems are subject to failures during their operation due to the aging
effects and external/environmental conditions. These faults may affect the different system …
effects and external/environmental conditions. These faults may affect the different system …
Chronic leak detection for single and multiphase flow: A critical review on onshore and offshore subsea and arctic conditions
Leak detection in pipelines has been a prevalent issue for several decades. Pipeline leaks
from sources such as small cracks and pinholes are termed chronic leaks, as they have the …
from sources such as small cracks and pinholes are termed chronic leaks, as they have the …
A novel multivariate statistical process monitoring algorithm: Orthonormal subspace analysis
Z Lou, Y Wang, Y Si, S Lu - Automatica, 2022 - Elsevier
Partial least squares (PLS) and canonical correlation analysis (CCA) are two most popular
key performance indicators (KPI) monitoring algorithms, which have shortcomings in dealing …
key performance indicators (KPI) monitoring algorithms, which have shortcomings in dealing …
Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems
In this paper, we consider a machine learning approach merged with statistical testing
hypothesis for enhanced fault detection performance in photovoltaic (PV) systems. The …
hypothesis for enhanced fault detection performance in photovoltaic (PV) systems. The …
Time series multiple channel convolutional neural network with attention-based long short-term memory for predicting bearing remaining useful life
JR Jiang, JE Lee, YM Zeng - Sensors, 2019 - mdpi.com
This paper proposes two deep learning methods for remaining useful life (RUL) prediction of
bearings. The methods have the advantageous end-to-end property that they take raw data …
bearings. The methods have the advantageous end-to-end property that they take raw data …
Online reduced kernel principal component analysis for process monitoring
Kernel principal component analysis (KPCA), which is a nonlinear extension of principal
component analysis (PCA), has gained significant attention as a monitoring method for …
component analysis (PCA), has gained significant attention as a monitoring method for …
Distributed process monitoring based on canonical correlation analysis with partly-connected topology
In this work, a novel data-driven residual generation based process monitoring method is
proposed for plant-wide process systems which can be partitioned into several sub …
proposed for plant-wide process systems which can be partitioned into several sub …
Online reduced kernel PLS combined with GLRT for fault detection in chemical systems
In this paper, an improved fault detection method is proposed based on kernel partial least
squares (KPLS) model and generalized likelihood ratio test (GLRT) detection chart in order …
squares (KPLS) model and generalized likelihood ratio test (GLRT) detection chart in order …
Investigating machine learning and control theory approaches for process fault detection: a comparative study of KPCA and the observer-based method
The paper focuses on the importance of prompt and efficient process fault detection in
contemporary manufacturing industries, where product quality and safety protocols are …
contemporary manufacturing industries, where product quality and safety protocols are …
Fault detection of petrochemical process based on space-time compressed matrix and Naive Bayes
Due to the high available and reliable requirements of petrochemical processes, it is critical
to develop real-time fault detection approaches with high performance. Some machine …
to develop real-time fault detection approaches with high performance. Some machine …