The random forest (RF) classifier, which is a combination of tree predictors, is one of the
most powerful classification algorithms that has been recently applied for fault detection and …

Random Forest (RF) is one of the mostly used machine learning techniques in fault
detection and diagnosis of industrial systems. However, its implementation suffers from …

This paper proposes a novel structural damage detection method by combining the
advantages of variational mode decomposition algorithm and kernel principal component …

Fault detection and diagnosis (FDD) based on data-driven techniques play a crucial role in
industrial process monitoring. It intends to promptly detect and identify abnormalities and …

Timely detection and diagnosis of process abnormality in industries is crucial for minimizing
downtime and maximizing profit. Among various process monitoring and fault detection …

Abstract Principal Component Analysis (PCA) is a widely used technique for fault detection
and diagnosis. PCA works well when the data set has linear characteristics. However, most …

Most existing industrial process fault detection and diagnosis (FDD) techniques operate on
data collected at a single scale and focus only on known faults. However, actual process …

In chemical plants, operated processes require different conditions to produce various
product grades and meet the time-to-market demand. Conventional multivariate statistical …

Abstract Principal Component Analysis (PCA)-based approach for fault detection is a simple
and accurate data-driven technique for feature extraction and selection. However, PCA …

This paper investigates a use case of robust anomaly detection applied to the scenario of a
photovoltaic production factory—namely, Enel Green Power's 3SUN solar cell production …