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

Quality control process has become one of the most critical issues in intelligent
manufacturing. As the most practical and prevalent tools for continuously monitoring, control …

This paper proposes an improved Reduced Kernel Principal Component Analysis (RKPCA)
for handling nonlinear dynamic systems. The proposed method is entitled Moving Window …

This paper proposes a new method for fault detection using a reduced kernel principal
component analysis (RKPCA). The proposed RKPCA method consists on approximating the …

Unnatural control chart patterns (CCPs) can be associated with the quality problems of the
production process. It is quite critical to detect and identify these patterns effectively based …

It is vital for fault detection technology to extract features of industrial process data effectively.
Local kernel principal component analysis (LKPCA) has proved its good performance in …

This paper presents a new data-driven method for diagnosing multiplicative key
performance degradation in automation processes. Different from the well-established …

Abstract Kernel Principal Component Analysis (KPCA) is a nonlinear extension of Principal
Component Analysis (PCA). Recently, it is the most popular technique for monitoring …

This paper proposes a new reduced kernel method for monitoring nonlinear dynamic
systems on reproducing kernel Hilbert space (RKHS). Here, the proposed method is a …

A model combining kernel principal component analysis (KPCA) and Xtreme Gradient
Boosting (XGBoost) was introduced for forecasting the final oxygen content of electroslag …