Multivariate batch time-series data sets within Semiconductor manufacturing processes present a difficult environment for effective Anomaly Detection (AD). The challenge is amplified by the limited availability of ground truth labelled data. In scenarios where AD possible, black box modelling approaches constrain model interpretability. These challenges obstruct widespread adoption Deep Learnin...

Clustering algorithms and deep learning methods have been widely applied in the multimode process monitoring. However, for data with unknown mode, traditional clustering can hardly identify number of modes automatically. Further, learn effective features from nonlinear data, while extracted cannot follow Gaussian distribution, which may lead to incorrect control limit fault detection. In this p...

The traditional data-driven process monitoring methods may not be applicable for the system which has dynamic and multimode characteristics. In this paper, a novel scheme named modified t-distribution stochastic neighbor embedding using augmented Mahalanobis-distance chemical (AKMD-t-SNE) is proposed to realize multimodal monitoring. First, matrix strategy utilized ensure sample contains autoco...

This work demonstrates for the first time application of network topology variance decompositions in analyzing connectedness chemical plant process variable oscillations arising from disturbances and faults. Specifically, time-based frequency-based variables can be used to compute net pairwise dynamic (NPDC), which originated as a volatility spillover index financial markets studies field econo...

The concept of globally optimal controlled variable selection has recently been proposed to improve self-optimizing control performance of traditional local approaches. However, the associated measurement subset selection problem has not be studied. In this paper, we consider the measurement subset selection problem for globally self-optimizing control (gSOC) of Tennessee Eastman (TE) process. ...

This paper focuses on the Tennessee Eastman (TE) process and for the first time investigates it in a cognitive way. The cognitive fault diagnosis does not assume prior knowledge of the fault numbers and signatures. This approach firstly employs deterministic reservoir models to fit the multiple-input and multiple-output signals in the TE process, which map the signal space to the (reservoir) mo...

When detecting cyberattacks in Industrial settings, it is not sufficient to determine whether the system suffering a cyberattack. It also fundamental explain why under cyberattack and which are assets affected. In this context, Anomaly Detection based on Machine Learning (ML) Deep (DL) techniques showed great performance when industrial scenarios. However, two main limitations hinder using them...

The control loop in the industry is a component that must be maintained because it will determine plant's performance. Most industrial controllers experience oscillations with various causes, such as noise, oscillation, backlash, dead band, hysteresis, random variation, and poor controller tuning. oscillation diagnosis system, which can understand type characteristics, built based on machine le...

The process monitoring method for industrial production can technically achieve early warning of abnormal situations and help operators make timely reliable response decisions. Because practical processes have multimodal operating conditions, the data distributions variables are different. different may cause fault detection model to be invalid. In addition, diagnosis cannot find correct root v...