In this paper, a new process monitoring methodology is presented to detect fault occurrence. The proposed methodology incorporates a wavelet de-noising approach based on the fast wavelet transform (FWT) to extract the embodied fault dynamics from the noisy measured data. A level dependent soft thresholding technique using Daubechies 3 with three levels of decomposition is utilized. An appropria...

Transfer entropy (TE) is a model-free approach based on information theory to capture causality between variables, which has been used for the modeling and monitoring of, and fault diagnosis in, complex industrial processes. It is able to detect the causality between variables without assuming any underlying model, but it is computationally burdensome. To overcome this limitation, a hybrid meth...

This study describes a classification methodology based on support vector machines (SVMs), which offer superior classification performance for fault diagnosis in chemical process engineering. The method incorporates an efficient parameter tuning procedure (based on minimization of radiudmargin bound for SVM's leave-one-out errors) into a multi-class classification strategy using a fuzzy decisio...

Complex multimode processes may have dynamic operation scenario shifts and strong transient behaviors so that the conventional monitoring methods become ill-suited. In this article, a new particle filter based dynamic Gaussian mixture model (DGMM) is developed by adopting particle filter resampling method to update the mixture model parameters in a dynamic fashion. Then the particle filtered Ba...

Fault detection is an important and demanding problem in industry. Recently, many researchers have addressed the use of deep learning architectures for fault applications such as autoencoder. Traditional methods based on autoencoder usually complete by comparing reconstruction errors, ignore a lot useful information about distribution latent variables. To deal with this problem, paper proposes ...

The proliferation of automated data collection schemes and the advances in sensorics are increasing amount we able to monitor real-time. However, given high annotation costs time required by quality inspections, is often available an unlabeled form. This fostering use active learning for development soft sensors predictive models. In production, instead performing random inspections obtain prod...

This paper presents a novel mutual information (MI) matrix based method for fault detection. Given m-dimensional process, the MI is m×m in which (i,j)-th entry measures values between ith dimension and jth variables. We introduce recently proposed matrix-based Rényi’s α-entropy functional to estimate each of matrix. The new estimator avoids density estimation it operates on eigenspectrum (norma...