The effective robust management of plant requires the implementation of industrial alarm
systems in a very significant capacity. The core objective of alarms is to warn the operator of …

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 …

With the development of the chemical industry, fault diagnosis of chemical processes has
become a challenging problem because of the high-dimensional data and complex time …

This article proposes a robust end-to-end deep learning-induced fault recognition scheme
by stacking multiple sparse-denoising autoencoders with a Softmax classifier, called stacked …

Dynamic information is a non-negligible part of time-correlated process data, and its full
utilization can improve the performance of fault detection. Traditional dynamic methods …

For modern industrial processes, many sensors equipped operate in harsh environments
and the large number of sensors increases the probability of sensor malfunction. In order to …

In this article, a methodology based on machine learning for fault detection in continuous
processes is presented. It aims to monitor fully distributed scenarios, such as the Tennessee …

Industrial processes are nonlinear and complicated in nature, requiring accurate fault
detection to minimize the deterioration in performance and to respond quickly to …

This paper introduces a new structure kernel principal component analysis (KPCA) that can
successfully model symbolic interval-valued data for fault detection. In the proposed …

Nonlinearity is extremely common in industrial processes. For handling the nonlinearity
problem, this paper combines artificial neural networks (ANN) with principal component …