Process fault detection and diagnosis (FDD) is an essential tool to ensure safe production in
chemical industries. After decades of development, despite the promising performance of …

Abstract Model-based methods are widely used for the fault diagnosis of stochastic dynamic
systems by simply using the input–output relationship of the system. Despite encouraging …

This article is concerned with data-driven realization of fault detection (FD) for nonlinear
dynamic systems. In order to identify and parameterize nonlinear Hammerstein models …

Industrial cyber-physical systems (ICPSs) play an important role in many critical
infrastructures. To ensure the secure and reliable operation of ICPSs, this work presents a …

Sensors have been used in various agricultural production scenarios due to significant
advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture …

Process monitoring is a critical activity in manufacturing industries. A wide variety of data-
driven approaches have been developed and employed for fault detection and fault …

Big Data will revolutionize modern industry by improving process optimization, facilitating
insight discovery, and improving decision-making. This big data revolution presents a …

This work presents a novel Automated Machine Learning (AutoML) approach for Real-Time
Fault Detection and Diagnosis (RT-FDD). The approach's particular characteristics are: it …

This paper proposes a data‐driven sensor fault detection and isolation approach for the
general class of nonlinear systems. The proposed method uses deep neural network …

The fast-growing literature corpus of industry 4.0 in the last years also reflects the various
applications of Artificial Intelligence (AI) and Machine Learning (ML) in the manufacturing …