We consider the stabilization problem for a class of networked control systems in discrete-time domain with random delays. The sensor-to-controller and controller-to-actuator delays are modeled as two Markov chains, resulting closed-loop jump linear modes. necessary sufficient conditions on existence stabilizing controllers established. It is shown that state-feedback gains mode-dependent. An iterative matrix inequality (LMI) approach employed to calculate gains.

Data mining and analytics have played an important role in knowledge discovery decision making/supports the process industry over past several decades. As a computational engine to data analytics, machine learning serves as basic tools for information extraction, pattern recognition predictions. From perspective of learning, this paper provides review on existing applications The state-of-the-art are reviewed through eight unsupervised ten supervised algorithms, well application status...

In modern industrial processes, soft sensors have played an important role for effective process control, optimization, and monitoring. Feature representation is one of the core factors to construct accurate sensors. Recently, deep learning techniques been developed high-level abstract feature extraction in pattern recognition areas, which also great potential sensing applications. Hence, stacked autoencoder (SAE) introduced sensor this paper. As output prediction purpose, traditional...

1Ministry of Education Key Lab For Intelligent Networks and Network Security (MOE KLINNS Lab), Department Automation, School Electronic Information Engineering, Xi’an Jiaotong University, 710049, China 2NIMBUS Centre for Embedded Systems Research, Cork Institute Technology, Rossa Avenue, Cork, Ireland 3Department Shanghai 200240, 4 Electrical Nanyang Technological BLK S2, Singapore 639798 5Department Chemical Materials University Alberta, Edmonton, AB, Canada T6G 2G6

Recently, to ensure the reliability and safety of high-speed trains, detection diagnosis faults (FDD) in traction systems have become an active issue transportation area over past two decades. Among these FDD methods, data-driven designs, that can be directly implemented without a logical or mathematical description systems, received special attention because their overwhelming advantages. Based on existing methods for first objective this paper is systematically review categorize most...

Multivariate statistical process monitoring involves dimension reduction and latent feature extraction in large-scale processes typically incorporates all measured variables. However, involving variables without beneficial information may degrade performance. This study analyzes the effect of variable selection on principal component analysis (PCA) Then, it proposes a fault-relevant Bayesian inference-based distributed method for efficient fault detection isolation. First, optimal subset is...

Process monitoring is crucial for maintaining favorable operating conditions and has received considerable attention in previous decades. Currently, a plant-wide process generally consists of multiple operational units large number measured variables. The correlation among the variables complex results imperative but challenging such processes. With rapid advancement industrial sensing techniques, data with meaningful information are collected. Data-driven multivariate statistical (DMSPPM)...

Chemical processes are in general subject to time variant conditions because of load changes, product grade transitions, or other causes, resulting typical nonstationary dynamic characteristic. It is a considerable challenge for process monitoring consider all possible operation simultaneously including multifarious steady states and switchings. In this work, novel full‐condition strategy proposed based on both cointegration analysis (CA) slow feature (SFA) with the following considerations:...

Deep learning is a recently developed feature representation technique for data with complicated structures, which has great potential soft sensing of industrial processes. However, most deep networks mainly focus on hierarchical the raw observed input data. For sensor applications, it important to reduce irrelevant information and extract quality-relevant features from quality prediction. To deal this problem, novel network proposed in article, based stacked quality-driven autoencoder...

Abstract Multivariate statistical process monitoring (MSPM) methods are significant for improving production efficiency and enhancing safety. However, to the authors’ best knowledge, there is no survey paper providing statistics of published papers over past decade. In this paper, several issues related MSPM reviewed studied. First, annual publication numbers journal articles concerning provided show active development important research field point out promising directions in future....

The increased complexity and intelligence of automation systems require the development intelligent fault diagnosis (IFD) methodologies. By relying on concept a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More specifically, we parameterize through generalized kernel representation system modeling associated diagnosis. An important result obtained is unified form representations, applicable to both unsupervised supervised...

In this article, an indirect adaptive iterative learning control (iAILC) scheme is proposed for both linear and nonlinear systems to enhance the P-type controller by from set points. An mechanism included in iAILC method regulate gain using input–output measurements real time. first designed improve performance fully utilizing model information if such a known exactly. Then, dynamic linearization (IDL)-based nonaffine system, whose completely unknown. The IDL technique employed deal with...

This article is concerned with data-driven realization of fault detection (FD) for nonlinear dynamic systems. In order to identify and parameterize Hammerstein models using input output data, a stacked neural network-aided canonical variate analysis (SNNCVA) method proposed, based on which residual generator formed. Then, the threshold used FD purposes obtained via quantiles-based learning, where both estimation errors approximation are considered. Compared existing work, main novelties this...

Over the last decade, transfer learning has attracted a great deal of attention as new paradigm, based on which fault diagnosis (FD) approaches have been intensively developed to improve safety and reliability modern automation systems. Because inevitable factors such varying work environment, performance degradation components, heterogeneity among similar systems, FD method having long-term applicabilities becomes attractive. Motivated by these facts, an indispensable tool that endows...

With the rise of deep learning, there has been renewed interest within process industries to utilize data on large-scale nonlinear sensing and control problems. We identify key statistical machine learning techniques that have seen practical success in industries. To do so, we start with hybrid modeling provide a methodological framework underlying core application areas: soft sensing, optimization, control. Soft contains wealth industrial applications methods. quantitatively research...

Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of nonlinear processes. However, traditional JITL approaches mainly focus on equal sample sizes between process (input) variables and quality (output) variables, which may not be practical in processes since are usually much harder to obtain than other variables. In order handle unequal length dataset with only few labeled data, novel semisupervised framework proposed sensor modeling processes, based...