Guest Editors
Prof. Shunyi Zhao, Jiangnan University, Wuxi, China
Prof. Xiaoli Luan, Jiangnan University, Wuxi, China
Prof. Jinfeng Liu, University of Alberta, Edmonton, Canada
Dr. Ruomu Tan, ABB Corporate Research Germany, Ladenburg, Germany
Summary
In the past few years, significant progress has been made in modeling and state estimation for industrial processes to improve control performance, reliable monitoring, quick and accurate fault detection, diagnosis, high product quality, fule and resource consumption, etc. However, with the fast development of information technology, numerous essential issues are facing in modeling and state estimation, which generates the new need for novel modeling and or state estimation methodologies and in-depth studies of them.
For example, due to many online sensors equipped, measurements are commonly collected with the characterizes of high volume, velocity, and variety. Therefore, feature selection or dimension reduction that extracts the signal interested and removes redundant information will be more critical during modeling than before, which will, in turn, affect the modeling and estimation algorithmic performance. Fast operation and high computational efficiency of an algorithm matter more since data often arrive rapidly, and the target values are usually required in an online manner. Besides, high accurate estimates of key parameters become more critical since reliable and precise modeling plays a vital role in many tasks, including control, detection, and monitoring. All these challenge the current statistical signal processing techniques from applicability, computational efficiency, and effectiveness. This special issue aims to bring the researchers in this area together with the engineers to break down barriers and develop innovative solutions and practical algorithms.
Potential topics include but are not limited to the following:
● Variational Bayesian Modeling Methods for Industrial Process
● Transfer Modeling for Industrial Process
● Unsupervised Modeling for Industrial Process
● First Principle Modeling for Industrial Process
● Non-parametric Bayesian Modeling for Industrial Process
● Distributed Multi-Agent Modeling Algorithms and Its Industrial Applications
● Robust Modeling Methods for Industrial Process
● Supervised Modeling and Its Industrial Applications
● Filter-Aided Methods for Industrial Processes
● Nonlinear Modeling Methods and Its Industrial Applications
Keywords
• First Principal Modeling
• Unsupervised Modeling
• Non-parametric Bayesian Modeling
• Robust Modeling
• Filter-Aided Methods
• Nonlinear Modeling Methods
• Industrial Process
Published Papers
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Open Access
ARTICLE
Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility
Rebecca Gedda, Larisa Beilina, Ruomu Tan
Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1737-1759, 2023, DOI:10.32604/cmes.2022.019764
（This article belongs to this Special Issue:
Advances on Modeling and State Estimation for Industrial Processes)
Abstract Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner. In the context of process data analytics, change points in the time series of process variables may have an important indication about the process operation. For example, in a batch process, the change points can correspond to the operations and phases defined by the batch recipe. Hence identifying change points can assist labelling the time series data. Various unsupervised algorithms have been developed for change point detection, including the optimisation approach which minimises a cost function with certain…
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Graphic Abstract
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Open Access
ARTICLE
Improved Adaptive Iterated Extended Kalman Filter for GNSS/INS/UWB-Integrated Fixed-Point Positioning
Qingdong Wu, Chenxi Li, Tao Shen, Yuan Xu
Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1761-1772, 2023, DOI:10.32604/cmes.2022.020545
（This article belongs to this Special Issue:
Advances on Modeling and State Estimation for Industrial Processes)
Abstract To provide stable and accurate position information of control points in a complex coastal environment, an adaptive iterated extended Kalman filter (AIEKF) for fixed-point positioning integrating global navigation satellite system, inertial navigation system, and ultra wide band (UWB) is proposed. In this method, the switched global navigation satellite system (GNSS) and UWB measurement are used as the measurement of the proposed filter. For the data fusion filter, the expectation-maximization (EM) based IEKF is used as the forward filter, then, the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing. Tests illustrate that the proposed AIEKF is able to provide an accurate estimation.
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Open Access
ARTICLE
Self-Triggered Consensus Filtering over Asynchronous Communication Sensor Networks
Huiwen Xue, Jiwei Wen, Akshya Kumar Swain, Xiaoli Luan
Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 857-871, 2023, DOI:10.32604/cmes.2022.020127
（This article belongs to this Special Issue:
Advances on Modeling and State Estimation for Industrial Processes)
Abstract In this paper, a self-triggered consensus filtering is developed for a class of discrete-time distributed filtering
systems. Different from existing event-triggered filtering, the self-triggered one does not require to continuously
judge the trigger condition at each sampling instant and can save computational burden while achieving good
state estimation. The triggering policy is presented for pre-computing the next execution time for measurements
according to the filter’s own data and the latest released data of its neighbors at the current time. However, a
challenging problem is that data will be asynchronously transmitted within the filtering network because each
node self-triggers independently. Therefore,…
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Open Access
ARTICLE
State Estimation Moving Window Gradient Iterative Algorithm for Bilinear Systems Using the Continuous Mixed p-norm Technique
Wentao Liu, Junxia Ma, Weili Xiong
Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 873-892, 2023, DOI:10.32604/cmes.2022.020565
（This article belongs to this Special Issue:
Advances on Modeling and State Estimation for Industrial Processes)
Abstract This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space
models in the presence of disturbances. A bilinear state observer is designed for deriving identification algorithms
to estimate the state variables using the input-output data. Based on the bilinear state observer, a novel gradient
iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous
mixed
p-norm cost function. The gain at each iterative step adapts to the data quality so that the algorithm has
good robustness to the noise disturbance. Furthermore, to improve the performance of…
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Open Access
ARTICLE
Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence
Zhiguo Wang, Fangqing Gao, Fei Liu
Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 343-355, 2023, DOI:10.32604/cmes.2022.020569
（This article belongs to this Special Issue:
Advances on Modeling and State Estimation for Industrial Processes)
Abstract In this paper, an improved high-order model-free adaptive iterative control (IHOMFAILC) method for a class of
nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method. This
method adds the differential of tracking error in the criteria function to compensate for the effect of the random
disturbance. Meanwhile, a high-order estimation algorithm is used to estimate the value of pseudo partial derivative
(PPD), that is, the current value of PPD is updated by that of previous iterations. Thus the rapid convergence of the
maximum tracking error is not limited by the initial value of PPD. The convergence…
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Open Access
ARTICLE
Enhancing the Effectiveness of Trimethylchlorosilane Purification Process Monitoring with Variational Autoencoder
Jinfu Wang, Shunyi Zhao, Fei Liu, Zhenyi Ma
Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 531-552, 2022, DOI:10.32604/cmes.2022.019521
（This article belongs to this Special Issue:
Advances on Modeling and State Estimation for Industrial Processes)
Abstract In modern industry, process monitoring plays a significant role in improving the quality of process conduct. With
the higher dimensional of the industrial data, the monitoring methods based on the latent variables have been
widely applied in order to decrease the wasting of the industrial database. Nevertheless, these latent variables do
not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices,
especially the T2 on them. Variational AutoEncoders (VAE), an unsupervised deep learning algorithm using the
hierarchy study method, has the ability to make the latent variables follow the Gaussian distribution. The partial
least squares…
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Open Access
ARTICLE
Skew t Distribution-Based Nonlinear Filter with Asymmetric Measurement Noise Using Variational Bayesian Inference
Chen Xu, Yawen Mao, Hongtian Chen, Hongfeng Tao, Fei Liu
Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 349-364, 2022, DOI:10.32604/cmes.2021.019027
（This article belongs to this Special Issue:
Advances on Modeling and State Estimation for Industrial Processes)
Abstract This paper is focused on the state estimation problem for nonlinear systems with unknown statistics of measurement noise. Based on the cubature Kalman filter, we propose a new nonlinear filtering algorithm that employs a
skew t distribution to characterize the asymmetry of the measurement noise. The system states and the statistics
of skew t noise distribution, including the shape matrix, the scale matrix, and the degree of freedom (DOF) are
estimated jointly by employing variational Bayesian (VB) inference. The proposed method is validated in a target
tracking example. Results of the simulation indicate that the proposed nonlinear filter can perform…
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Open Access
ARTICLE
A Novel Bidirectional Interaction Model and Electric Energy Measuring Scheme of EVs for V2G with Distorted Power Loads
Jiarui Cui, Qing Li, Bin Cao, Xiangquan Li, Qun Yan
Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1789-1806, 2022, DOI:10.32604/cmes.2022.017958
（This article belongs to this Special Issue:
Advances on Modeling and State Estimation for Industrial Processes)
Abstract With the increasing demand for petroleum resources and environmental issues, new energy electric vehicles are increasingly being used. However, the large number of electric vehicles connected to the grid has brought new challenges to the operation of the grid. Firstly, A novel bidirectional interaction model is established based on modulation theory with nonlinear loads. Then, the electric energy measuring scheme of EVs for V2G is derived under the conditions of distorted power loads. The scheme is composed of fundamental electric energy, fundamental-distorted electric energy, distorted-fundamental electric energy and distorted electric energy. And the characteristics of each electric energy are analyzed.…
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Open Access
ARTICLE
Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations
Xiangquan Li, Zhengguang Xu, Cheng Han, Ning Li
Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1807-1825, 2022, DOI:10.32604/cmes.2022.017799
（This article belongs to this Special Issue:
Advances on Modeling and State Estimation for Industrial Processes)
Abstract This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm (M-AM-SGRPIA) for a class of single input single output (SISO) linear output error models with multi-threshold quantized observations. It proves the convergence of the designed algorithm. A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output (SIMO) or SISO nonlinear systems, and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system. The system input design is accomplished using the measurement technology of random repeatability test, and the probabilistic characteristic…
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Open Access
ARTICLE
Range-Only UWB SLAM for Indoor Robot Localization Employing Multi-Interval EFIR Rauch-Tung-Striebel Smoother
Yanli Gao, Wanfeng Ma, Jing Cao, Jianling Qu and Yuan Xu
Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1221-1237, 2022, DOI:10.32604/cmes.2022.017533
（This article belongs to this Special Issue:
Advances on Modeling and State Estimation for Industrial Processes)
Abstract For improving the localization accuracy, a multi-interval extended finite impulse response (EFIR)-based RauchTung-Striebel (R-T-S) smoother is proposed for the range-only ultra wide band (UWB) simultaneous localization
and mapping (SLAM) for robot localization. In this mode, the EFIR R-T-S (ERTS) smoother employs EFIR filter
as the forward filter and the R-T-S smoothing method to smooth the EFIR filter’s output. When the east or the
north position is considered as stance, the ERTS is used to smooth the position directly. Moreover, the estimation
of the UWB Reference Nodes’ (RNs’) position is smoothed by the R-T-S smooth method in parallel. The test
illustrates…
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