A generalized probabilistic monitoring model with both random and sequential data

Yu, Wanke; Wu, Min; Huang, Biao; Lu, Chengda

doi:10.1016/j.automatica.2022.110468

Cited by 35 publications

(4 citation statements)

References 33 publications

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“…Process monitoring has seen significant advancements in recent years driven by improvements in computer processing power and the emergence of artificial intelligence techniques [20,21]. For example, Yu et al introduce a generalized probabilistic monitoring model (GPMM) capable of analyzing random and sequential data for process monitoring, validated using numerical examples and the Tennessee Eastman (TE) process [22]. Similarly, Yu et al (2019) propose the denoising autoencoder and elastic net (DAE-EN) method for robust process monitoring and fault isolation in industrial processes, demonstrating its effectiveness through experimental validation on real industrial processes [23].…”

Section: Introductionmentioning

confidence: 99%

Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach

Kini,

Madakyaru,

Harrou

et al. 2024

ChemEngineering

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Effective fault detection in chemical processes is of utmost importance to ensure operational safety, minimize environmental impact, and optimize production efficiency. To enhance the monitoring of chemical processes under noisy conditions, an innovative statistical approach has been introduced in this study. The proposed approach, called Multiscale Principal Component Analysis (PCA), combines the dimensionality reduction capabilities of PCA with the noise reduction capabilities of wavelet-based filtering. The integrated approach focuses on extracting features from the multiscale representation, balancing the need to retain important process information while minimizing the impact of noise. For fault detection, the Kantorovich distance (KD)-driven monitoring scheme is employed based on features extracted from Multiscale PCA to efficiently detect anomalies in multivariate data. Moreover, a nonparametric decision threshold is employed through kernel density estimation to enhance the flexibility of the proposed approach. The detection performance of the proposed approach is investigated using data collected from distillation columns and continuously stirred tank reactors (CSTRs) under various noisy conditions. Different types of faults, including bias, intermittent, and drift faults, are considered. The results reveal the superior performance of the proposed multiscale PCA-KD based approach compared to conventional PCA and multiscale PCA-based monitoring methods.

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Section: Introductionmentioning

confidence: 99%

Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach

Kini,

Madakyaru,

Harrou

et al. 2024

ChemEngineering

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show abstract

“…In practical industrial applications, it is of great significance to establish accurate mathematical models to improve industrial production efficiency. [1][2][3] Most of the existing control methods and theories are established under the assumption that the parameters of the models are known. 4,5 However, in the actual industrial application, the parameters and structures of the models are unknown.…”

Section: Introductionmentioning

confidence: 99%

Unbiased recursive least squares identification methods for a class of nonlinear systems with irregularly missing data

Liu

2023

Adaptive Control & Signal

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Summary Missing data often occur in industrial processes. In order to solve this problem, an auxiliary model and a particle filter are adopted to estimate the missing outputs, and two unbiased parameter estimation methods are developed for a class of nonlinear systems (e.g., bilinear systems) with irregularly missing data. Firstly, an auxiliary model is constructed to estimate the unknown output, and an auxiliary model‐based multi‐innovation recursive least squares algorithm is presented by expanding the scalar innovation to an innovation vector. Secondly, according to the bias compensation principle, an auxiliary model‐based bias compensation multi‐innovation recursive least squares algorithm is proposed to compensate the bias caused by the colored noise. Thirdly, for further improving the parameter estimation accuracy, the unknown true output is estimated by a particle filter, and a particle filtering‐based bias compensation multi‐innovation recursive least squares algorithm is developed. Finally, a numerical example is selected to validate the effectiveness of the proposed algorithms. The simulation results indicate that the proposed algorithms have good performance in identifying bilinear systems with irregularly missing data.

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“…Therefore, data-driven identification is a highly effective modeling approach for realizing the MPC of nonlinear systems. This method uses input and output data to construct the system model and does not need to analyze the complex interrelationship between the various physical variables of the system [4,5].…”

Section: Introductionmentioning

confidence: 99%

LSTM-CNN Network-Based State-Dependent ARX Modeling and Predictive Control with Application to Water Tank System

Kang

Peng

2023

Actuators

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Industrial process control systems commonly exhibit features of time-varying behavior, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods are used to accurately capture a category of a smooth nonlinear system’s spatiotemporal features. The operating point of these systems may change over time, and their nonlinear characteristics can be locally linearized. We use a fusion of the long short-term memory (LSTM) network and convolutional neural network (CNN) to fit the coefficients of the state-dependent AutoRegressive with the eXogenous variable (ARX) model to establish the LSTM-CNN-ARX model. Compared to other models, the hybrid LSTM-CNN-ARX model is more effective in capturing the nonlinear system’s spatiotemporal characteristics due to its incorporation of the strengths of LSTM for learning temporal characteristics and CNN for capturing spatial characteristics. The model-based predictive control (MPC) strategy, namely LSTM-CNN-ARX-MPC, is developed by utilizing the model’s local linear and global nonlinear features. The control comparison experiments conducted on a water tank system show the effectiveness of the developed models and MPC methods.

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A generalized probabilistic monitoring model with both random and sequential data

Cited by 35 publications

References 33 publications

Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach

Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach

Unbiased recursive least squares identification methods for a class of nonlinear systems with irregularly missing data

LSTM-CNN Network-Based State-Dependent ARX Modeling and Predictive Control with Application to Water Tank System

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