Monitoring Of Large-scale Processes Research Articles

On the Equivalence of LEO-SAR Constellations and Complex High-Orbit SAR Systems for the Monitoring of Large-Scale Processes

On the Equivalence of LEO-SAR Constellations and Complex High-Orbit SAR Systems for the Monitoring of Large-Scale Processes

Data-knowledge-driven distributed monitoring for large-scale processes based on digraph

Large-scale processes play a pivotal role in modern industry. For process safety concerns, this work proposes a novel digraph-based data-knowledge-driven method (DG-DKD) for large-scale process monitoring. Different from traditional purely data-driven methods, DG-DKD combines process data and knowledge to improve the capability of fault detection and diagnosis. Firstly, the large-scale process is converted into a digraph based on process knowledge and then decomposed into multiple physical meaningful subblocks through a digraph partition method, which eliminates the spurious correlation and improves interpretability. On this basis, the spatial information between subblocks is characterized by a one-hop digraph diffusion method and the temporal information within subblock is captured by canonical variate analysis. With concurrent analysis of temporal and spatial information, the accurate detection of process faults can be guaranteed. Subsequently, a two-stage distributed fault diagnosis method is developed where process data and digraph are leveraged for contribution analysis and causality analysis respectively, which can reduce the smearing effect and identify the fault root cause and propagation path. Finally, the effectiveness of the proposed method is illustrated through the Tennessee Eastman benchmark process and a reactor separator process.

Integrate weighted dependence and skewness based multiblock principal component analysis with Bayesian inference for large-scale process monitoring

In a large-scale process, variables are correlated, and are Gaussian and non-Gaussian coexisted. The decentralized monitoring methods divide variables into blocks and carry out local monitoring is aimed at reducing the process complexity. Recently, the weighted copula-correlation-based multiblock principal component analysis (WCMBPCA) considering the correlation degree and correlation patterns was proposed to monitor a correlated process. While its monitoring efficiency has been shown, the WCMBPCA method has some drawbacks: 1) monitoring performance relies on the selection of copula function; 2) computation time for the weight matrix is long; 3) without considering the distribution information. This study proposes a novel weight strategy for multiblock PCA by developing a measurement, which simultaneously estimates the dependence and skewness of data, such that the distribution information is additionally considered. The proposed weight matrix is based on the use of non-parametric ranks, prompting the short computation time. The experiment results from Tennessee Eastman (TE) process monitoring shows the average fault detection rate for the proposed method is 84.19% that outperforms regular PCA, dynamic PCA, multiblock PCA, and WCMBPCA. Moreover, the weight matrix calculation takes 0.000635 s for the proposed method, while 195.040282 s for WCMBPCA.

Decentralized dynamic process monitoring based on manifold regularized slow feature analysis

For large-scale process monitoring, traditional decentralized monitoring methods fail to discriminate real faults from normal operation deviations. This paper proposes a novel decentralized method for monitoring large-scale industrial processes by exploring serial correlations and local manifold structures of the data. A block division strategy based on maximal information coefficient-spectral clustering is proposed, which can divide the measured variables into several blocks without any prior knowledge. To extract inter-block relevance, multiblock principal component analysis is introduced to whiten the original variables. On this basis, we develop a new dimensionality reduction algorithm named manifold regularized slow feature analysis (MRSFA) to capture the temporal dynamics and local structure information in each block. Monitoring statistics are constructed based on the captured feature information to concurrently monitor the operation deviations and anomalous dynamics. To achieve decision fusion, the monitoring results derived from all blocks are combined through Bayesian inference. Two case studies on the Tennessee Eastman process and a real industrial process are carried out and the experimental results demonstrate the effectiveness of the proposed method.

A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring.

Process monitoring plays an important role in ensuring the safety and stable operation of equipment in a large-scale process. This paper proposes a novel data-driven process monitoring framework, termed the ensemble adaptive sparse Bayesian transfer learning machine (EAdspB-TLM), for nonlinear fault diagnosis. The proposed framework has the following advantages: Firstly, the probabilistic relevance vector machine (PrRVM) under Bayesian framework is re-derived so that it can be used to forecast the plant operating conditions. Secondly, we extend the PrRVM method and assimilate transfer learning into the sparse Bayesian learning framework to provide it with the transferring ability. Thirdly, the source domain (SD) data are re-enabled to alleviate the issue of insufficient training data. Finally, the proposed EAdspB-TLM framework was effectively applied to monitor a real wastewater treatment process (WWTP) and a Tennessee Eastman chemical process (TECP). The results further demonstrate that the proposed method is feasible.

Hierarchical Bayesian Network Modeling Framework for Large-Scale Process Monitoring and Decision Making

In this brief, a hierarchical Bayesian network modeling framework is formulated for large-scale process monitoring and decision making, which includes a basic layer and a functional layer. First, the whole process is decomposed into different units, where local Bayesian networks are constructed, providing monitoring information and decision-making capability for the upper layer. The network structure is determined automatically based on the process data in each local unit of the basic layer. Then, through incorporating the topological structure of the process, a functional Bayesian network is further constructed to infer the information from the basic layer, which can be customized according to user demands, such as fault detection, fault diagnosis, and classification of operating status. The performance of the proposed method is evaluated through a benchmark process.

A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete information

Large-scale process monitoring has become a challenging issue due to the integration of sub-systems or subprocesses, leading to numerous variables with complex relationship and potential missing information in modern industrial processes. To avoid this, a distributed expectation maximization-principal component analysis scheme is proposed in this paper, where the process variables are first divided into several sub-blocks using two-layer process decomposition method, based on knowledge and generalized Dice’s coefficient. Then, the missing information of variables is estimated by expectation maximization algorithm in the principal component analysis framework, then the expectation maximization-principal component analysis method is applied for fault detection to each sub-block. Finally, the process monitoring and fault detection results are fused by Bayesian inference technique. Case studies on the Tennessee Eastman process is applied to show the effectiveness and performance of our proposed approach.

Distributed Dynamic Process Monitoring Based on Minimal Redundancy Maximal Relevance Variable Selection and Bayesian Inference

The conventional distributed modeling strategy generally includes all the process variables in large-scale process monitoring, thus submerging the local fault information. Meanwhile, fault diagnosis issues in the aforementioned process are also worth studying. To make up the deficiencies of the general distributed method, this brief presents an advantageously distributed fault monitoring and diagnosis scheme for large-scale dynamic processes. First, the optimal variable subblock is established by the minimal redundancy maximal relevance (mRMR) algorithm for each measured variable individually. Second, the corresponding monitoring model is set up in every subblock, respectively. Finally, all the distributed multiblocks’ results are combined to form an integrated decision through the Bayesian inference. The proposed scheme considers not only the interpretation of the correlation of variables but also the redundancy between them in the block division, which better characterizes the dynamic relationships among variables, thereby facilitating the monitoring performance significantly. After that, a new contribution plot method based on subblock analysis is applied for fault isolation. Both a real-world industrial process and the Tennessee Eastman benchmark process validate that the novel monitoring strategy is more efficient than the existing state-of-the-art approaches.

Local and Global Randomized Principal Component Analysis for Nonlinear Process Monitoring

Kernel principal component analysis (KPCA) has been widely used in nonlinear process monitoring since it can capture the nonlinear process characteristics. However, it suffers from high computational complexity and poor scalability while dealing with real-time process monitoring and large-scale process monitoring. In this paper, a novel dimension reduction technique, local and global randomized principal component analysis (LGRPCA), is proposed for nonlinear process monitoring. The proposed LGRPCA method first maps the input space onto a feature space to reveal nonlinear patterns through random Fourier features. With the aid of random Fourier features, the proposed LGRPCA method is scalable and with much lower computational and storage costs. To exploit the underlying local and global structure information in the feature space, local structure analysis is integrated into the framework of global variance information extraction. The resulting LGRPCA can provide an improved representation of input data than the traditional KPCA. Thus, the proposed LGRPCA method is quite suitable for real-time process monitoring and large-scale process monitoring. $T^{2}$ and squared prediction error (SPE) statistic control charts are built for fault detection using the proposed LGRPCA method. Furthermore, contribution plots to LGRPCA-based $T^{2}$ and SPE ( $Q$ ) statistics are established to identify the root cause variables through a sensitivity analysis principle. The superior performance of the proposed LGRPCA-based nonlinear process monitoring method is demonstrated through a numerical example and the comparative study of the Tennessee Eastman benchmark process.

Signature-Based Detection of Notable Transitions in Numeric Data Streams

A major challenge in large-scale process monitoring is to recognize significant transitions in the process conditions and to distinguish them from random fluctuations that do not produce a notable change in the process dynamics. Such transitions should be recognized at the early stages of their development using a minimal snapshot of the observable process log. We developed a novel approach to detect notable transitions based on analysis of coherent behavior of frequency components in the process log (coherency portraits). We have found that notable transitions in the process dynamics are characterized by unique coherency portraits, which are also invariant with respect to random process fluctuations. Our experimental study demonstrates significant efficiency of our approach as compared to traditional change detection techniques.

Efficient Recursive Principal Component Analysis Algorithms for Process Monitoring

Principal component analysis (PCA) has been successfully applied in large scale process monitoring. However, classical PCA has some drawbacks: one of these aspects is the inability to deal with parameter-varying processes, where it interprets the natural changes in the process as faults, resulting in numerous false alarms. These false alarms threaten the credibility of the monitoring system. Therefore, recursive PCA (RPCA) algorithms are recommended. The most important challenge faced by these algorithms is the high computation costs, due to repeated eigenvalue decomposition (EVD) or singular value decomposition (SVD). Motivated by this issue, we present two RPCA algorithms that will greatly reduce the computation cost. The first algorithm is based on first-order perturbation analysis (FOP), which is a rank-one update of the eigenvalues and their corresponding eigenvectors of a sample covariance matrix. The second one is based on the data projection method (DPM), which is a simple and reliable approach for adaptive subspace tracking. The effectiveness of the presented RPCA algorithms is evaluated with an application of monitoring a nonisothermal continuous stirred tank reactor (CSTR) system. The results show the efficiency of these approaches compared to the classical PCA.

Improved principal component monitoring of large-scale processes

In this work, the integration of ARMA filters into the multivariate statistical process control (MSPC) framework is presented to improve the monitoring of large-scale industrial processes. As demonstrated in the paper, such filters can remove auto-correlation from the monitored variables to avoid the production of false alarms. This is exemplified by application studies to a synthetic example from the literature and to the Tennessee Eastman benchmark process.