Hot Strip Mill Process Research Articles

An Adaptive Spatiotemporal Decouple Graph Convolutional Network Based Quality‐Related Fault Detection Method for Complex Industrial Processes

ABSTRACTWith the rapid development of industrial technology, industrial processes become increasingly complex, presenting characteristics of large‐scale and multi‐unit collaboration. However, most current fault detection methods focus on nonlinearity, dynamics, and other characteristics, while neglecting spatiotemporal information. To address this issue, an adaptive spatiotemporal decouple graph convolutional network based quality‐related fault detection method is proposed in this article. First, the temporal graph convolutional network and spatial graph convolutional network are combined organically in the form of joint training. Second, considering that fixed graph structures cannot reflect the dynamic relationships among nodes, we proposed an adaptive weighted mask mechanism to construct dynamic correlation graph embedded with priori knowledge. Multi‐attention mechanism is used to integrate spatiotemporal information, besides, we designed a decoupling layer to avoid information redundancy. Finally, the proposed spatiotemporal graph convolutional network is used to establish a regression model, the quality‐related latent variables are extracted by the decoupling layer, and the statistic is constructed based on Kullback–Leibler divergence. The effectiveness and feasibility of the proposed method are illustrated with the hot strip mill process and the Tennessee Eastman process.

Multi-level monitoring method based on slow independent component analysis-tensor decomposition for industrial batch processes

Industrial batch processes employ similar or identical equipment to manufacture various specification products, hence multi-batch data exhibit common characteristics. Additionally, complex characteristics exist within each batch. In this paper, a multi-level monitoring method based on slow independent component analysis-tensor decomposition (SICA-TD) is proposed considering the characteristics of industrial batch processes. In the first-level monitoring model, SICA is used to identify whether the current batch is faulty. An integrated control limit is constructed to enhance robustness. When a fault is detected, fault and normal data features are combined into a tensor. Subsequently, the second-level monitoring model extracts common features through TD. The disparity in common feature distribution between normal and fault conditions is measured via Bhattacharyya distance (BD) statistics to ascertain whether the fault affects all batches. The method is validated through an actual hot strip mill process (HSMP) and implemented in a process monitoring platform for a steel manufacturing company.

Spatio-temporal feature extraction network based multi-performance indicators synergetic monitoring method for complex industrial processes

In the context of smart manufacturing, modern industrial processes are becoming increasingly complex in terms of process flows, product varieties, and performance indicators (PIs). Performance-driven process monitoring attracts extensive attentions in recent years. However, most methods require spatio-temporal correspondence between process variables and PIs, and rarely consider the correlation among various PIs. In this paper, a spatio-temporal feature extraction network-based multi-performance indicators synergetic monitoring framework is presented. Firstly, considering the missing data in PI measurements, a weighted sum of tensor nuclear norm (WSTNN) based batch-process data completion approach is developed, which can adeptly handle local missing and incomplete data issues and establish the spatio-temporal correspondence for subsequent modeling. Secondly, for a specific PI, a new canonical variate analysis embedded spatio-temporal convolutional network (CVA-STCN) is designed to extract the PI-related features with spatio-temporal dependency. Thirdly, considering the dynamic interaction of multiple PIs, a third-order feature tensor is established to perform the future fusion, and the correlations among various PI-related features are explored via tensor decomposition. Finally, a hierarchical multi-performance indicators synergetic monitoring model is developed over several subspaces. The proposed method is verified on Tennessee Eastman process and an actual hot strip mill process. Overall, the monitoring performance of the proposed method outperforms the traditional ones, in terms of higher fault detection rates and lower false alarm rates. Moreover, the information provided by the multi-subspace synergetic monitoring charts can offer valuable guidance to field engineers.

A cloud–edge collaboration based quality-related hierarchical fault detection framework for large-scale manufacturing processes

Against the backdrop of the new-generation intelligent manufacturing and Industrial Internet of Things, manufacturing processes are evolving towards integration, large-scale operations, and complexity, and the requirements for process safety and product quality reliability are constantly improving. However, these processes are generally distinguished by multi-production sub-processes with cooperative coupling, and multi-hierarchical levels integrated automation systems, where any deviation or abnormality occurring within a sub-process or system level will lead to the extensive propagation and evolution of faults, making the comprehensive quality-related fault detection face increasing challenges in practice. In response, a novel cloud–edge collaboration-based quality-related hierarchical fault detection framework is constructed in this paper, which can facilitate interconnected communication between sub-processes and enhance collaborative interactions at different hierarchical levels to improve fault detection accuracy and reliability. First, two quality supervised training mechanisms based on minimal gated unit are introduced for capturing different scale dynamics features on the cloud side and edge side. These methods enable more adequate extraction of quality-related deep hidden features, particularly in the presence of nonlinearity and dynamics. Subsequently, a federated bi-directional knowledge distillation-based strategy is proposed, leveraging the concept of federated learning. Quality knowledge such as prediction and quality-related features from both the cloud side and edge side are integrated into the training steps of federated learning, thereby strengthening the interaction between multiple sub-processes and distinct levels. Furthermore, comprehensive quality-related fault detection strategies are formulated based on the quality-related features of the cloud side and each sub-process on the edge side, respectively. Finally, the proposed framework is deployed into a cloud–edge-device collaborative prototype system based on a real hot strip mill process to demonstrate its effectiveness and applicability.

A quality‐related distributed process monitoring framework for large‐scale manufacturing processes with multirate sampling measurements

SummaryQuality‐related process monitoring has become a research hot‐spot in the field of industrial control because it is essential to ensure the process safety and product quality. A great number of data driven quality‐related process monitoring methods have been developed for large‐scale manufacturing processes, and most of them are developed based on homogeneous sampling measurement. Therefore, it is necessary to develop quality‐related monitoring methods for large‐scale processes with multirate sampling measurements. In this paper, a new quality‐related distributed monitoring framework for large‐scale manufacturing processes with multirate sampling measurements is proposed. First, a new subsystem decomposition method for multirate sampling processes combining prior knowledge and mutual information is proposed by introducing mathematic expectations. Second, local monitoring model is designed for each subsystem. Multirate partial least squares regression is adopted for modeling among the process and quality variables. The monitoring metrics of the isolation‐based anomaly detection using nearest‐neighbor ensembles are built in prediction space and process space, respectively. Finally, Bayesian inference is introduced to obtain statistical indicators for the plant‐wide processes. The validity of the proposed framework is verified in Tennessee Eastman process and a real hot strip mill process. The results show that the proposed method has favorable effectiveness and significant performance gains compared with state‐of‐the‐art methods.

A spatiotemporal synergetic operating performance assessment based on reconstructed correlation matrix and dissimilarity analytics for plant-wide hot strip mill process

Operating performance assessment (OPA) of process industries plays a significant role in improving the product quality and pursuing the best economic benefits. With the continuous improvement of industrial intelligence, massive data has been collected. Variable time-delay issue is brought by the different spatial and temporal distributions of production units, which affects the actual causal relationship of collected data and assessment accuracy. Therefore, a spatiotemporal synergetic operating performance assessment (SSOPA) method is proposed in this paper. First, a reconstructed correlation matrix is carefully crafted in both time and space, leveraging the intricate multi-correlated spatiotemporal-delay (MCSD) parameters. To precisely quantify the multi-correlated relationships, a mixed gray relation analysis (MGRA) is employed. Subsequently, a predatory search-based genetic algorithm (PSGA) is utilized to meticulously search for the optimal MCSD parameters. Once these parameters are determined, an OPA model is formulated for each subsystem, drawing upon aligned process data and a distributed dissimilarity analysis (DISSIM). Finally, based on the subsystem-level OPA, a comprehensive assessment of the global operating performance level is conducted. The feasibility and effectiveness of the proposed method are verified by the plant-wide hot strip mill process (PHSMP).

An adaptive strategy for time‐varying batch process fault prediction based on stochastic configuration network

AbstractFault prediction ensures safe and stable production, and cuts maintenance costs. Due to the changing operating conditions that lead to the changes in the characteristics of industrial processes, there is a need to monitor the fault state of batch processes in real‐time and to accurately predict fault trends. An adaptive slow feature analysis‐neighborhood preserving embedding‐improved stochastic configuration network (SFA‐NPE‐ISCN) algorithm for batch process fault prediction is proposed. Firstly, SFA is used to extract the time‐varying features of process data and establish the update index of the NPE model. Then, to extract local nearest‐neighbor features and reconstruct them by the NPE model with adaptive update capability, square prediction error (SPE) statistics are constructed as fault state features based on the reconstructed error. Further, the hunter‐prey optimization (HPO) algorithm optimizes the weights and biases in the stochastic configuration network, and the singular value decomposition (SVD) and QR decomposition of column rotation are introduced to solve the ill‐posed problem of SCN and obtain the prediction model of ISCN. Finally, the obtained statistics SPE is formed into a time series, and the ISCN model is used to predict the process state trend. The effectiveness of the proposed algorithm is verified by case studies of industrial‐scale penicillin fermentation processes and the Hot strip mill process.

A novel process monitoring framework combined temporal feedback autoencoder and multilevel correlation analysis for large-scale industrial processes

Modeling and monitoring generally face numerous challenges such as complex characteristics of multi-unit, temporal correlations and strong interaction among subblocks in large-scale industrial processes. To handle those challenges, a novel process monitoring framework combined temporal feedback autoencoder and multilevel correlation analysis is proposed in this paper. Firstly, large-scale industrial processes are decomposed into multiple subblocks in spatial and temporal order based on process knowledge. Secondly, an improved autoencoder with temporal feedback mechanism is constructed as local monitoring model to capture the important latent variables of each subblock. Then, considering the sequential transmission and correlations among subblocks in series, a multilevel correlation analysis method is employed to efficiently extract the unique features of each subblock and the joint features of the whole process. Finally, the irregular contribution indices of the unique features and the joint features are designed for hierarchical process monitoring. The superiority of the proposed framework can be verified by Tennessee Eastman process and a real hot strip mill process.

A Shape-Based Monitoring Method for Variable Trajectories of Finishing Mill Processes

As the most critical procedure in the hot strip mill process (HSMP), the performance of the finishing mill process (FMP) has a significant influence on the key performance indicators (KPIs). Considering that the multivariate statistical process monitoring (MSPM) methods and batch alignment methods (which are implemented in a point-to-point manner) cannot effectively handle the uneven-length and multiphase characteristics of FMP data, we propose a method to monitor the shape of the variable trajectories that are closely associated with KPIs. First, the historical FMP data characterized by different grades and thickness is utilized to build monitoring models based on the Shape-Based Distance (SBD) to obtain two statistics, i.e. the SBDs and the corresponding offsets when aligned with the central trajectories. Then, by comparing the statistics of the new coming trajectories with the control limits obtained from the training set, the new coming batches can be monitored. In the end, the proposed method is tested on the real FMP dataset to validate its effectiveness.

Nonlinear Dynamic Granger Causality Analysis Framework for Root-Cause Diagnosis of Quality-Related Faults in Manufacturing Processes

Root-cause diagnosis of quality-related faults plays a crucial role in ensuring stable product quality and high-efficient production for modern manufacturing processes. However, there exists complex nonlinear and dynamic sequential characteristics for process and quality data before and after faults, which contain important fault information. How to fully explore and use this information to locate the root-cause and identify the propagation path is a hot topic. Thus, a new nonlinear dynamic Granger causality analysis method is developed for root-cause diagnosis, which will provide timely and useful reference information to take reasonable measures for field engineers. First, an optimal variable division based on minimal redundancy maximal relevance algorithm is presented to get the quality-related variables. Then, a new attention-based random disturbance gated recurrent unit is designed for nonlinear dynamic Granger causality analysis, aiming at locating the root-cause and identifying the propagation path of quality-related faults. A typical manufacturing process, the hot strip mill process is used for verification. The results show the practicability of the proposed method, and its strong robustness to noise. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Reasonable and feasible root-cause diagnosis technology will provide timely reference information for field operators to take maintenance measures. However, the nonlinear and dynamic characteristics of time-series data before and after quality-related faults have brought great challenges to the traditional root-cause diagnosis techniques. To address this issue, a practical nonlinear dynamic Granger causality analysis method is proposed, which is purely data driven without knowing complex mechanism knowledge. It will provide feasible solutions for safety monitoring of hot strip mill process as the representative of manufacturing processes.

Spatio-temporal information analytics based performance-driven industrial process monitoring framework with cloud–edge-device collaboration

Smart manufacturing in the context of Industry 4.0 presents new characteristics, such as large-scale, complex sub-processes cooperation, and integrated information management, which makes comprehensive performance-driven process monitoring difficult to be achieved in practice. Cloud–edge-device collaboration brings new perspectives for modern industries. This paper presents a performance-driven process monitoring framework in cloud–edge-device collaborative architecture, including a spatio-temporal information analytics based method and its application to a hot strip mill process (HSMP). Firstly, an integrated performance-driven process monitoring framework is proposed, where different monitoring tasks are deployed in the device, edge, and cloud layers, respectively. Secondly, a new performance-related spatio-temporal information analytics based fault detection and localization method is developed. In detail, temporal kernel canonical correlation analysis (CCA) is employed to extract the canonical correlation features with nonlinearities in the temporal domains, and spatial CCA is designed to form the spatial features. Based on the spatio-temporal features, the equipment operation condition monitoring model is developed in the device layer for real-time fault localization, and the process monitoring model is deployed in the edge for fault detection. Furthermore, to give a high-level evaluation of the operating status of the process, a qualitative trend analysis based operation risks evaluation model is deployed in the cloud. Different from the traditional architectures, the data processing capability of each layer is fully utilized in the proposed one, and the function is relevant to the enterprise management system. It provides a topological basis with clear priorities for subsequent process regulation and optimization. Finally, an actual HSMP is introduced to testify the performance of the framework. From the results, the fault detection rates are improved in comparison with traditional methods, and the fault localization results are consistent with the actual situations.

Data-Driven Soft Sensor Based on Sparse Relational Graph Attention Network for Hot Strip Mill Process

Due to technological or financial constraints, it is always challenging to measure the mechanical properties (MPs) of the hot strip mill process (HSMP) online. A feasible solution that offers a consistent and trustworthy online estimation of MPs based on available process variables is to develop an Effective soft sensor. To achieve this, we propose a novel method for MP modeling based on a sparse relational graph attention network (SRGAT). In SRGAT, a sparse relationship learning (SRL) module is first utilized to learn a graph structure that can describe sparse interactive correlations among process variables. Then, both the learned graph structure and process variables are fed into the graph attention network (GAT) to highlight useful information for MP prediction of the steel strip. The proposed SRGAT soft sensor surpasses state-of-the-art approaches in terms of root mean square error (RMSE), mean absolute error (MAE), and r-square (R2), according to experimental results on the HSMP data collected from a real Iron & Steel Co., Ltd, China.

A novel exergy-related fault detection and diagnosis framework with transformer-based conditional generative adversarial networks for hot strip mill process

Fault detection and diagnosis (FDD) plays a crucial role in the iron and steel industry. However, the iron and steel industry have unique complex characteristics such as high energy consumption, temporal and spatial correlation, and data imbalance, which makes the application of traditional FDD methods ineffective. With the development of FDD, deep learning-based methods have attracted more and more attention. To address the above issues, this work combines the advantages of transformer and conditional generative adversarial networks (CGAN) to propose a novel exergy-related FDD framework with transformer-based CGAN (TransCGAN). Firstly, we extract exergy-related variables and convert one-dimensional data into image data to preserve spatiotemporal information. Next, the transformer encoder is used as the basic block of CGAN to capture the relationship between normal and fault data, and generate fault samples. Then, the generated data and original data are mixed to train the proposed FDD model. Finally, the feasibility of TransCGAN is verified by the hot strip mill process (HSMP). Compared with other state-of-the-art (SOTA) methods, the proposed method has higher fault detection rate and better fault diagnostic performance.

Kernel principal component analysis fault diagnosis method based on improving Golden Jackal optimization algorithm

Aiming at the shortcomings of the Golden Jackal optimization algorithm, such as low convergence accuracy and easy falling into the optimal local solution, an improved Golden Jackal optimization algorithm was proposed. First, sine and piecewise linear (SPM) chaotic mapping was introduced to increase the population number to achieve the purpose of initial population diversity. The self-adaptive weight and sine–cosine algorithm improved the position update formula of the Golden Jackal optimization algorithm, so the global search ability of the golden jackal algorithm is improved, and avoid the algorithm that fell into local optimality. Second, simulation experiments with eight standard test functions are performed to prove that the algorithm has excellent optimization ability. The improved Golden Jackal optimization algorithm was applied to optimize the kernel parameters of hybrid kernel principal component analysis. A fault diagnosis model is proposed to improve the golden jackal algorithm to optimize the kernel principal component analysis. Finally, the proposed method is used to fault diagnosis in the hot strip mill process. According to the study of simulation results, the faulty data can be identified effectively by this method, the accuracy is up to 100%, and the fault false alarm rate is greatly reduced.

A Novel Distributed CVRAE-Based Spatio-Temporal Process Monitoring Method With Its Application

Due to the interconnected characteristics between subsystems and the strong correlation within subsystems, the monitoring of plant-wide processes has become a challenging problem, especially for tandem plant-wide processes that exist in various industrial fields, such as petrochemicals, metallurgy and sewage treatment. In this paper, a novel spatio-temporal monitoring method is proposed for the hot strip mill process, a typical tandem industrial process. Firstly, the plant-wide process is divided into different sub-blocks based on the tandem structure. Then, a distributed conditional variational recurrent autoencoder (CVRAE)-based process monitoring method is proposed to build the local latent variable model of each subsystem using relevant dynamic features extracted from the previous subsystem. The latent distributions and reconstructed errors are used to design local monitoring statistics for local process monitoring. A global monitoring statistic is established by deep support vector data description (SVDD) to monitor the whole process. Finally, the effectiveness and superiority of the proposed method are demonstrated by a hot strip mill (HSM) process case, which shows better monitoring performance compared to the existing methods.

Exergy-related process monitoring for hot strip mill process based on improved support tensor data description

Process monitoring is important for ensuring industrial production safety. If faults are detected in time, maintenance plan will be made to avoid economic losses. Traditional process monitoring methods pay more attention to the utilization of process data and ignore process mechanism. It is necessary to consider the energy flow and the spatial information of different production equipments. As the unity of quality and quantity of energy, exergy contains the performance change information of the process and can be used as another way of achieving the required dimensionality reduction. Moreover, the introduction of spatial information will lead to the increase of data dimension. Support vector data description (SVDD) are oriented to vector data and cannot deal with tensor data directly. To handle above issues, a novel exergy-related process monitoring method based on improved support tensor data description (ISTDD) is proposed in this paper. First, exergy efficiency are calculated and exergy-related process variables are obtained by the minimal redundancy maximal relevance (mRMR). Second, a third-order tensor is constructed with spatial information. Then, the exergy-related monitoring model and its robust version are developed. Finally, case study on a hot strip mill process (HSMP) is given to illustrate the effectiveness of proposed method.

Data-driven distributed collaborative fault detection and isolation for large-scale dynamic processes in simultaneous-fault cases

This paper proposes a data-driven distributed collaborative fault detection and isolation scheme for large-scale dynamic processes, interconnected by heterogeneous subsystems with both actuator and sensor faults. The residual generator in each subsystem is directly constructed with both local and neighboring process data based on subspace identification. Utilizing the rank deficiency property, the healthy mode residual covariance matrix is estimated with noisy data, and thus the test statistic and detection threshold are obtained. Then, a collaborative simultaneous fault isolation method is developed, where the local residual is decoupled with neighboring actuator fault’s effects and a series of robust residual generators are constructed by solving corresponding optimization problems. In each subsystem, both local fault isolation and neighboring sensor fault isolation are realized. Thus, sensor fault detection and isolation for the whole process can be achieved with a reduced number of monitoring subsystems. Finally, case studies on the Tennessee Eastman (TE) process and a real hot strip mill process (HSMP) verify the feasibility and good performance of the proposed scheme.

A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill

Width-related defects are a common occurrence in the Hot Strip Mill process which can lead to extra processing, concessions, or scrapping. The detection and Root Cause Analysis of these defects is a largely manual process and is vulnerable to several negative factors including human error, late feedback, and knock-on effects in successive steel strip products. Automated tools which utilize Artificial Intelligence and Machine Learning for defect detection and Root Cause Analysis in hot rolling have not yet been adopted outside of surface defect detection and roller force optimization. In this paper, we propose an automated tool for the detection and Root Cause Analysis of width-related defects in the hot rolling process which utilizes a combination of expert knowledge and several Machine Learning models. Through this, we aim to increase the scope, and encourage further development, of Machine Learning applications within the Hot Strip Mill process. Both classical algorithms and Computer Vision methods were used for the Machine Learning component of the tool, namely, classification trees and pre-trained convolutional neural networks. The tool is trained and validated using data from an existing hot rolling mill and thus the challenges of collecting and processing real-world legacy data are highlighted and discussed. The Machine Learning models used are shown to perform optimally by validation performance metrics. The tool is found to be suitable for the specified purpose and would be further improved with more training data.

Hierarchical Causal Graph-Based Fault Root Cause Diagnosis and Propagation Path Identification for Complex Industrial Process Monitoring

Modern complex industrial processes are increasingly integrated and automated. There are numerous control loops in the process system and the closed-loop feedback makes units strongly coupled. This makes the faults propagate and evolve with the material and energy flow networks. Thus, it poses a great challenge to the fault root cause diagnosis and propagation path identification for industrial processes. This paper proposes a fault root cause diagnosis and propagation path identification strategy for complex industrial process based on hierarchical causal graph (HCG). Firstly, the process is divided into several sub-blocks according to process knowledge. The monitoring model is built for fault detection. Secondly, the fault-related sub-blocks and variables are selected based on contribution plot, and the causal analysis based on transfer entropy is performed on them to construct HCG. In this way, the causal connections can be significantly reduced. Subsequently, a propagation path identification method based on causal feature long short-term memory (CF-LSTM) is designed. Finally, real data from hot strip mill process (HSMP) and their comparative experiments are adopted to validate the fault diagnosis performance of the proposed framework.

A comprehensive operating performance assessment framework based on distributed Siamese gated recurrent unit for hot strip mill process

For modern industrial processes, the operating performance assessment has a significant role in ensuring high quality and efficiency in production under normal condition. However, with the switching of working conditions, aging of the equipments, and uncertain disturbance, the inherent nonlinear and dynamic characteristics make it difficult to make an accurate assessment. Meanwhile, industrial processes usually include multiple subsystems, and traditional distributed assessment methods seldom consider practical communications between different subsystems. In addition, normal operating performance assessment and fault level assessment are rarely investigated simultaneously in the existing methods. In this paper, a comprehensive operating performance assessment framework is proposed to deal with the above issues. First, a Siamese gated recurrent unit network is developed by Siamese neural network and gated recurrent unit. Then, based on the process knowledge, a distributed Siamese gated recurrent unit (DSGRU) framework with partial communication is proposed. After that, the assessment strategies are designed for normal and abnormal conditions, respectively. Finally, the effectiveness of the proposed framework is demonstrated on a real hot strip mill process (HSMP).