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23 pages, 9395 KiB

Open AccessArticle

MAS-LSTM: A Multi-Agent LSTM-Based Approach for Scalable Anomaly Detection in IIoT Networks

by Zhenkai Qin, Qining Luo, Xunyi Nong, Xiaolong Chen, Hongfeng Zhang and Cora Un In Wong

Processes 2025, 13(3), 753; https://doi.org/10.3390/pr13030753 - 5 Mar 2025

Viewed by 824

The increasing complexity of interconnected systems in the Internet of Things (IoT) demands advanced methodologies for real-time security and management. This study presents MAS-LSTM, an anomaly-detection framework that combines multi-agent systems (MASs) with long short-term memory (LSTM) networks. By training agents on IoT [...] Read more.

The increasing complexity of interconnected systems in the Internet of Things (IoT) demands advanced methodologies for real-time security and management. This study presents MAS-LSTM, an anomaly-detection framework that combines multi-agent systems (MASs) with long short-term memory (LSTM) networks. By training agents on IoT traffic datasets (NF-ToN-IoT, NF-BoT-IoT, and their V2 versions), MAS-LSTM offers scalable, decentralized anomaly detection. The LSTM networks capture temporal dependencies, enhancing anomaly detection in time-series data. This framework overcomes key limitations of existing methods, such as scalability in heterogeneous traffic and computational efficiency in resource-constrained IIoT environments. Additionally, it leverages graph signal processing for adaptive and modular detection across diverse IoT scenarios. Experimental results demonstrate its effectiveness, achieving F1 scores of 0.9861 and 0.8413 on NF-BoT-IoT and NF-ToN-IoT, respectively. For V2 versions, MAS-LSTM achieves F1 scores of 0.9965 and 0.9678. These results highlight its robustness in handling large-scale IIoT traffic. Despite challenges in real-world deployment, such as adversarial attacks and communication overhead, future research could focus on self-supervised learning and lightweight architectures for resource-constrained environments. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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9 pages, 4313 KiB

Open AccessArticle

Power Load Forecasting System of Iron and Steel Enterprises Based on Deep Kernel–Multiple Kernel Joint Learning

by Yan Zhang, Junsheng Wang, Jie Sun, Ruiqi Sun and Dawei Qin

Processes 2025, 13(2), 584; https://doi.org/10.3390/pr13020584 - 19 Feb 2025

Cited by 1 | Viewed by 404

Abstract

The traditional power load forecasting learning method has problems such as overfitting and incomplete learning of time series information when dealing with complex nonlinear data, which affects the accuracy of short–medium term power load forecasting. A joint learning method, LSVM-MKL, was proposed based [...] Read more.

The traditional power load forecasting learning method has problems such as overfitting and incomplete learning of time series information when dealing with complex nonlinear data, which affects the accuracy of short–medium term power load forecasting. A joint learning method, LSVM-MKL, was proposed based on the bidirectional promotion of deep kernel learning (DKL) and multiple kernel learning (MKL). The multi-kernel method was combined with the input layer, the highest coding layer, and the highest encoding layer to model the network of the stack autoencoder (SAE) to obtain more comprehensive information. At the same time, the deep kernel was integrated into the optimization training of Gaussian multi-kernel by means of the nonlinear product to form the nonlinear composite kernel. Through a large number of reference datasets and actual industrial data experiments, it was shown that compared with the Elman and LSTM-Seq2Seq methods, the proposed method achieved a higher prediction accuracy of 4.32%, which verified its adaptability to complex time-varying power load forecasting processes and greatly improved the accuracy of power load forecasting. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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28 pages, 19163 KiB

Open AccessArticle

Quick Combustion Optimization for Utility Boilers Using a Novel Adaptive Hybrid Case Library

by Cong Yu, Shuo Chen, Haiquan Yu, Yukun Zhu, Qiang Wang, Guangting Liao and Ling Shi

Processes 2025, 13(2), 469; https://doi.org/10.3390/pr13020469 - 8 Feb 2025

Viewed by 462

Abstract

To achieve carbon neutrality, thermal power plants must undertake heavier peak shaving tasks; consequently, utility boilers will be required to operate under frequently changing operating conditions. In light of this new circumstance, a combustion optimization decision that is executed more rapidly is necessary. [...] Read more.

To achieve carbon neutrality, thermal power plants must undertake heavier peak shaving tasks; consequently, utility boilers will be required to operate under frequently changing operating conditions. In light of this new circumstance, a combustion optimization decision that is executed more rapidly is necessary. A novel online combustion optimization framework is proposed for the combustion system of utility boilers. First, a robust filter for extracting high-quality steady-state data samples is designed and executed. Then, the K-means algorithm is used to divide the cleaned sample space and construct the working condition case library. Based on the constructed library, the boiler combustion model is constructed using the XGBoost algorithm. Therefore, the corresponding optimization case library can be established using the multiobjective optimization algorithm and working condition case library. To further capture the phenomenon of data distribution migrating as the operating conditions change, an adaptive update strategy for the combustion system is proposed, including online querying and data and model updates. The findings of this study conducted on a 660 MW utility boiler show that the proposed online optimization method can effectively decrease NO_x emissions and improve combustion efficiency in approximately 2 milliseconds. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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27 pages, 3503 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Thermodynamic Model-Based Synthesis of Heat-Integrated Work Exchanger Networks

by Aida Amini-Rankouhi, Abdurrafay Siddiqui and Yinlun Huang

Processes 2024, 12(10), 2293; https://doi.org/10.3390/pr12102293 - 19 Oct 2024

Viewed by 980

Abstract

Heat integration has been widely and successfully practiced for recovering thermal energy in process plants for decades. It is usually implemented through synthesizing heat exchanger networks (HENs). It is recognized that mechanical energy, another form of energy that involves pressure-driven transport of compressible [...] Read more.

Heat integration has been widely and successfully practiced for recovering thermal energy in process plants for decades. It is usually implemented through synthesizing heat exchanger networks (HENs). It is recognized that mechanical energy, another form of energy that involves pressure-driven transport of compressible fluids, can be recovered through synthesizing work exchanger networks (WENs). One type of WEN employs piston-type work exchangers, which demonstrates techno-economic attractiveness. A thermodynamic-model-based energy recovery targeting method was developed to predict the maximum amount of mechanical energy feasibly recoverable by piston-type work exchangers prior to WEN configuration generation. In this work, a heat-integrated WEN synthesis methodology embedded by the thermodynamic model is introduced, by which the maximum mechanical energy, together with thermal energy, can be cost-effectively recovered. The methodology is systematic and general, and its efficacy is demonstrated through two case studies that highlight how the proposed methodology leads to designs simpler than those reported by other researchers while also having a lower total annualized cost (TAC). Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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17 pages, 9965 KiB

Open AccessArticle

Fault Intelligent Diagnosis for Distribution Box in Hot Rolling Based on Depthwise Separable Convolution and Bi-LSTM

by Yonglin Guo, Di Zhou, Huimin Chen, Xiaoli Yue and Yuyu Cheng

Processes 2024, 12(9), 1999; https://doi.org/10.3390/pr12091999 - 17 Sep 2024

Viewed by 932

Abstract

The finishing mill is a critical link in the hot rolling process, influencing the final product’s quality, and even economic efficiency. The distribution box of the finishing mill plays a vital role in power transmission and distribution. However, harsh operating conditions can frequently [...] Read more.

The finishing mill is a critical link in the hot rolling process, influencing the final product’s quality, and even economic efficiency. The distribution box of the finishing mill plays a vital role in power transmission and distribution. However, harsh operating conditions can frequently lead to distribution box damage and even failure. To diagnose faults in the distribution box promptly, a fault diagnosis network model is constructed in this paper. This model combines depthwise separable convolution and Bi-LSTM. Depthwise separable convolution and Bi-LSTM can extract both spatial and temporal features from signals. This structure enables comprehensive feature extraction and fully utilizes signal information. To verify the diagnostic capability of the model, five types of data are collected and used: the pitting of tooth flank, flat-headed sleeve tooth crack, gear surface crack, gear tooth surface spalling, and normal conditions. The model achieves an accuracy of 97.46% and incorporates a lightweight design, which enhances computational efficiency. Furthermore, the model maintains approximately 90% accuracy under three noise conditions. Based on these results, the proposed model can effectively diagnose faults in the distribution box, and reduce downtime in engineering. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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13 pages, 291 KiB

Open AccessArticle

Modified Deng’s Grey Relational Analysis Model for Panel Data and Its Applications in Assessing the Water Environment of Poyang Lake

by Fanghong Jian, Jiangfeng Li, Xiaomei Liu, Qiong Wu and Dan Zhong

Processes 2024, 12(9), 1935; https://doi.org/10.3390/pr12091935 - 9 Sep 2024

Viewed by 955

Abstract

Deng’s grey relational analysis (GRA) model is widely used in clustering because of its simple mathematical mechanisms. For sample data of different dimensions, people have put forward different Deng’s GRA models, including time series data, panel data, and panel time series data. The [...] Read more.

Deng’s grey relational analysis (GRA) model is widely used in clustering because of its simple mathematical mechanisms. For sample data of different dimensions, people have put forward different Deng’s GRA models, including time series data, panel data, and panel time series data. The purpose of this paper is to improve the clustering accuracy of the existing Deng’s GRA model for panel data in order to overcome some of its shortcomings. Firstly, the existing Deng’s GRA model for panel data was tested based on the dataset LP1 of Robot Execution Failures. Then, according to the test results, the existing Deng’s GRA model for panel data is modified by means of Taylor’s formula, and the modified model is successfully validated by the dataset LP1 of Robot Execution Failures. Finally, as a practical application, the modified Deng’s GRA model for panel data is applied to assess the water environment of Poyang Lake over the past five years. Compared with other cluster methods, the results of the case study show that the modified Deng’s GRA model for panel data is applicable and also confirm the remarkable effectiveness of the Chinese government’s water quality regulation in Poyang Lake. Therefore, the modified Deng’s GRA model presented in this paper improves the clustering accuracy compared to the original model and can be applied well to the classification of data with a large dimension. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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16 pages, 7795 KiB

Open AccessArticle

Local Path Planner for Mobile Robot Considering Future Positions of Obstacles

by Xianhua Ou, Zhongnan You and Xiongxiong He

Processes 2024, 12(5), 984; https://doi.org/10.3390/pr12050984 - 12 May 2024

Cited by 2 | Viewed by 2196

Abstract

Local path planning is a necessary ability for mobile robot navigation, but existing planners are not sufficiently effective at dynamic obstacle avoidance. In this article, an improved timed elastic band (TEB) planner based on the requirements of mobile robot navigation in dynamic environments [...] Read more.

Local path planning is a necessary ability for mobile robot navigation, but existing planners are not sufficiently effective at dynamic obstacle avoidance. In this article, an improved timed elastic band (TEB) planner based on the requirements of mobile robot navigation in dynamic environments is proposed. The dynamic obstacle velocities and TEB poses are fully integrated through two-dimensional (2D) lidar and multi-obstacle tracking. First, background point filtering and clustering are performed on the lidar points to obtain obstacle clusters. Then, we calculate the data association matrix of the obstacle clusters of the current and previous frame so that the clusters can be matched. Thirdly, a Kalman filter is adopted to track clusters and obtain the optimal estimates of their velocities. Finally, the TEB poses and obstacle velocities are associated: we predict the obstacle position corresponding to the TEB pose through the detected obstacle velocity and add this constraint to the corresponding TEB pose vertex. Then, a pose sequence considering the future positions of obstacles is obtained through a graph optimization algorithm. Compared with the original TEB, our method reduces the total running time by 22.87%, reduces the running distance by 19.23%, and increases the success rate by 21.05%. Simulations and experiments indicate that the improved TEB enables robots to efficiently avoid dynamic obstacles and reach the goal as quickly as possible. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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23 pages, 11489 KiB

Open AccessArticle

Data-Driven-Based Intelligent Alarm Method of Ultra-Supercritical Thermal Power Units

by Xingfan Zhang, Lanhui Ye, Cheng Zhang and Chun Wei

Processes 2024, 12(5), 889; https://doi.org/10.3390/pr12050889 - 28 Apr 2024

Viewed by 1036

Abstract

In order to ensure the safe operation of the ultra-supercritical thermal power units (USCTPUs), this paper proposes an intelligent alarm method to enhance the performance of the alarm system. Firstly, addressing the issues of slow response and high missed alarm rate (MAR [...] Read more.

In order to ensure the safe operation of the ultra-supercritical thermal power units (USCTPUs), this paper proposes an intelligent alarm method to enhance the performance of the alarm system. Firstly, addressing the issues of slow response and high missed alarm rate (MAR) in traditional alarm systems, a threshold optimization method is proposed by integrating kernel density estimation (KDE) and convolution optimization algorithm (COA). Based on the traditional approach, the expected detection delay (EDD) indicator is introduced to better evaluate the response speed of the alarm system. By considering the false alarm rate (FAR), and EDD, a threshold optimization objective function is constructed, and the COA is employed to obtain the optimal alarm threshold. Secondly, to address the problem of excessive nuisance alarms, this paper reduces the number of nuisance alarms by introducing an adaptive delay factor into the existing system. Finally, simulation results demonstrate that the proposed method significantly reduces the MAR and EDD, improves the response speed and performance of the alarm system, and effectively reduces the number of nuisance alarms, thereby enhancing the quality of the alarms. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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17 pages, 4576 KiB

Open AccessArticle

Rotating Machinery Fault Diagnosis under Time–Varying Speed Conditions Based on Adaptive Identification of Order Structure

by Xinnan Yu, Xiaowang Chen, Minggang Du, Yang Yang and Zhipeng Feng

Processes 2024, 12(4), 752; https://doi.org/10.3390/pr12040752 - 8 Apr 2024

Cited by 1 | Viewed by 1824

Abstract

Rotating machinery fault diagnosis is of key significance for ensuring safe and efficient operation of various industrial equipment. However, under nonstationary operating conditions, the fault–induced characteristic frequencies are often time–varying. Conventional Fourier spectrum analysis is not suitable for revealing time–varying details, and nonstationary [...] Read more.

Rotating machinery fault diagnosis is of key significance for ensuring safe and efficient operation of various industrial equipment. However, under nonstationary operating conditions, the fault–induced characteristic frequencies are often time–varying. Conventional Fourier spectrum analysis is not suitable for revealing time–varying details, and nonstationary fault feature extraction methods are still in desperate need. Order spectrum can reveal the rotational–speed–related time–varying frequency components as spectral peaks in order domain, thus facilitating fault feature extraction under time–varying speed conditions. However, the speed–unrelated frequency components are still nonstationary after angular–domain resampling, thus causing wide–band features and interferences in the order spectrum. To overcome such a drawback, this work proposes a rotating machinery fault diagnosis method based on adaptive separation of time–varying components and order feature extraction. Firstly, the rotational speed is estimated by the multi–order probabilistic approach (MOPA), thus eliminating the inconvenience of installing measurement equipment. Secondly, adaptive separation of the time–varying frequency component is achieved through time–varying filtering and surrogate test. It effectively eliminates interference from irrelevant components and noise. Finally, a high–resolution order spectrum is constructed based on the average amplitude envelope of each mono–component. It does not involve Fourier transform or angular–domain resampling, thus avoiding spectral leakage and resampling errors. By identifying the fault–related spectral peaks in the constructed order spectrum, accurate fault diagnosis can be achieved. The Rényi entropy values of the proposed order spectrum are significantly lower than those of the traditional order spectrum. This result verifies the effective energy concentration and high resolution of the proposed order spectrum. The results of both numerical simulation and lab experiments confirm the effectiveness of the proposed method in accurately presenting the time–varying frequency components for rotating machinery diagnosing faults. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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18 pages, 13951 KiB

Open AccessArticle

Modeling and Switched Control of Modular Reconfigurable Flight Array for Faulty Redundancy

by Bin Ren, Chunxi Yang, Xiufeng Zhang and Wenyuan Mao

Processes 2024, 12(4), 646; https://doi.org/10.3390/pr12040646 - 24 Mar 2024

Cited by 1 | Viewed by 1232

Abstract

The modular reconfigurable flight array (MRFA) is composed of multiple identical flight unit modules, which has several advantages such as structural variability, strong versatility, and low cost. Due to the redundant properties of MRFA, it keeps stable by adopting a suitable control law [...] Read more.

The modular reconfigurable flight array (MRFA) is composed of multiple identical flight unit modules, which has several advantages such as structural variability, strong versatility, and low cost. Due to the redundant properties of MRFA, it keeps stable by adopting a suitable control law when it suffers actuator fault or actively stops some actuators. To address the attitude stability issue of the modular flight array when actuators actively stop or encounter failures during the flight process, a modeling method based on a switched system is proposed at first, and an arbitrary switched controller design method based on the segmented Lyapunov functions and the average dwell time is also given. By introducing the actuator efficiency matrix, the dynamic switched model of the modular flight array is described. Then, a group of arbitrary switched linear feedback gains is designed to ensure the exponential stability of the flight array if the switched process satisfies the constraint of the average dwell time. Simulation and experiment results indicate that when there is an accident in the actuator states, the switched controllers can achieve precise tracking of the desired trajectory, thus confirming the effectiveness of the proposed modeling method and controller. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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17 pages, 5584 KiB

Open AccessArticle

Measurement Method of Bar Unmanned Warehouse Area Based on Binocular Vision

by Shuzong Yan, Dong Xu, He Yan, Ziqiang Wang, Hainan He, Xiaochen Wang and Quan Yang

Processes 2024, 12(3), 466; https://doi.org/10.3390/pr12030466 - 25 Feb 2024

Cited by 1 | Viewed by 1404

Abstract

With the development of Industry 4.0 and the implementation of the 14th Five-Year Plan, intelligent manufacturing has become a significant trend in the steel industry, which can propel the steel industry toward a more intelligent, efficient, and sustainable direction. At present, the operation [...] Read more.

With the development of Industry 4.0 and the implementation of the 14th Five-Year Plan, intelligent manufacturing has become a significant trend in the steel industry, which can propel the steel industry toward a more intelligent, efficient, and sustainable direction. At present, the operation mode of unmanned warehouse area for slabs and coils has become relatively mature, while the positioning accuracy requirement of bars is getting more stringent because they are stacked in the warehouse area according to the stacking position and transferred by disk crane. Meanwhile, the traditional laser ranging and line scanning method cannot meet the demand for precise positioning of the whole bundle of bars. To deal with the problems above, this paper applies machine vision technology to the unmanned warehouse area of bars, proposing a binocular vision-based measurement method. On the one hand, a 3D reconstruction model with sub-pixel interpolation is established to improve the accuracy of 3D reconstruction in the warehouse area. On the other hand, a feature point matching algorithm based on motion trend constraint is established by means of multi-sensor data fusion, thus improving the accuracy of feature point matching. Finally, a high-precision unmanned 3D reconstruction of the bar stock area is completed. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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15 pages, 3195 KiB

Open AccessArticle

Online Partition-Cooling System of Hot-Rolled Electrical Steel for Thermal Roll Profile and Its Industrial Application

by Qiuna Wang, Jiquan Sun, Jiaxuan Yang, Haishen Wang, Lijie Dong, Yanlong Jiao, Jieming Li, Zhenyang Zhi and Lipo Yang

Processes 2024, 12(2), 410; https://doi.org/10.3390/pr12020410 - 18 Feb 2024

Viewed by 1328

Abstract

The shape and convexity are crucial quality assessment indicators for hot-rolled electrical steel strips. Besides bending rolls, shifting rolls, and the original roll profile, the thermal roll profile also plays a significant role in controlling the shape and convexity during the hot-rolling process. [...] Read more.

The shape and convexity are crucial quality assessment indicators for hot-rolled electrical steel strips. Besides bending rolls, shifting rolls, and the original roll profile, the thermal roll profile also plays a significant role in controlling the shape and convexity during the hot-rolling process. However, it is always overlooked due to its dynamic uncertainty. To solve this problem, it is necessary to achieve online cooling-status control for the local thermal expansion of rolls. Based on the existing structure of a mill, a pair of special partition-cooling beams with an intelligent cooling system was designed. For high efficiency and practicality, a new online predictive model was established for the dynamic temperature field of the hot-rolling process. An equivalent treatment was applied to the boundary condition corresponding to the practical cooling water flow. In addition, by establishing the corresponding target distribution curve for the partitioned water flow cooling, online water-flow-partitioning control of the thermal roll profile was achieved. In the practical application process, a large number of onsite results exhibited that the predicted error was within 5% compared to the experimental results. The temperature difference between the upper and lower rolls was within 5 °C, and the temperature difference on both sides of the rolls was controlled within 0.7 °C. The hit rate of convexity (C40) increased by 33%. It was demonstrated that the partition-cooling processes of hot rolling are effective for the local shape and special convexity. They are able to serve as a better control method in the hot-rolling process. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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23 pages, 945 KiB

Open AccessArticle

Novel Multi-Criteria Group Decision Making Method for Production Scheduling Based on Group AHP and Cloud Model Enhanced TOPSIS

by Xuejun Zhang, Zhimin Lv, Yang Liu, Xiong Xiao and Dong Xu

Processes 2024, 12(2), 305; https://doi.org/10.3390/pr12020305 - 1 Feb 2024

Cited by 3 | Viewed by 1474

Abstract

Optimized production scheduling can greatly improve efficiency and reduce waste in the steel manufacturing industry. With the increasing demands on the economy, the environment, and society, more and more factors need to be considered in the production scheduling process. Currently, only a few [...] Read more.

Optimized production scheduling can greatly improve efficiency and reduce waste in the steel manufacturing industry. With the increasing demands on the economy, the environment, and society, more and more factors need to be considered in the production scheduling process. Currently, only a few methods are developed for the comprehensive evaluation and prioritization of scheduling schemes. This paper proposes a novel MCGDM (multi-criteria group decision making) method for the ranking and selection of production scheduling schemes. First, a novel indicator system involving both qualitative and quantitative indicators is put forward. Diverse statistical methods and evaluation functions are proposed for the evaluation of quantitative indicators. The evaluation method of qualitative indicators is proposed based on heterogeneous data, cloud model theory, and group decision-making techniques. Then, a novel Group AHP model is proposed to determine the weights of all evaluation indicators. Finally, a novel cloud-model-enhanced TOPSIS (technique for order of preference by similarity to ideal solution) method is proposed to rank alternative production scheduling schemes. A practical example is presented to show the implementation details and demonstrate the feasibility of our proposed method. The results and comparative analysis indicate that our hybrid MCGDM method is more reasonable, flexible, practical, and effective in evaluating and ranking production scheduling schemes in an uncertain environment. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)

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