Field Of Industrial Control Research Articles

The Application of PLC in Industry Fields

This article explores the widespread application and importance of the Programmable Logic Controller (PLC) in industry. PLC, as an industrial controller designed specifically for industrial environments, occupies an important position in the field of industrial automation control due to its advantages of being less susceptible to interference, high reliability, easy programming, fast installation, and small size. With the rapid development of PLC technology, great progress has been made in the development of technology, hardware, and software, resulting in increasingly powerful functionalities and significantly enhanced system development and compatibility. This paper mainly analyzes the design ideas and applications of PLC in life and industry in detail, aiming to research PLC and find more scenes in industry fields in which PLC can participate. PLC has a wide range of applications and significant importance in industry. The continuous progress and innovation of its technology will bring more opportunities and challenges to the field of industrial automation control. In the future, with the deepening development of advanced manufacturing models such as Industry 4.0 and intelligent manufacturing, PLC technology is expected to play a greater role in more fields and promote industrial automation to a higher level.

Performance Analysis and Prediction of 5G Round-Trip Time Based on the VMD-LSTM Method

With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure the certainty of information transmission, making it especially difficult to meet the requirements of low latency in industrial manufacturing fields. The 5G technology is characterized by a high transmission rate and low latency; therefore, it has good prospects in industrial applications. To apply 5G technology to factory environments with low latency requirements for data transmission, in this study, we analyze the statistical performance of the round-trip time (RTT) in a 5G-R15 communication system. The results indicate that the average value of 5G RTT is about 11 ms, which is less than the 25 ms of WIA-FA. We then consider 5G RTT data as a group of time series, utilizing the augmented Dickey–Fuller (ADF) test method to analyze the stability of the RTT data. We conclude that the RTT data are non-stationary. Therefore, firstly, the original 5G RTT series are subjected to first-order differencing to obtain differential sequences with stronger stationarity. Then, a time series analysis-based variational mode decomposition–long short-term memory (VMD-LSTM) method is proposed to separately predict each differential sequence. Finally, the predicted results are subjected to inverse difference to obtain the predicted value of 5G RTT, and a predictive error of 4.481% indicates that the method performs better than LSTM and other methods. The prediction results could be used to evaluate network performance based on business requirements, reduce the impact of instruction packet loss, and improve the robustness of control algorithms. The proposed early warning accuracy metrics for control issues can also be used to indicate when to retrain the model and to indicate the setting of the control cycle. The field of industrial control, especially in the manufacturing industry, which requires low latency, will benefit from this analysis. It should be noted that the above analysis and prediction methods are also applicable to the R16 and R17 versions.

Strategic data augmentation with CTGAN for smart manufacturing: Enhancing ML predictions of paper breaks in pulp-and-paper production

This article addresses the significant challenge posed by the rare occurrence of paper breaks in the pulp-and-paper industry, which, despite their infrequency during production, have considerable economic consequences. We analyze operational data from a paper manufacturing machine, focusing on the rare instances of paper breaks (124 out of 18,398 cases) identified through a quality assurance protocol. This scarcity presents a challenge for machine learning-based predictive models. To overcome this, we introduce a novel data augmentation strategy using Conditional Generative Adversarial Networks (CTGAN) and Synthetic Minority Oversampling Technique (SMOTE), aimed at generating synthetic data that not only reflects the real operational data’s distribution but also improves the predictive models’ performance metrics. We assess the impact of this data augmentation on three machine learning algorithms—Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR)—both before and after augmentation. The use of a CTGAN-enhanced dataset led to notable enhancements in predictive maintenance metrics. Specifically, the ability of the models to detect machine breaks (Class 1) increased significantly: over 30% for Decision Trees, 20% for Random Forest, and nearly 90% for Logistic Regression. This methodological progress offers valuable contributions to the fields of industrial quality control and maintenance scheduling by improving the prediction of rare events in manufacturing processes.

PMF based Sagnac interferometric sensor for simultaneous measurement of strain, temperature, and torsion

We propose a Sagnac interferometric sensor for simultaneous strain, temperature, and torsion measurement based on a polarization-maintaining fiber (PMF). The Sagnac sensor consists of a single-mode fiber (SMF), PMF and SMF spliced sequentially to form an SMF-PMF-SMF structure. The designed Sagnac interferometric sensor exhibits different wavelength shifts for strain, temperature and torsion measurements. Therefore, we observed three selected dips in the interferogram and measured strain, temperature and torsion simultaneously by their wavelength shifts. The maximum sensitivity of the sensor to strain, temperature, and torsion were experimentally measured to be 28.9 pm/µε. (10-12 m), −1.7504 nm/°C, and −1.0964 nm/(rad/m), and the measurement ranges were 0∼720 με, 25 °C–39 °C, and −7.679 rad/m∼4.887 rad/m (−110°–70°), respectively. In the subsequent experiments, we also verified the feasibility of the sensor for simultaneous measurements, and the errors of the results obtained were less than 10%. And we have successfully increased the temperature measurement range to 25°C∼85°C by cascading FBG in the original structure. The designed sensor has the advantages of easy fabrication, high sensitivity, and bi-directional torsional measurement, which have a wide range of applications in the fields of structural health detection, industrial control, and aircraft attitude recognition.

Design and Implementation of IPsec VPN IoT Gateway System in National Secret Algorithm

With the development of Internet of Things technology, the security threats faced by the industrial control field are increasing, and strengthening the security protection capabilities of intelligent systems on IoT highways is becoming increasingly important. IPSec VPN tunneling technology can achieve identity authentication and encrypted data transmission, and is an important means to achieve secure data transmission in intelligent systems on Expressway intelligent tunnel system. The commonly used IPSec VPN gateway uses a traditional Linux protocol stack-based approach for data capture, which requires multiple data copies and context switching, resulting in low efficiency of IPSec services. In addition, the commonly used IPSec VPN security gateway is implemented on the basis of the open-source IPSec framework, using internationally recognized algorithms for encryption and decryption, which poses security risks. This article is based on the IPSec protocol, and studies the high-speed network packet capture framework PFRING technology, the fusion technology of national secret algorithm and IPSec protocol. It designs and implements an IPSec VPN IoT security gateway based on national secret algorithm. After experimental verification, the IPSec VPN gateway system constructed in this article has complete functions and better performance than the common open-source IPSec frameworks OpenSwan and strongSwan, and can meet the application requirements of IoT data encryption transmission.

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.

Er3+/Yb3+/Ho3+ tri-doped no-core fiber temperature sensor based on fluorescence intensity ratio technology: towards high stability and accurate measurements.

In this paper, the green upconversion (UC) fluorescence emission from E r 3+/Y b 3+/H o 3+ tri-doped tellurite glass is investigated for temperature sensing. The doping of H o 3+ ions not only enhances the chance of energy level transition but also avoids the influence of the thermal effect caused by the proximity of 2 H 11/2 and 4 S 3/2 energy levels. The luminescence characteristics at different Y b 3+ and H o 3+ ion concentration doping molar ratios were investigated, and the strongest luminescence characteristics were exhibited when the Y b 3+ ion concentration was at 5mol% and H o 3+ at 0.2mol%. Based on this, a tri-doped T e O 2-Z n O-B i 2 O 3 (TZB) no-core fiber was fabricated and connected with multimode fibers (MMFs) to form a temperature sensor. The temperature sensing performance of the tri-doped TZB temperature sensor was evaluated in detail over the temperature range of 255-365K. The repeatability and stability of the temperature sensor was experimentally verified. The E r 3+/Y b 3+/H o 3+ tri-doped sensor can be used for noninvasive optical temperature sensing in the fields of environmental monitoring, biological sensing, and industrial process temperature control, etc.

Design of Car Control Algorithm Based on Deep Reinforcement Learning

Reinforcement learning algorithm is widely used in the field of agent control because of its high robustness and strong adaptability. The problem of Mountain-Car-v0 is a classic control problem, which attracts the attention of many researchers. Traditional control methods, for example, PID control, are widely used in industrial and robot control fields, but they show obvious limitations in the face of such highly nonlinear and unknown environments. Therefore, this paper proposes a car control algorithm based on deep reinforcement learning, aiming at overcoming the shortcomings of traditional methods and realizing efficient control of complex dynamic systems. By combining the automatic extraction of high-dimensional features of deep learning with the dynamic decision-making mechanism of reinforcement learning, an adaptive control algorithm is designed, which can continuously learn and optimize in the interaction with the environment without establishing an accurate system model in advance. The experimental results show that compared with traditional control methods and other intelligent control strategies, the proposed algorithm shows higher efficiency and better adaptability in the task of Mountain-car-V0, and obviously improves the success rate of the Mountain-Car-v0.

Evaluation on the design of embedded platform vision system under the background of artificial intelligence

At present, the application of Artificial Intelligence (AI) in industrial control, smart home and other fields has received good response. However, AI technology has certain requirements for computer performance, and also faces problems in network security, data analysis, human-computer interaction, etc. At present, the visual platform of embedded system has achieved remarkable results in practical applications, but its development has been seriously hampered by problems such as low overall development efficiency and unstable system performance. This paper designed an EP Vision System (VS) based on AI technology. The platform combined the embedded hardware design with the Support Vector Machine (SVM) algorithm to realize the intelligent robot interaction and target detection functions. The test results showed that when other conditions were the same, students and experts had 83.5% and 90% positive evaluations of System X, and 16.5% and 10% negative evaluations respectively. However, their positive evaluation of System Y only accounted for 19% and 4%, while the negative evaluation accounted for 81% and 96%. The proportion of positive evaluation of System X was much higher than that of System Y, which indicated that System X can meet the actual application requirements and improve the system recognition efficiency to a certain extent. It showed the positive relationship between AI technology and EP VS.

Real-time hollow defect detection in tiles using on-device tiny machine learning

This study addresses the challenge of subsurface defect detection in floor tiles for quality control in residential construction. To overcome the limitations of traditional inspection methods and the complexities associated with existing artificial intelligence (AI)-based approaches, we have developed the AI diagnostic Stick (AID-Stick), a novel tool designed to advance the field of tile defect detection. This innovative tool integrates an embedded machine-learning framework, leveraging convolutional neural networks and tiny machine learning techniques. The AID-Stick utilizes spectrogram, Mel-frequency cepstral coefficient, and Mel filterbank energy for real-time, on-microcontroller unit diagnostics of auditory signals from tile tapping tests. Our methodology effectively utilizes these acoustic features in distinguishing between intact and subsurface hollow defective tiles. The study’s findings, revealing a notable validation accuracy of 97% and a real-world accuracy of 81.25%, showcase a promising improvement over traditional methods. The AID-Stick’s practicality, cost-effectiveness, and user-friendly design make it potentially beneficial for small-to-medium enterprises and economically constrained markets. Furthermore, this research opens avenues for future enhancements in embedded AI systems, with potential applications extending beyond the construction industry to other domains requiring non-destructive testing. This work not only contributes to the field of industrial quality control but also to the development of intelligent diagnostic tools, paving the way for future innovations in automated inspection technologies.

Bridging the Cybersecurity Gap: A Comprehensive Analysis of Threats to Power Systems, Water Storage, and Gas Network Industrial Control and Automation Systems

This research addresses the dearth of real-world data required for effective neural network model building, delving into the crucial field of industrial control and automation system (ICS) cybersecurity. Cyberattacks against ICS are first identified and then generated in an effort to raise awareness of vulnerabilities and improve security. This research aims to fill a need in the existing literature by examining the effectiveness of a novel approach to ICS cybersecurity that draws on data from real industrial settings. Real-world data from a variety of commercial sectors is used in this study to produce a complete dataset. These sectors include power systems, freshwater tanks, and gas pipelines, which together provide a wide range of commercial scenarios where anomaly detection and attack classification approaches are critical. The generated data are shown to considerably improve the models’ precision. An amazing 71% accuracy rate is achieved in power system models, and incorporating generated data reliably increases network speed. Using generated data, the machine learning system achieves an impressive 99% accuracy in a number of trials. In addition, the system shows about 90% accuracy in most studies when applied to the setting of gas pipelines. In conclusion, this article stresses the need to improve cybersecurity in vital industrial sectors by addressing the dearth of real-world ICS data. To better understand and defend against cyberattacks on industrial machinery and automation systems, it demonstrates how generative data can improve the precision and dependability of neural network models.

Preparation and Properties of Graphene Flexible Membrane

Graphene material has excellent electrical, thermal and mechanical properties, and the flexible film prepared by it can be widely used in intelligent wearables, industrial temperature control, biomedicine, home heating and other fields. In this paper, graphene paste was used to prepare textile substrate and non-substrate graphene flexible film, and its tensile strength, elongation at break, resistivity, hardness, modulus and other parameters were tested. The results show that the graphene flexible film has smooth appearance, good electrical conductivity and good mechanical strength, and is a good choice for smart wear and industrial parts gasket applications.

Optimal Control of Mechanical Systems Based on Path-Fitted Variational Integrators

Abstract In view of the crucial importance of optimal control in many application areas and the improved performance of path-fitted variational integrators, the paper links these two aspects and presents a methodology to find optimal control policies for mechanical systems. The main process of the methodology is employing path-fitted variational integrators to discretize the forced mechanical equations and further take the obtained discrete equations as equality constraints for the final optimization problem. Simultaneously, the discretization also provides a reasonable way to approximate the objective function and incorporate the boundary conditions. With the transformation of optimal control problems into nonlinear optimization problems, all the benefits of path-fitted variational integrators are inherited by the presented methodology, mainly expressed in giving more faithful optimizations and thus more accurate solutions, providing a greater possibility of global optimality, as well as conserving computed control efforts. These superiorities, verified by the optimal control of an overhead crane, indicate that the methodology has high potential application in industrial control field.

Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory

With the development of industrial demand, precise identification of system models is currently required in the field of industrial control, which limits the whale search algorithm. In response to the fact that whale optimization algorithms are prone to falling into local optima and the identification of important Hammerstein models ignores the issue of noise outliers in actual industrial environments, this study improves the whale algorithm and constructs a Hammerstein model identification strategy for nonlinear systems under heavy‐tailed noise using the improved whale algorithm. Results showed that it had a lower rank average and an average success rate of 95.65%. It found the global optimum when the number of iterations reached around 150 and had faster convergence speed and accuracy. In identifying Hammerstein model under heavy‐tailed noise, the average prediction recognition accuracy of the improved whale algorithm was 92.38%, the determination coefficient was 0.89, the percentage fitting error was 0.03, and the system error was 0.02. This research achievement has certain value in the field of industrial control and can serve as a technical reference.

A Lightweight Unsupervised Intrusion Detection Model Based on Variational Auto-Encoder.

With the gradual integration of internet technology and the industrial control field, industrial control systems (ICSs) have begun to access public networks on a large scale. Attackers use these public network interfaces to launch frequent invasions of industrial control systems, thus resulting in equipment failure and downtime, production data leakage, and other serious harm. To ensure security, ICSs urgently need a mature intrusion detection mechanism. Most of the existing research on intrusion detection in ICSs focuses on improving the accuracy of intrusion detection, thereby ignoring the problem of limited equipment resources in industrial control environments, which makes it difficult to apply excellent intrusion detection algorithms in practice. In this study, we first use the spectral residual (SR) algorithm to process the data; we then propose the improved lightweight variational autoencoder (LVA) with autoregression to reconstruct the data, and we finally perform anomaly determination based on the permutation entropy (PE) algorithm. We construct a lightweight unsupervised intrusion detection model named LVA-SP. The model as a whole adopts a lightweight design with a simpler network structure and fewer parameters, which achieves a balance between the detection accuracy and the system resource overhead. Experimental results on the ICSs dataset show that our proposed LVA-SP model achieved an F1-score of 84.81% and has advantages in terms of time and memory overhead.

An Intrusion Detection Method Based on Hybrid Machine Learning and Neural Network in the Industrial Control Field

Aiming at the imbalance of industrial control system data and the poor detection effect of industrial control intrusion detection systems on network attack traffic problems, we propose an ETM-TBD model based on hybrid machine learning and neural network models. Aiming at the problem of high dimensionality and imbalance in the amount of sample data in the massive data of industrial control systems, this paper proposes an IG-based feature selection method and an oversampling method for SMOTE. In the ETM-TBD model, we propose a hyperparameter optimization method based on Bayesian optimization used to optimize the parameters of the four basic machine learners in the model. By introducing a multi-head-attention mechanism, the Transformer module increases the attention between local features and global features, enabling the discovery of the internal relationship between features. Additionally, the BiGRU is used to preserve the temporal features of the dataset, while the DNN is used to extract deeper features. Finally, the SoftMax classifier is used to classify the output. By analyzing the results of the comparison and ablation experiments, it can be concluded that the F1-score of the ETM-TBD model on a robotic arm dataset is 0.9665 and the model has very low FNR and FPR scores of 0.0263 and 0.0081, respectively. It can be seen that the model in this paper is better than the traditional single machine learning algorithm as well as the algorithm lacking any of the modules.

Design and Research of Power Battery Temperature Control by PLC

With the increasingly serious environmental pollution, electric vehicles are more and more favored by people, its power battery temperature control is an important premise to ensure driving safety. At the same time, with the gradual advancement of automobile intelligence and fully automatic driving program, the use of more powerful, more compatible controller has become an important research direction. In this paper, S7 200 Smart PLC, which is currently mature in the field of rail transit and industrial control, is used as the control core to build the hardware platform. Step 7-Microwin Smart collaborates with Kingview software to develop the control algorithm and interface, so as to realize the purpose of power battery temperature control and human-computer interaction. It has high research significance.

Efficient Revocable Anonymous Authentication Mechanism for Edge Intelligent Controllers

In the field of industrial control, edge computing puts forward higher requirements for the local processing ability of data. The traditional industrial Programmable Logic Controller (PLC) cannot complete this task. Edge Intelligent Controller (EIC) is developed according to the demand for edge computing. As the core component of edge computing, the security and reliable operation of EIC has great significance and influence on the promotion and development of edge computing. For the access authentication of EIC, an efficient revocable anonymous authentication scheme for EICs by utilizing a group signature technique is proposed in this paper, which can trace the identity information of the EIC and protect the privacy of the EIC, and apply an efficient revocation mechanism to revoke any illegal or abnormal EICs. Through security analysis, we prove the authentication scheme is anonymity, integrity, traceability, forward secrecy, resistance to the replay attack and efficient revocability. Furthermore, the performance analysis and comparisons show that the authentication scheme is efficient and feasible for EICs.

A Marine Magnetometer Based on TMR

TMR (tunneling magnetoresistance) has the characteristics of high sensitivity, large linear range, low power consumption, and easy miniaturization, and has received great attention in the field of miniaturized and high-performance magnetic sensors. Widely used in marine magnetic field detection, earthquake monitoring, industrial control, and other fields. In this paper, A TMR marine magnetometer with AC modulation and magnetic field feedback compensation is proposed, designed, and manufactured, which can be used for the measurement of weak marine magnetic fields. After testing, its measurement range is ±100,000nT, the noise fluctuation level is 0.27nT, and nonlinearity level reaches 0.07%. It works by means of ac modulation and real-time compensation of feedback magnetic field, which greatly reduces magnetic hysteresis and increases the linearity. Compared with the nonlinearity of TMR material (more than 20% within ±1Oe, more than 7% within ±0.3Oe), it has been significantly improved.

A Measuring Tool for Interrupt Latency Based on Linux

Linux has been used more and more in the field of industrial control and robot control, the non-real-time nature of the final human-computer interaction program may cause system performance impact in industrial production. Interrupt response time is an important indicator to measure the real-time performance of real-time operating systems. Accurate test data can be used as an evaluation index for selecting the real-time performance of the operating system. In order to improve the real-time performance of Linux, analyze real-time problems, and solve possible real-time bottlenecks, this paper designs a probe-type tool that can measure the interrupt response time, output the interrupt call stack, and analyze the reasons for high interrupts. By optimizing specific limited and high-latency paths, Linux can still maintain an acceptable range of latency for a long time under various high-load scenarios. After testing, the tool has good measurement accuracy, which provides a basis for developing real-time tasks and solving real-time problems.