Industrial Control Applications Research Articles

Design and Investigation of a High-Performance Quartz-Based SAW Temperature Sensor.

In this work, a surface acoustic wave (SAW) temperature sensor based on a quartz substrate was designed and investigated. Employing the Coupling-of-Modes (COM) model, a detailed analysis was conducted on the effects of the number of interdigital transducers (IDTs), the number of reflectors, and their spacing on the performance of the SAW device. The impact of the transversal mode of quartz SAWs on the device was subsequently examined using the finite element method (FEM). The simulation results indicate that optimizing these structural parameters significantly enhances the sensor's sensitivity and frequency stability. SAW devices with optimal structural parameters were fabricated, and their resonant frequencies were tested across a temperature range of 25-150 °C. Experimental results demonstrate that the SAW temperature sensor maintains high performance stability and data reliability throughout the entire temperature range, achieving a Bode-Q of 7700. Furthermore, the sensor exhibits excellent linearity and repeatability. An analysis of the sensor's response under varying temperature conditions reveals a significant temperature dependency on its Temperature Coefficient of Frequency (TCF). This feature suggests that the sensor possesses potential advantages for applications in industrial process control and environmental monitoring.

Two-cylinder Synchronous Electro-hydraulic Servo System and its Control Technology Development

Background: Electro-hydraulic servo control system synchronous control in industrial application is very extensive because the application of electro-hydraulic servo control system is mostly in a poor working environment, there are a variety of complex external factors. Objective: Improve the comprehensive performance indicators such as accuracy, speed, and stability of synchronous control of hydraulic cylinders Methods: In the process of synchronous movement, the typical hardware structure and control methods and algorithm strategies of the double hydraulic cylinder are analyzed in detail, and the basic principle of common hydraulic double cylinder synchronous control is summarized. Results: Through the comprehensive analysis of papers and patent documents related to the synchronous control of hydraulic twin cylinders, these papers and patented technologies study the control algorithm, and apply the new technology to the synchronous control of electro-hydraulic servo hydraulic cylinders to improve its comprehensive performance indicators such as work accuracy, speed and stability. Conclusion: Referring to the development trajectory of control algorithms in this regard, combined with new control theory and new technology, many achievements can be applied and researched in hydraulic cylinder synchronous control, and better develop the synchronous control system when double hydraulic cylinders and more hydraulic cylinders work together. By collating and analyzing the relevant research results, the two-cylinder synchronous control technology is comprehensively evaluated and prospected, which provides a valuable reference for the research and application in this field.

Efficient Multitask Reinforcement Learning Without Performance Loss.

We propose an iterative sparse Bayesian policy optimization (ISBPO) scheme as an efficient multitask reinforcement learning (RL) method for industrial control applications that require both high performance and cost-effective implementation. Under continual learning scenarios in which multiple control tasks are sequentially learned, the proposed ISBPO scheme preserves the previously learned knowledge without performance loss (PL), enables efficient resource use, and improves the sample efficiency of learning new tasks. Specifically, the proposed ISBPO scheme continually adds new tasks to a single policy neural network while completely preserving the control performance of previously learned tasks through an iterative pruning method. To create a free-weight space for adding new tasks, each task is learned through a pruning-aware policy optimization method called the sparse Bayesian policy optimization (SBPO), which ensures efficient allocation of limited policy network resources for multiple tasks. Furthermore, the weights allocated to the previous tasks are shared and reused in new task learning, thereby improving sample efficiency and the performance of new task learning. Simulations and practical experiments demonstrate that the proposed ISBPO scheme is highly suitable for sequentially learning multiple tasks in terms of performance conservation, efficient resource use, and sample efficiency.

EASY: Energy-Efficient Analysis and Control Processes in the Dynamic Edge-Cloud Continuum for Industrial Manufacturing

AbstractAccording to the guiding principles of Industry 4.0, edge computing enables the data-sovereign and near-real-time processing of data directly at the point of origin. Using these edge devices in manufacturing organization will drive the use of industrial analysis, control, and Artificial Intelligence (AI) applications close to production. The goal of the EASY project is to make the added value of edge computing available by providing an easily usable Edge-Cloud Continuum with a runtime environment and services for the execution of AI-based Analysis and Control processes. Within this continuum, a dynamic, distributed, and optimized execution of services is automated across the entire spectrum from centralized cloud to decentralized edge instances to increase productivity and resource efficiency.

AI-Based Integrated Smart Process Sensor for Emulsion Control in Industrial Application

In industry, reliable process supervision is essential to ensure efficient, safe, and high-quality production. The droplet size distribution represents a critical quality attribute for emulsification processes and should be monitored. For emulsion characterization, image-based analysis methods are well-known but are often performed offline, leading to a time-delayed and error-prone process evaluation. The use of an integrated smart process sensor to characterize the emulsification process over time enables the real-time evaluation of the entire system. The presented integrated smart process sensor consists of an optical measurement flow cell built into a camera system. The overall system is placed in a bypass system of a production plant for emulsification processes. AI-based image evaluation is used in combination with a feature extraction method (You Only Look Once version 4 (YOLOv4) and Hough circle (HC)) to characterize the process over time. The sensor system is installed in the plant and tested with different cosmetic products. Various iteration, prototyping, and test steps for the final sensor design are performed prior to this in a laboratory test setup. The results indicate robust and accurate detection and determination of the droplet size in real time to improve product control and save time. For benchmarking the integrated smart process sensor, the results are compared with common analysis methods using offline samples.

Evaluation of Storage Placement in Computing Continuum for a Robotic Application

This paper analyzes the timing performance of a persistent storage designed for distributed container-based architectures in industrial control applications. The timing performance analysis is conducted using an in-house simulator, which mirrors our testbed specifications. The storage ensures data availability and consistency even in presence of faults. The analysis considers four aspects: 1. placement strategy, 2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Comparison results also reveal that, while the method may underperform current centralized solutions in fault-free conditions, it outperforms the centralized solutions in failure scenario. Moreover, the used evaluation method is applicable for assessing other container-based critical applications with timing constraints that require persistent storage.

Advances in Autonomous Robotics: Integrating AI and Machine Learning for Enhanced Automation and Control in Industrial Applications.

Advances in Autonomous Robotics: Integrating AI and Machine Learning for Enhanced Automation and Control in Industrial Applications.

Solid-state real-time colorimetric sensing of ultra-trace copper ions using aqueous compatible chromatic probe imbued porous polymer monolithic sensor

In this work, we design an aqua-compatible and sustainable chromatic sensor for the selective detection of copper ions in aqueous solutions for application in industrial quality control and environmental analysis. The solid-state chromatic sensor is fabricated through the uniform decoration of an indigenously synthesized unique chromoionophoric probe (TMAP), i.e., (2,2′-(((1E,1′E)-thiophene-2,5-diylbis(methaneylylidene)) bis(azaneylylidene))diphenol) onto a bimodal macro-/meso‑porous polymer monolith, poly(2VP-co-DVB), i.e., poly(2vinylpyridine-co-divinylbenzene), to exploit the possibility of solid-state colorimetric ion sensing. The polymer monolith and sensor materials' topography and morphology are characterized through p-XRD, FE-SEM, XPS, EDX, TEM, SAED, TGA, FT-IR and BET/BJH analysis. The ion-sensing is accompanied by a discernible color transition from vivid yellow to subdued red, with a detection and quantification limit value of 0.20 and 0.51 μg/L for Cu2+, respectively. The sensor affirms a fast detection (≤60 s) for Cu2+, with a linear signal/colorimetric response in the concentration range of 3–100 μg/L. The parameters such as sensor dosage, chromatic receptor dose, pH, temperature, sensing kinetics and reusability are optimized for reliable and reproducible ion-sensing. The solid-state sensor reveals excellent selectivity and sensitivity for Cu2+, and its practical utility with environmental/industrial samples proves its utility as a portable sensor kit for real-time on-site analysis for ultra-trace Cu2+.

Design of Advanced Controllers for Speed Control of DC Motor

In this research work, an open-loop experimental DC motor from National Instruments is investigated for speed control applications in the nuclear industry with higher accuracy. For performance analysis, the DC motor is mathematically modeled, and advanced control techniques are established. The two design approaches are therefore hardware in loop which is an experimental facility and mathematical in nature which is a simulation model. An adequate mathematical model of the DC motor is developed using the first principle. The hardware realization is accomplished by the DC motor experimental setup. Different advanced control algorithms such as PID, SMC, and MPC are developed for both model-based and hardware-based DC motor research studies. Model development, control synthesis, simulation, and analysis are carried out in Simulink and LabVIEW programming environments. Closed loop performance analyses of nonlinear and predictive controllers are evaluated and found much better, smoother, and robust than linear controllers for speed control of DC motors under different conditions.

A Methodology for Advanced Manufacturing Defect Detection through Self-Supervised Learning on X-ray Images

In industrial quality control, especially in the field of manufacturing defect detection, deep learning plays an increasingly critical role. However, the efficacy of these advanced models is often hindered by their need for large-scale, annotated datasets. Moreover, these datasets are mainly based on RGB images, which are very different from X-ray images. Addressing this limitation, our research proposes a methodology that incorporates domain-specific self-supervised pretraining techniques using X-ray imaging to improve defect detection capabilities in manufacturing products. We employ two pretraining approaches, SimSiam and SimMIM, to refine feature extraction from manufacturing images. The pretraining stage is carried out using an industrial dataset of 27,901 unlabeled X-ray images from a manufacturing production line. We analyze the performance of the pretraining against transfer-learning-based methods in a complex defect detection scenario using a Faster R-CNN model. We conduct evaluations on both a proprietary industrial dataset and the publicly available GDXray dataset. The findings reveal that models pretrained with domain-specific X-ray images consistently outperform those initialized with ImageNet weights. Notably, Swin Transformer models show superior results in scenarios rich in labeled data, whereas CNN backbones are more effective in limited-data environments. Moreover, we underscore the enhanced ability of the models pretrained with X-ray images in detecting critical defects, crucial for ensuring safety in industrial settings. Our study offers substantial evidence of the benefits of self-supervised learning in manufacturing defect detection, providing a solid foundation for further research and practical applications in industrial quality control.

Investigating disentangled GAN latent spaces for the removal of fixture-related backgrounds in reconstructed CT images for industrial quality control applications

Investigating disentangled GAN latent spaces for the removal of fixture-related backgrounds in reconstructed CT images for industrial quality control applications

Real-Time Simulation and Experimental Implementation of Luenberger Observer-Based Speed Sensor Fault Detection of BLDC Motors

Abstract Modern control systems’ dependability, safety and efficiency have all been improved by studying fault-tolerant control systems (FTCS). FTCS techniques can typically be active or passive controls. The fault detection and diagnosis (FDD) method is used in this study’s active control branch to identify probable faults that could develop in the speed Hall sensors of brushless DC motors (BLDC). FDD methodologies can be categorised into two types, depending on the available data and the process involved: model-based methods and data-based methods. The proposed approach in this study explores the implementation of the Luenberger observer methodology as part of the model-based approach. The chosen methodology was practically implemented and subjected to experimental evaluation. The proposed observer relies on the residual signal, which displays the difference between the plant’s observed and estimated speed signals and serves as a failure alert for the entire system. Given the increasing demand for BLDC motors in various industrial control applications, including medical fields, automation and robotics, this particular motor was selected as a benchmark to thoroughly evaluate and validate the proposed method. The primary contribution of this paper lies in the real-time application of model-based sensor fault detection methods to BLDC motors. The efficiency of the suggested method is showcased through extensive MATLAB simulations, where the obtained results confirm the successful detection of faults with a high level of responsiveness. As a result, the project was successfully implemented in real-time, and the experimental results exhibited a close correlation with the simulated outcomes. This consistency between simulation and practical implementation validates the accuracy and reliability of the proposed methodology for detecting faults in the BLDC motor speed sensor. The results underscore the heightened reliability and safety attained by promptly and accurately detecting sensor faults during the operation of the motor.

Fast state transfer for updates and live migration of industrial controller runtimes in container orchestration systems

Many industrial processes (e.g., steel, fertilizer, paper production) utilize programmable logic controllers or distributed control systems, which cyclically execute control applications. Dynamic updates of such applications at runtime are desirable for optimizing the production while avoiding production stops. However, such updates are challenging since they require fast switching to an updated instance and executing an internal state transfer within the control cycle times. Existing dynamic update approaches for industrial applications can for example not update controller runtimes or operating systems, since they do not utilize modern container virtualization technologies. Furthermore, existing state transfer methods for container orchestration systems do not support fast cyclic applications. We propose a novel state transfer method for industrial control applications deployed as containerized Kubernetes microservices on server hosts or resource-rich industrial personal computers. The method enables dynamic updates and live migration of applications to others hosts without interrupting the underlying production facilities, which avoids costly downtimes. We have implemented our method as a Kubernetes extension and found that it can transfer large application states (i.e. 100.000 variables) via OPC UA in less than 42 ms across different hosts, which can fit into the available cycle slack time, therefore enabling a wide range of bumpless updates at runtime.

Incremental Stability of Discrete-Time First-Order Projection Elements

In this paper, we present and analyze a class of discrete-time and sampled-data controllers named first-order projection elements (FOPEs). FOPEs form a class of hybrid controllers that have an underlying linear base dynamics, which are combined with a projection operator that ensures that the input-output (I/O) pair is contained in a well-designed sector. The I/O sector-constraint enables performance benefits in various application domains including motion control of high-precision machines. After the introduction of FOPEs, we focus on incremental stability analysis of these hybrid controllers. We show that the properties of the projection operator ensure incremental stability of the open-loop FOPE dynamics in case of an underlying linear dynamics that is exponentially stable. In this case, we compute tight bounds on the incremental gain. Under an appropriate small-gain condition, we show incremental input-to-state stability of the sampled-data interconnection of a FOPE with a continuous-time linear time-invariant plant. For the case of projected integrator dynamics, we provide a counterexample showing that incremental stability can be compromised. We provide additional, necessary and sufficient conditions under which FOPEs with integrator dynamics are incrementally asymptotically stable. Numerical simulations provide a practical example of the established incremental properties for the trajectories of a dynamical system coming from an industrial motion control application.

Design and performance analysis of PCF-based octagonal chemical sensor to detect benzene, ethanol, and water in terahertz spectrum

An octagonal cladding with a hexahedron core PCF sensor is designed to detect chemical compounds at terahertz frequency. The proposed sensor contains circular air holes (CAHs) in the vestibule area. To measure the performance of the sensor effective material loss, confinement loss, and a large frequency area have been used in terms of relative sensitivity (RS). For the design and analysis of our proposed hexahedron PCF sensors, the finite element (FEM) utilizes numerally the optic parameters. The diameter and core size of the proposed air whole were checked to optimize the production tolerance and sensing performance. At the area of 1 THz spectrum Ethanol, Benzene, and Water are used for numerical examination The fiber appears to be a high Relative sensitivity which is 91.25 %, 93.80 %, and 89.95 % respectively for Ethanol (n = 1.354), Benzene (n = 1.366) and Water (n = 1.330). The sensor provides low confinement losses with 5.95 × 10−08 dB/m, 6.25 × 10−08 dB/m, and 5.94 × 10−08 dB/m corresponding for those three chemicals. The geometric construction of the projected sensor can be used in chemical sensing in biomedical, industrial quality control, material research, clinics, gas sensors, micro-optics, and many communication applications in THz technology.

Aberration compensation for enhanced holographic particle characterization.

Holographic particle characterization treats holographic microscopy of colloidal particles as an inverse problem whose solution yields the diameter, refractive index and three-dimensional position of each particle in the field of view, all with exquisite precision. This rich source of information on the composition and dynamics of colloidal dispersions has created new opportunities for fundamental research in soft-matter physics, statistical physics and physical chemistry, and has been adopted for product development, quality assurance and process control in industrial applications. Aberrations introduced by real-world imaging conditions, however, can degrade performance by causing systematic and correlated errors in the estimated parameters. We identify a previously overlooked source of spherical aberration as a significant source of these errors. Modeling aberration-induced distortions with an operator-based formalism identifies a spatially varying phase factor that approximately compensates for spherical aberration in recorded holograms. Measurements on model colloidal dispersions demonstrate that phase-only aberration compensation greatly improves the accuracy of holographic particle characterization without significantly affecting measurement speed for high-throughput applications.

Analyzing the performance of persistent storage for fault-tolerant stateful fog applications

In this paper, we analyze the scalability and performance of a persistent, fault-tolerant storage approach that provides data availability and consistency in a distributed container-based architecture with intended use in industrial control applications. We use simulation to evaluate the performance of this storage system in terms of scalability and failures. As the industrial applications considered have timing constraints, the simulation results show that for certain failure patterns, it is possible to determine whether the storage solution can meet critical deadlines. The presented approach is applicable for evaluating timing constraints also of other container-based critical applications that require persistent storage.

SoLiCiT: Synchronous Listen, Code, and Transmit Protocol for Wireless Control Applications

Industrial control applications are deployed in environments characterized by high signal attenuation and severe multipath fading, which is a potential cause for significant packet losses. This is in conflict with achieving low latency and highly reliable wireless communication, a requirement of closed-loop control applications. We propose SoLiCiT, a wireless networking protocol that employs concurrent transmissions with specifically designed erasure coding to meet these demands without the need for retransmissions. Synchronous transmissions help to realize a simple protocol design while using helper nodes to transmit redundancy allows for lost packets to be recovered within fixed and tight latency bounds. We provide a range of coding solutions that can be tailored to the application and also present a case study with a battery storage management application in which battery cells report their status to a central management unit. Using a realistic ray tracing model of the actual battery storage container, we evaluate the radio environment and show the efficacy of SoLiCiT compared to benchmarking schemes, such as a low-power wireless bus.

Antibacterial mechanism of vanillin against Escherichia coli O157: H7

Vanillin, a plant-derived antimicrobial volatile substance, has potential microbial control applications in the food industry. However, the effect of vanillin on the food-borne pathogen Escherichia coli (E. coli) O157:H7 has not been well studied. This study aims to explore the antibacterial mechanism of vanillin against E. coli O157:H7. The minimum inhibitory concentration (MIC) and antibacterial effect of vanillin were determined by microdilution. Scanning electron microscopy (SEM) was used to observe the damage of vanillin to the cell membrane, while cell membrane potential and the leakage of nucleic acid protein were measured to explore the effect of vanillin on the membrane system. Confocal laser scanning and intracellular adenosine triphosphate (ATP) concentration determination were utilized to investigate the effects of vanillin on the energy, life, and death of E. coli. Finally, transcriptome sequencing was conducted to investigate the gene expression differences induced by vanillin treatment. The results showed that vanillin treatment effectively controlled E. coli O157:H7 with an MIC of 2 mg/mL. After treatment, damage to the membrane system, depolarization of the membrane, and leakage of nucleic acid and protein were observed. Meanwhile, vanillin treatment caused decreased ATP content and cell death. Transcriptome analysis showed that vanillin treatment significantly affected the expression of genes involved in cell membrane formation, tricarboxylic acid (TCA) cycling pathway, and oxidative phosphorylation pathway in E. coli O157:H7. In conclusion, membrane damage and energy metabolism disruption are important mechanisms of vanillin's inhibitory effect on E. coli O157:H7. This study provides new insights into the molecular reaction mechanism of vanillin against E. coli O157:H7, highlighting its potential as an antibacterial substance for preventing E. coli contamination in the food industry.

An adjustable machine learning gradient boosting-based controller for PV applications

Traditional Proportional, Integral, and Derivative (PID) controllers are employed in most industrial control applications because they are simple and easy to implement. However, they are fallible and exhibit poor performance in complicated, non-linear, time-delayed systems, and noisy feedback loops. In photovoltaic systems applications such as maximum power point (MPP) tracking, PI controller performance in low irradiance levels, varying load conditions, and partial shading scenarios is considerably inefficient in tracking the corresponding reference current, which generates MPP and causes considerable steady-state error. Machine learning can be used with selective input/output data from the PI controller to build a model that has the ability to adjust in case of steady-state error. Thus, an adjustable Machine Learning Gradient Boosting-based (MLGB) controller with a PV module and a DC-DC boost converter is proposed in this work. To create the raw dataset, a PI controller was simulated, and the input/output signals were recorded. Data preprocessing, utilizing feature engineering and Shapley Additive Explanations (SHAP) values, was used to explain the dynamic behavior of each feature, the inter-feature dependability, and their significance with reference to the model output. Hyperparameters were tuned with cross-validation while the model was being created using the CatBoost method. To assess the adjustment of each feature to minimize the error, particularly for lower irradiance values, varying load conditions, and partial shading, a regression model was built. Using the MATLAB Simulink Environment, a complete system controlled by both the PI and the MLGB controller was developed and tested. A random irradiance level fluctuation profile, along with varying load and partial shading scenarios, was used to compare three controllers, namely the PI, conventional MLGB, and adjusted MLGB controllers, to see how they respond to rapidly changing environmental conditions. It has been proven through a thorough simulation analysis that the novel adjustable MLGB controller exhibits good tracking performance to follow the DC-DC boost converter's inductor current. When compared to the traditional PI controller, the new controller exhibits better results in terms of steady-state behavior and transient responsiveness in general, with a much lower mean (3.059E-03), median (1.908E-03), and RMS value (2.168E-02) of the signal error under various conditions.