Process Monitoring Research Articles

Development of a CNN-Based System for Real-Time Process Monitoring and Anomaly Detection in CNC Welding Robots

Development of a CNN-Based System for Real-Time Process Monitoring and Anomaly Detection in CNC Welding Robots

Empowering Capacitive Devices: Harnessing Transfer Learning for Enhanced Data-Driven Optimization.

Developing data-driven models has found successful applications in engineering tasks, such as material design, process modeling, and process monitoring. In capacitive devices like deionization and supercapacitors, there exists potential for applying this data-driven machine learning (ML) model in optimizing its potential use in energy-efficient separations or energy generation. However, these models are faced with limited datasets, and even in large quantities, the datasets are incomplete, limiting their potential use for successful data-driven modeling. Here, the success of transfer learning in resolving the challenges with limited datasets was exploited. A two-step data-driven ML modeling framework named ImputeNet involving training with ML-imputed datasets and then with clean datasets was explored. Through data imputation and transfer learning, it is possible to develop a data-driven model with acceptable metrics mirroring experimental measurements. By using the model, optimization studies using the genetic algorithm were implemented to analyze the solution under the Pareto optimality. This early insight can be used in the initial stage of experimental measurements to rapidly identify experimental conditions worthy of further investigation. Moreover, we expect that the insights from these results will drive accurate predictive modeling in other fields including healthcare, genomic data analysis, and environmental monitoring with incomplete datasets.

Advanced Monitoring of Manufacturing Process through Video Analytics.

The digitization of production systems has revolutionized industrial monitoring. Analyzing real-time bottom-up data enables the dynamic monitoring of industrial processes. Data are collected in various types, like video frames and time signals. This article focuses on leveraging images from a vision system to monitor the manufacturing process on a computer numerical control (CNC) lathe machine. We propose a method for designing and integrating these video modules on the edge of a production line. This approach detects the presence of raw parts, measures process parameters, assesses tool status, and checks roughness in real time using image processing techniques. The efficiency is evaluated by checking the deployment, the accuracy, the responsiveness, and the limitations. Finally, a perspective is offered to use the metadata off the edge in a more complex artificial-intelligence (AI) method for predictive maintenance.

DECISION-MAKING SUPPORT TECHNOLOGIES IN THE FORMATION OF QUALITY MANAGEMENT SYSTEMS AT VEHICLE REPAIR ENTERPRISES

In recent years, the strategy of creating a safely functioning and efficient transport complex of Ukraine integrated into the world transport network has been fixed. Therefore, the creation of a progressive system of product quality management in the conditions of car repair enterprises is considered an urgent scientific and technical task. The quality management system is a management subsystem of an industrial enterprise. Modern production systems are based on TQM principles and widely use ISO 9001 certification. At the same time, certification is based on independent third-party audits (a conformity assessment organization). The wide spread of the standard is based on the fact that it contains generalized requirements (principles) for the organization of product quality control, which can be used at enterprises that differ significantly both in terms of specialization and the scale of production (depots or car repair plants). But this is the «bottleneck» of the standard – the lack of consideration of specialized production conditions makes it impossible to compensate for critical factors affecting the final quality of products. Currently, the accepted approaches to quality assessment do not fully take into account the factors that affect the final quality of products. In order to put all market participants - railway operators, manufacturers, rolling stock repairers and suppliers in mutually beneficial conditions, a single International Railway Industry Standard ISO / TS 22163 International Railway Industry Standart (IRIS) was developed.For the effective use of tools for ensuring the quality of rail car repair, it is considered necessary to use modern management decision support systems. They will allow real-time monitoring of production processes. It is possible to use the method of fuzzy sets to improve the quality management systems of wagon repairs based on the development of separate control algorithms with decision tables. In order to meet the requirements of international quality standards, it is necessary to significantly modernize management approaches based on the creation of progressive decision support systems.

Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey

Recently, extensive research has been conducted to explore the utilization of machine learning (ML) algorithms in various direct-detected and (self)-coherent short-reach communication applications. These applications encompass a wide range of tasks, including bandwidth request prediction, signal quality monitoring, fault detection, traffic prediction, and digital signal processing (DSP)-based equalization. As a versatile approach, ML demonstrates the ability to address stochastic phenomena in optical systems networks where deterministic methods may fall short. However, when it comes to DSP equalization algorithms such as feed-forward/decision-feedback equalizers (FFEs/DFEs) and Volterra-based nonlinear equalizers, their performance improvements are often marginal, and their complexity is prohibitively high, especially in cost-sensitive short-reach communications scenarios such as passive optical networks (PONs). Time-series ML models offer distinct advantages over frequency-domain models in specific contexts. They excel in capturing temporal dependencies, handling irregular or nonlinear patterns effectively, and accommodating variable time intervals. Within this survey, we outline the application of ML techniques in short-reach communications, specifically emphasizing their utilization in high-bandwidth demanding PONs. We introduce a novel taxonomy for time-series methods employed in ML signal processing, providing a structured classification framework. Our taxonomy categorizes current time-series methods into four distinct groups: traditional methods, Fourier convolution-based methods, transformer-based models, and time-series convolutional networks. Finally, we highlight prospective research directions within this rapidly evolving field and outline specific solutions to mitigate the complexity associated with hardware implementations. We aim to pave the way for more practical and efficient deployment of ML approaches in short-reach optical communication systems by addressing complexity concerns.

Mussel‐inspired self‐assembly of silver nanoclusters into multifunctional silver aerogels for enhanced catalytic and bactericidal applications

AbstractSilver nanoclusters (AgNCs) have shown broad application prospects in catalysis, sensing, and biological fields. However, the limited stability of AgNCs has become the main challenge restricting their practical application in complex environments. Herein, a mussel‐inspired, dopamine‐assisted self‐assembly approach is reported to fabricate 3D AgNC aerogels (PDA/AgNCs), which possess significantly enhanced structural stability and synergistic functional properties. The prepared AgNC aerogels display a hierarchical network structure with an ultrafine ligament size of 10.3 ± 1.2 nm and a high specific surface area of 50.7 m2 g−1. The gelation mechanism is elucidated by in‐depth characterization and time‐lapse monitoring of the gelation process vis spectroscopic and microscopic approaches. Owing to the distinct features of aerogels and the synergistic effect of AgNCs and PDA, the fabricated aerogels can not only efficiently decolorize dyes with a faster kinetic than individual AgNCs, but also exhibit remarkable broad‐spectrum antimicrobial activity. Consequently, a conceptual water‐treatment device is established by depositing PDA/AgNC aerogels on the cotton substrate, which shows good performance in both catalytic dye degradation and bacterial killing in the flowing system. This mussel‐inspired self‐assembly strategy has great potential in developing robust AgNC‐based functional materials, which also provides a new guideline for designing sophisticated materials with integrated functions and synergistic properties.

Revolutionizing food science with mass spectrometry imaging: A comprehensive review of applications and challenges.

Food science encounters increasing complexity and challenges, necessitating more efficient, accurate, and sensitive analytical techniques. Mass spectrometry imaging (MSI) emerges as a revolutionary tool, offering more molecular-level insights. This review delves into MSI's applications and challenges in food science. It introduces MSI principles and instruments such as matrix-assisted laser desorption/ionization, desorption electrospray ionization, secondary ion mass spectrometry, and laser ablation inductively coupled plasma mass spectrometry, highlighting their application in chemical composition analysis, variety identification, authenticity assessment, endogenous substance, exogenous contaminant and residue analysis, quality control, and process monitoring in food processing and food storage. Despite its potential, MSI faces hurdles such as the complexity and cost of instrumentation, complexity in sample preparation, limited analytical capabilities, and lack of standardization of MSI for food samples. While MSI has a wide range of applications in food analysis and can provide more comprehensive and accurate analytical results, challenges persist, demanding further research and solutions. The future development directions include miniaturization of imaging devices, high-resolution and high-speed MSI, multiomics and multimodal data fusion, as well as the application of data analysis and artificial intelligence. These findings and conclusions provide valuable references and insights for the field of food science and offer theoretical and methodological support for further research and practice in food science.

Biocalorimetry-aided monitoring of fungal pretreatment of lignocellulosic agricultural residues

The present study aimed to investigate whether and how non-invasive biocalorimetric measurements could serve for process monitoring of fungal pretreatment during solid-state fermentation (SSF) of lignocellulosic agricultural residues such as wheat straw. Seven filamentous fungi representing different lignocellulose decay types were employed. Water-soluble sugars being immediately available after fungal pretreatment and those becoming water-extractable after enzymatic digestion of pretreated wheat straw with hydrolysing (hemi)cellulases were considered to constitute the total bioaccessible sugar fraction. The latter was used to indicate the success of pretreatments and linked to corresponding species-specific metabolic heat yield coefficients (YQ/X) derived from metabolic heat flux measurements during fungal wheat straw colonisation. An YQ/X range of about 120 to 140 kJ/g was seemingly optimal for pretreatment upon consideration of all investigated fungi and application of a non-linear Gaussian fitting model. Upon exclusion from analysis of the brown-rot basidiomycete Gloeophyllum trabeum, which differs from all other here investigated fungi in employing extracellular Fenton chemistry for lignocellulose decomposition, a linear relationship where amounts of total bioaccessible sugars were suggested to increase with increasing YQ/X values was obtained. It remains to be elucidated whether an YQ/X range being optimal for fungal pretreatment could firmly be established, or if the sugar accessibility for post-treatment generally increases with increasing YQ/X values as long as “conventional” enzymatic, i.e. (hemi)cellulase-based, lignocellulose decomposition mechanisms are operative. In any case, metabolic heat measurement–derived parameters such as YQ/X values may become very valuable tools supporting the assessment of the suitability of different fungal species for pretreatment of lignocellulosic substrates.Key points• Biocalorimetry was used to monitor wheat straw pretreatment with seven filamentous fungi.• Metabolic heat yield coefficients (YQ/X) seem to indicate pretreatment success.• YQ/X values may support the selection of suitable fungal strains for pretreatment.

Eight ways to get a grip on validity as a social imperative

Validity as a social imperative foregrounds the social consequences of assessment and highlights the importance of building quality into the assessment development and monitoring processes. Validity as a social imperative is informed by current assessment trends such as programmatic-, longitudinal-, and rater-based assessment, and is one of the conceptualizations of validity currently at play in the Health Professions Education (HPE) literature. This Black Ice is intended to help readers to get a grip on how to embed principles of validity as a social imperative in the development and quality monitoring of an assessment. This piece draws on a program of work investigating validity as a social imperative, key HPE literature, and data generated through stakeholder interviews. We describe eight ways to implement validation practices that align with validity as a social imperative.

Youth migration during war: Triggers of positive aspirations and preservation of human resources in Ukraine

Russia’s full-scale invasion of Ukraine has become an existential challenge and a trigger of the migration crisis. The study aims to identify migration intentions of youth and factors for the preservation of the young population in Ukraine (a case study of university youth). The study conducts a sociological survey using a Google Forms questionnaire. The sample was formed by the method of three-stage selection: (1) quotas for the share of undergraduate and graduate students; (2) the higher education institutions in Ukraine were selected by the criterion of the number of students and specialties; and (3) field of knowledge. The sample size is calculated based on the resampling method and included 2,200 people from all regions in Ukraine (except Donetsk, Luhansk, Zaporizhzhia, and Dnipropetrovsk oblasts). The study reveals that 10% of students plan to go abroad in the near future before graduation, 30% plan to stay in Ukraine only if the socio-economic situation improves after the end of the war, and 28.3% plan to migrate after graduation. The reasons for positive migration aspirations among students are socio-economic and security issues (14.9% can find a job abroad in the short term, 11.1% see the lack of further prospects in Ukraine even after the end of the war). Monitoring of youth migration processes across two vectors – current volumes and potential aspirations – can serve as an information and analytical basis for the development of a new vision of the country’s migration security strategy to preserve human resources in Ukraine.

Exploring agricultural landscape change from the second half of the twentieth century onwards: combining aerial imagery with farmer perspectives

ContextAnthropogenic landscape change is an important driver shaping our environment. Historical landscape analysis contributes to the monitoring and understanding of these change processes. Such analyses are often focused on specific spatial scales and single research methods, thus covering only limited aspects of landscape change.ObjectivesHere, we aim to assess the potential of combining the analysis of historical aerial imagery and local stakeholder interviews for landscape change studies using a standardized mapping and interviewing approach.MethodsWe compared six agricultural landscapes across Europe and mapped land-cover using historical aerial imagery (starting between 1930 and 1980, depending on data availability, until recent years) with an object-based image analysis and random forest classification. For local perspectives of landscape change, we conducted oral history interviews (OHIs) with (almost) retired farmers. Comparing recorded landscape changes from both approaches provided insight into advantages of combining these two methods.ResultsObject-based analysis enabled the identification of high-resolution land-cover dynamics, with scale enlargement and cropland/grassland expansion being the most commonly recurring trends across European landscapes. Perceived landscape changes identified in the OHIs included changes in farm management, landscape structure, and infrastructure. Farmers also reported drivers and personal values associated with landscape change. Combining the two historical landscape analysis tools resulted in a qualitative and quantitative understanding of changes in land-cover, land use, and land management.ConclusionsComparing physical land-cover change with local farmer perspectives is key to a comprehensive understanding of landscape change. There are different ways the two methods can be combined, leading to different venues for science and policy making.

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine.

Identifying and diagnosing faults is a critical task in process industries to maintain effective monitoring of process and plant safety. Minimizing process downtime is critical for enhancing the quality of the product and minimizing production costs. Real-time categorization of issues across several levels is essential for the monitoring of processes. However, there are still notable obstacles, that must be addressed, such as the existence of robust correlations, the complexity of the data, and the lack of linearity. This study introduces a novel fault identification technique in batch reactor experimental trials that employs multikernel support vector machines (SVMs) to categorize internal and external issues, specifically reactor temperature, coolant temperature, and jacket temperature. The data set was obtained from empirical research. The classification has been conducted using a multikernel SVM. This article identified that the nonlinear classifier using the radial bias function results in an accuracy that is at least 22.08% superior to other methods.

Collagen structures of demineralized bone paper direct mineral metabolism.

Bone is a dynamic mineralized tissue that undergoes continuous turnover throughout life. While the general mechanism of bone mineral metabolism is documented, the role of underlying collagen structures in regulating osteoblastic mineral deposition and osteoclastic mineral resorption remains an active research area, partly due to the lack of biomaterial platforms supporting accurate and analytical investigation. The recently introduced osteoid-inspired demineralized bone paper (DBP), prepared by 20-μm thin sectioning of demineralized bovine compact bone, holds promise in addressing this challenge as it preserves the intrinsic bony collagen structure and retains semi-transparency. Here, we report on the impact of collagen structures on modulating osteoblast and osteoclast-driven bone mineral metabolism using vertical and transversal DBPs that exhibit a uniaxially aligned and a concentric ring collagen structure, respectively. Translucent DBP reveals these collagen structures and facilitates longitudinal tracking of mineral deposition and resorption under brightfield microscopy for at least 3wk. Genetically labeled primary osteogenic cells allow fluorescent monitoring of these cellular processes. Osteoblasts adhere and proliferate following the underlying collagen structures of DBPs. Osteoblastic mineral deposition is significantly higher in vertical DBP than in transversal DBP. Spatiotemporal analysis reveals notably more osteoblast adhesion and faster mineral deposition in vascular regions than in bone regions. Subsequent osteoclastic resorption follows these mineralized collagen structures, directing distinct trench and pit-type resorption patterns. In vertical DBP, trench-type resorption occurs at an 80% frequency, whereas transversal DBP shows 35% trench-type and 65% pit-type resorption. Our studies substantiate the importance of collagen structures in regulating mineral metabolism by osteogenic cells. DBP is expected to serve as an enabling biomaterial platform for studying various aspects of cellular and extracellular bone remodeling biology.

A research study of lightweight state perception algorithm based on improved YOLOv5s‐Tiny for fully mechanized top‐coal caving mining

AbstractReal‐time monitoring of the coal caving process in fully mechanized mining is crucial for achieving intelligent and efficient top‐coal caving. While the coal gangue identification method, employing vision and deep learning, has advanced in the realm of intelligent monitoring, it exhibits a dependency on high‐performance hardware. This reliance poses challenges for deploying identification equipment on mobile terminals, hindering the widespread application of this method. To address the issues above, the paper presents a lightweight algorithm, utilizing You Only Look Once version 5s (YOLOv5s), utilizing YOLOv5s for the real‐time perception of the top‐coal caving state in fully mechanized caving mining. We replace the backbone network of YOLOv5s with the ShuffleNetv2 structure that is more suitable for lightweight deployment, and add the Simple Attention Mechanism attention mechanism to the network structure to enhance the model's receptive field and feature expression ability, and reduce the impact of falling debris on the detection results. A dynamic experimental platform for top‐coal caving in fully mechanized caving mining for thick coal seams is set up, and preprocessing operations such as brightness, sharpening, and denoising are performed on the image data sets collected by high‐speed industrial cameras. Research results show that compared with the traditional YOLOv5s, the improved model's P, mAP, F1 score, and other indicators have increased by 3.4%, 2.1%, and 1.1%, respectively, the model size is 70% of the original, and the detection frames per second value has increased by 48.1%. The lightweight algorithm stabilizes the accuracy of coal gangue identification dramatically in real time. It dramatically reduces the computing pressure on the mobile terminal, providing basic theory and practice for real‐time monitoring of fully mechanized coal caving mining.

Rule based complex event processing for an air quality monitoring system in smart city

Smart city development is accelerating, aiming for systematic urban living, but challenges like air quality and traffic management need addressing for sustainability in cities. The World Health Organization (WHO) links air pollution to severe respiratory diseases, emphasizing the need for real-time air quality monitoring and timely decisions based on varying pollutant concentrations (NH3, O3, SO2, NO2) across locations. This research work proposes a framework combining Siddhi Complex Event Processing (CEP) and SPARQL queries for air quality monitoring. Dataset collected from Central Pollution Control Board (CPCB) India, is preprocessed, passed through Apache Kafka, and transformed into a knowledge graph. The RDF data is integrated with a knowledge graph via Apache Jena to enhance CEP performance. Decision trees generate rules and enrich CEP engines with CPCB parameters to identify complex patterns. SPARQL queries on real-time RDF data categorize air quality conditions for all stakeholders. The model's effectiveness is validated by deploying RDF chunks to the CEP engine and evaluating performance through various query execution times. Our model efficiently processed numerous air pollutant events (200, 400, 600 etc.), filtering out about 80% and demonstrating robust event processing capabilities, which enhances air quality management and supports sustainable urban ecosystems and societies.

Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process.

As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.

KT-Biologics I (KTB1): A dynamic simulation model for continuous biologics manufacturing

The pharmaceutical industry's shift towards biological therapeutics has led to a transition from conventional batch production to continuous manufacturing. This change highlights the crucial need for effective process monitoring and control strategies to ensure consistent product quality and stability. Open-source benchmark simulation models have become essential tools for refining these processes, offering a platform for testing research hypotheses. This study uses the production of Lovastatin as a case study for continuous biopharmaceutical production. A comprehensive dynamic model covering upstream and downstream components provides an integrated perspective of the production process. The study introduces a basic control system emphasizing realistic sensor and actuator integration to enhance simulation accuracy. It assesses the benchmark through open-loop and closed-loop simulations, offering an in-depth analysis of the KTB1 model's dynamic response and functionality. KTB1 represents a model-driven decision support tool that enables the evaluation of monitoring strategies, process design, process optimization, and control for biomanufacturing.

A Cyber-Physical Production System for the Integrated Operation and Monitoring of a Continuous Manufacturing Train for the Production of Monoclonal Antibodies.

The continuous manufacturing of biologics offers significant advantages in terms of reducing manufacturing costs and increasing capacity, but it is not yet widely implemented by the industry due to major challenges in the automation, scheduling, process monitoring, continued process verification, and real-time control of multiple interconnected processing steps, which must be tightly controlled to produce a safe and efficacious product. The process produces a large amount of data from different sensors, analytical instruments, and offline analyses, requiring organization, storage, and analyses for process monitoring and control without compromising accuracy. We present a case study of a cyber-physical production system (CPPS) for the continuous manufacturing of mAbs that provides an automation infrastructure for data collection and storage in a data historian, along with data management tools that enable real-time analysis of the ongoing process using multivariate algorithms. The CPPS also facilitates process control and provides support in handling deviations at the process level by allowing the continuous train to re-adjust itself via a series of interconnected surge tanks and by recommending corrective actions to the operator. Successful steady-state operation is demonstrated for 55 h with end-to-end process automation and data collection via a range of in-line and at-line sensors. Following this, a series of deviations in the downstream unit operations, including affinity capture chromatography, cation exchange chromatography, and ultrafiltration, are monitored and tracked using multivariate approaches and in-process controls. The system is in line with Industry 4.0 and smart manufacturing concepts and is the first end-to-end CPPS for the continuous manufacturing of mAbs.

Real-time determination of combustion degree by laser-induced breakdown spectroscopy

The combustion of fuels in industries is one of the sources of greenhouse gases, posing a considerable threat to human health and the natural environment. The monitoring of the combustion process and the measurement of combustion degrees, therefore, are crucial and significant. In this work, an experimental system was developed based on Laser-induced breakdown spectroscopy (LIBS). Charcoals with different combustion degrees were taken as samples, and in the LIBS spectra of charcoals, the characteristic line of C and the molecular bands of CN and CaO were observed. Moreover, a univariate calibration model based on the exponential fitting curve was constructed to predict the combustion degree according to the variation of the C I line intensity under different combustion degrees. Furthermore, different algorithms including support vector machine (SVM), partial least squares (PLS), random forest (RF), and convolutional neural network (CNN) were applied to establish the multivariate calibration model, and the prediction performance was compared according to the 10-fold cross-validation. Particle swarm optimization (PSO) was used to optimize hyperparameters of the selected SVM-based multivariate calibration model. Principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to extract spectral features and reduce dimensions for the improvement of the model's performance, with the final root mean square error of prediction (RMSEP) and mean relative error (MRE) of the LDA-PSO-SVM calibration model of 0.035 and 3.877%, respectively. Finally, c. The results prove feasible to realize the real-time determination of combustion degree to control carbon emissions and monitor carbon concentration through the LIBS-based method with machine learning, which also provides new ideas for the application of LIBS in the atmospheric field.

A propagation path-based interpretable neural network model for fault detection and diagnosis in chemical process systems

Process monitoring through automated fault detection and diagnosis (FDD) plays a crucial role in maintaining a productive and reliable chemical process system. Developments in AI and machine learning have boosted FDD model performances especially with deep learning methods. However, these neural network models are considered black-boxes where the reasoning behind a diagnosis is unclear, preventing industrial adoption. Therefore, in this study, an interpretable neural network model is proposed for FDD in chemical processes. This framework detects and diagnoses faults based on the propagation paths of different faults which are embedded into the architecture through graph convolutional networks. A mechanism for interpreting the node activations which represent process variables is developed for decision verification. The proposed method is evaluated on the benchmark Tennessee Eastman Process where it achieves a 93.56% accuracy on selected faults.