Process Monitoring Research Articles

Bridge Monitoring Using Smartphones Installed on Driving Vehicles: Oriented to Crowdsourcing Model

The acquisition of bridge frequency by using the ubiquitous ordinary passenger vehicles and smartphones in cities has attracted an increasing amount of attention. This study proposes a bridge monitoring method and develops a vehicle–bridge crowdsourcing monitoring (VBCM) platform for public participation. It takes smartphones carried by vehicles as monitoring terminals for a long-term bridge monitoring task. To validate the feasibility of available smartphone brands as a signal collector, a small shake table test is carried out and the processing scheme for the smartphone sampling data is investigated. A smartphone sensor frequency gain technique is proposed to satisfy the fusion of multisensor data in different smartphones. Furthermore, a magnetic target identification technique is proposed to pick up and extract the bridge response signal segment corresponding to the vehicle’s entering and leaving, as well as to eliminate redundant data. To this end, the network of multiple smartphones is synchronized. For the sake of applicability and feasibility, a series of scaled experiments are conducted in the laboratory considering an adapted toy car driving over a simply supported steel bridge. The results demonstrated the practicality of the proposed methodology. The combination of easily accessible smartphones oriented to a crowdsourcing model and a driving vehicle lowers the threshold for the bridge monitoring process, which also pinpoints a potential for future structural health monitoring development.

The Project Performance Evaluation of PLBN Project Using Earned Value Management Method

Process of monitoring and controlling projects requires data evaluation activities in the order to findout whether the project is in accordance with the plan to avoid an incompatibility between theplanning and the actual time. The failure of construction project almost reached the figure of 20% -40% which occurred in the project monitoring and controlling process. The purpose of this researchis analysis project performance with earned value management method at PLBN Project. Thisresearch was carried out by several stages: data collection, data processing and analysis. From theresults of the performance index analysis show that the project’s performance on the constructionproject is still not good. At week 16 the CPI and SPI values are 0.94 and 0.85. Both values can beinterpreted that the costs incurred are greater than planning and the implementation time is longerthan planning. The result of EAC is Rp. 2.423.695.294 with an estimated completion time is 21weeks which indicates that there is an additional 3 weeks of completion.

Endothelium-targeted NF-κB siRNA nanogel for magnetic resonance imaging and visualized-anti-inflammation treatment of atherosclerosis

Atherosclerosis-induced lethal cardiovascular disease remains a severe healthcare threat due to the limited drug efficiency and untimely prediction of high-risk events caused by inadequate target specificity of medications, incapable recognition of insensitive patients, and variable morphology of vulnerable plaques. Therefore, it is necessary to develop efficient strategies to improve the diagnosis accuracy and achieve visualized treatment of atherosclerosis. Herein, we establish an inflamed endothelium-targeted three-in-one nucleic acid nanogel system that can reverse the inflammatory state of endothelial cells (ECs) in plaques and simultaneously achieve real-time monitoring of the therapy process for efficient atherosclerosis diagnosis and treatment. For this purpose, contrast agent (Gd-DOTA) and VCAM-1-targeted peptide (VP) are first covalently conjugated onto DNA strands by click reaction respectively, which could self-assemble into Y-shaped structures (Gd-Y1 and VP-Y2 motifs) with magnetic resonance (MR) imaging and endothelium targeting capacities. Thereafter, NF-κB subunit p65-targeting siRNA (siNF-κB) is crosslinked with Gd-Y1 and VP-Y2 motifs to construct the endothelium-targeting nanogel platform. With contrast agents inside, the nanogel enables MR-based diagnosis and visualized therapy of atherosclerosis, providing accurate prognostic analysis and indications for treatment results, which ensures timely disclosure of insensitive individuals and avoids acute lethal events. By delivering siNF-κB to inflammatory endothelium, the nanogel significantly regresses plaques in both the aorta and carotid artery with reduced inflammation cytokines, collagens, macrophages, and apoptotic cells, providing a potential anti-inflammation strategy to treat atherosclerosis and avoid acute cardiovascular disease.

First step toward designing effective real-time control systems in laser directed energy deposition

In-process monitoring and control are essential for quality assurance and consistency of laser-based directed energy deposition processes. Detection of irregularities during deposition in terms of defects or flaws is based on in situ monitoring of output process parameters such as temperature, melt-pool geometry, or deposition height. The real-time feedback of these output parameters allows the development of control strategies for real-time adjustment of input process parameters, such as laser power or scanning speed, to correct detected deviations from the desired output process parameters. Therefore, criteria such as sensitivity, stability, correlation, trends, and interactions of the input-output process parameters have a direct impact on controller design, establishing, for example, control limits or tolerance ranges of the output parameters. This paper focuses on the study of the characteristics of output process parameters to input process parameters. This research involves analyzing and comparing the deposition of single tracks under various input process parameters, including laser power and scanning speed. Melt-pool geometry and temperature are estimated from a visual camera and a hyperspectral line camera, whereas the final deposition geometry is obtained from a laser triangulation scanner. The results show the linearity between input and output process parameters, the steadiness of the output process parameters, the relation between melt-pool and final deposition, and offer insights to design effective in-process control systems.

Sustainable and Advanced LED-Implanted Microfluidic Photoreactor with Coupled Optical Fiber Enabling Efficient Photocatalytic Synthesis Beside Online Reaction Progress Monitoring.

The conventional methods for the photoinduced synthesis of benzimidazoles may need further improvement regarding a safer and energy-efficient synthesis platform with in-process monitoring for maximum yield within a much reduced reaction time. This work describes the use of a novel optofluidic Lab-on-a-Chip technology for safer, energy-efficient, and expedited synthesis of benzimidazole derivatives in excellent yields along with real-time quantitative measurement during product formation. This innovative method involves synthesis within LED-embedded microfluidic reactors, allowing for rapid synthesis of benzimidazole derivatives via photoinduced condensation cyclization reactions of aryl aldehydes and o-phenylenediamines using a Rose Bengal/fluorescein photocatalyst. It results in good to excellent yields (85-94%) in a notably shorter period of time (10 min) as compared to that for the batch protocol reaction (2-3 h). The incorporation of a reaction-monitoring microfluidic device precisely connected to the microfluidic reactor (microreactor) unit successfully avoids interference from light sources, ensuring consistent UV-vis spectroscopic observations of the produced benzimidazole derivatives. This LED-embedded microfluidic device's capability of photoinduced synthesis, along with real-time spectroscopic analysis, represents a promising breakthrough in organic synthesis. The proposed approach minimizes the potential for accidents, prevents waste of chemicals, maximizes atom economy, and designs an energy-efficient synthesis route, along with real-time in-process monitoring.

Causal Learning: Monitoring Business Processes Based on Causal Structures.

Conventional methods for process monitoring often fail to capture the causal relationships that drive outcomes, making hard to distinguish causal anomalies from mere correlations in activity flows. Hence, there is a need for approaches that allow causal interpretation of atypical scenarios (anomalies), allowing to identify the influence of operational variables on these anomalies. This article introduces (CaProM), an innovative technique based on causality techniques, applied during the planning phase in business process environments. The technique combines two causal perspectives: anomaly attribution and distribution change attribution. It has three stages: (1) process events are collected and recorded, identifying flow instances; (2) causal learning of process activities, building a directed acyclic graphs (DAGs) represent dependencies among variables; and (3) use of DAGs to monitor the process, detecting anomalies and critical nodes. The technique was validated with a industry dataset from the banking sector, comprising 562 activity flow plans. The study monitored causal structures during the planning and execution stages, and allowed to identify the main factor behind a major deviation from planned values. This work contributes to business process monitoring by introducing a causal approach that enhances both the interpretability and explainability of anomalies. The technique allows to understand which specific variables have caused an atypical scenario, providing a clear view of the causal relationships within processes and ensuring greater accuracy in decision-making. This causal analysis employs cross-sectional data, avoiding the need to average multiple time instances and reducing potential biases, and unlike time series methods, it preserves the relationships among variables.

MONITORING ACTIVITY IN THE RISK-ORIENTED INTERNAL AUDIT: A MODEL PROPOSAL

The purpose of this article is to make a proposal for process development for monitoring activities in a way that includes experiences gained from audits conducted in order to achieve the goal of risk-oriented internal audit function in institutions and to contribute a guiding study on the subject to the literature. In this study, first of all, the risk-oriented internal audit approach and monitoring process are explained with the document analysis method, and then a model for the monitoring process is presented and conclusions and recommendations are made. By using the document analysis method and the features of the risk-oriented internal audit approach, an original application process was compiled to test the risk and control processes for the internal audit activities required for an effectively functioning monitoring system. It is suggested that risk- oriented internal audit will be an important tool in increasing the added value for institutions with the monitoring system designed for risk- oriented internal audit activity, which examines the processes and offers suggestions for areas of development in order to ensure that all financial and non-financial resources are used effectively, economically and efficiently and to add value to the work of the organization.

Design of a Web-Based Monitoring and Evaluation Application for Mentoring Using the Rapid Application Development Method at The Faculty of Computer Science UNIKA Santo Thomas Medan

The academic and thesis supervision processes at the Faculty of Computer Science, UNIKA Santo Thomas Medan, currently face various challenges, including a lack of clear structure in scheduling and difficulties in monitoring student progress. To address these issues, this research aims to develop a web-based application that supports monitoring and evaluation of the supervision process. The application is designed using the Rapid Application Development (RAD) method, which facilitates flexible and responsive development tailored to user needs. With features such as supervision scheduling, student progress tracking, and automatic evaluation, this application is expected to enhance the effectiveness of supervision, facilitate communication between lecturers and students, and support more structured academic management within the faculty. The implementation of this application has successfully improved the efficiency of the supervision process and ensured that students receive more structured guidance.

Electroencephalographic depression after abruptly increasing partial pressure of end-tidal carbon dioxide: a case series

BackgroundProlonged electroencephalographic depression during surgery is associated with poor outcomes for patients. However, the published literature on electroencephalographic depression caused by a sudden increase in the partial pressure of end-tidal carbon dioxide (PETCO2) is lacking.Case presentationWe report four patients who were scheduled for laparoscopic liver surgery under general anesthesia. During the process of EEG monitoring with Sedline, four patients experienced electroencephalographic depression closely after a sudden increase in PETCO2. The four patients showed that electroencephalographic depression mainly manifested as a slow in EEG frequency, a reduction in the amplitude and power of EEG, and a decrease in spectral edge frequency. Patient state index was elevated in three cases.ConclusionsTo summarize, our patients showed EEG depression when PETCO2 suddenly increased, which suggests that clinical doctors should be alert to electroencephalographic depression when the PETCO2 abruptly increases. EEG monitoring devices should be applied in patients with possible hypercapnia. Anesthesiologists must comprehensively interpret the raw EEG, spectral edge frequency, and density spectral array data, in addition to patient sedation index values.

The Impact of Risk Management on Project Success: A Field Study at Humanitarian Organizations in Yemen

The study aimed to determine the impact of risk management (risk identification, risk analysis, risk response and risk monitoring and control) on project success (scope, schedule, cost and quality) at humanitarian organizations operating in Yemen (HOY). The study followed the analytical descriptive approach to meet its objectives. It utilized the simple random technique to select 15 organizations representing 127 HOY and the disproportional stratified random sampling technique to target the respondents. Data was collected through a printed questionnaire from 185 respondents who have roles in managing projects at the targeted organizations. The findings showed that risk management has a significant positive impact on project success. The findings also revealed that among 4 dimensions of risk management process, risk identification and risk monitoring and control were found as the most significant influencers that impact project success at HOY. The study recommends HOY to pay more attention to project risk management to attain higher project success level that ultimately contributes to the performance improvement and sustainability of their organizations. The study also recommends conducting more studies for further confirmations to the hypothesis of the study through testing the study’s variables at industries other than HOY such as banking industries and telecommunications companies.

Integrative approach to fluorescence-based oxygen sensing in polymer optical fibers with surface plasmon resonance

This study demonstrates a technique to enhance oxygen (O2) concentration measurement by combining surface plasmon resonance (SPR) and fluorescence-based sensor on polymer optical fiber (POF). The SPR was generated using silver (Ag) and the fluorescence was generated by organic dye. Three fluorescence dyes were used, Tris (4, 7-diphenyl-1, 10-phenanthroline) ruthenium (II) dichloride ([Ru(dpp)3]2+), platinum octaethylporphyrin (PtOEP) and 5,10,15,20-tetrakis (pentafluorophenyl) 21 H,23H-porphine palladium (II) (PdTFPP). The sensor was tested in a gas chamber with different concentration level of O2. Sensitivity values for the fluorescence dyes for [Ru(dpp)₃]²⁺, PtOEP, and PdTFPP were 0.0099 a.u/%, 0.0343 a.u/% and 0.05 a.u/% respectively. When SPR was implemented, the sensitivity values for Ag/[Ru(dpp)₃]²⁺, Ag/PtOEP, and Ag/PdTFPP were 0.0589 nm/%, 0.0345 nm/%, and 0.118 nm/% respectively. PdTFPP and Ag/PdTFPP exhibit highest sensitivity in each experiment. Therefore, the further analysis for the sensing performance of PdTFPP and Ag/PdTFPP were held. As a result, Ag/PdTFPP exhibits lower limit of detection (LOD), and limit of quantification (LOQ) with 3.054 % and 9.254 %, respectively, with higher R2 of 0.9971 and lower mean standard deviation of 0.174, compared to PdTFPP 16.496 % and 49.988 % respectively, with R2 of 0.9211 and higher mean standard deviation of 2.003. This improvement can benefit researchers and professionals in medical diagnostics, environmental monitoring, and industrial process control by providing a more sensitive and accurate method for measuring O2 levels.

Quality management of head and neck patient treatments using statistical process control techniques.

The treatment, planning, simulation, and setup of radiotherapy patients contain many processes subject to errors involving both staff and equipment. Cone-beam-CT (CBCT) provides a final check of patient positioning and corrections based on this can be made prior to treatment delivery. Statistical Process Control (SPC) techniques are used in various industries for quality management and error mitigation. The utility of SPC techniques to monitor process and equipment changes in our Head and Neck patient treatments was assessed by application to CBCT results from a quality-focused longitudinal study. Individuals and moving range (XmR) as well as exponentially-weighted moving average (EWMA) techniques were explored. The SPC techniques were sensitive to process changes and trends over the 12 years of data collected. A reduction in the random component of patient setup errors needing correction was observed. Systematic components of error remained more stable. An uptick in both datasets was observed correlating with the COVID-19 pandemic. Process control limits for use in prospective process monitoring were established. Challenges that arose from using SPC techniques in a retrospective study are outlined.

Advanced AI Technologies for Defect Prevention and Yield Optimization in PCB Manufacturing

The integration of Advanced Artificial Intelligence (AI) technologies in Printed Circuit Board (PCB) manufacturing has revolutionized the industry, offering significant improvements in defect prevention and yield optimization. As PCB manufacturing processes become increasingly complex and intricate, traditional methods of defect detection and quality assurance are often insufficient to meet the high demand for precision and efficiency. This paper explores the application of cutting-edge AI methodologies, including machine learning, computer vision, and neural networks, to address critical challenges in PCB production. The primary focus of this research is on the role of AI in enhancing automated defect detection systems and optimizing production yield. AI-based systems such as predictive maintenance, real-time process monitoring, and intelligent decision-making have been shown to drastically reduce defects while improving overall production throughput. Machine learning algorithms can identify subtle defects in real time, often undetectable by conventional methods, while neural networks can analyze historical data to predict potential failures before they occur. Additionally, AI-driven optimization techniques help manufacturers adjust production parameters dynamically, ensuring higher yields and minimizing waste. Through a combination of theoretical analysis and case studies, this paper highlights the effectiveness of AI-driven solutions in PCB manufacturing. Results from industry applications indicate significant improvements in both quality control and yield rates, providing a competitive edge to manufacturers adopting these technologies. Furthermore, this paper discusses future trends, including the integration of AI with the Internet of Things (IoT), edge computing, and sustainable manufacturing practices, which will further enhance the capabilities of AI systems in this field. The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions. This research underscores the transformative impact of AI in modernizing manufacturing processes, making it an indispensable tool for the future of the PCB industry.

Craving to belong and to forgive in travel social media: a synthesis between goal progress and self-regulation perspectives

ABSTRACT This research improvises a model highlighting how tourists’ ruminative thinking from travel SNSs could be mitigated through a four-component approach rooted in self-regulation theory and the goal progress theory of rumination. Using a survey design, the model posits the need to belong as a motivational factor that drives travel SNS engagement and travel impulsion through the meditational monitoring processes of forgiveness and trust in SNSs. Two willpower factors – perceived secondary control and perceived enjoyment – were examined. This inquiry adds to the literature by building a synthesis between the two theories to showcase how tourists regulate themselves after traumatic events in cyberspace, and how this is juxtaposed with rumination through the lens of goal setting. The proposed model helps to explain the rumination of negative instances in social media and how tourists negotiate their conduct.

High-Fidelity Sensing Modality for Anomaly Detection in Inkjet Printing

Abstract Inkjet three-dimensional (3D) printing has emerged as a transformative manufacturing technique, finding applications in diverse fields such as biomedical, metal fabrication, and functional materials production. It involves precise deposition of materials onto a moving substrate through a nozzle, achieving submillimeter scale resolution. However, the dynamic nature of droplet deposition introduces uncertainties, challenging consistent quality control. Current process monitoring, relying on image-based techniques, is slow and limited, hindering real-time feedback in erratic droplet ejection. In response to these challenges, we present the zero-dimensional ultrafast sensing (0-DUS) system, a novel, cost-effective, in situ monitoring tool designed to assess the quality of drop-on-demand inkjet printing. The 0-DUS system leverages the sensitivity of the light-beam field interference effect to rapidly and precisely detect and analyze localized droplets. Two core technical advancements drive this innovation: first, the exploration of integral sensing of the computational light-beam field, which allows for efficient extraction of temporal and spatial information about droplet evolution, introducing a novel in situ sensing modality; second, the establishment of a robust mapping mechanism that aligns sensor data with image-based data, facilitating accurate estimation of droplet characteristics. We successfully implemented the 0-DUS system within a commercial inkjet printer and conducted a comparative analysis with ground truth data. Our experimental results demonstrate a detection accuracy exceeding 95%, even at elevated speeds, allowing for an impressive in situ certification throughput of up to 500 Hz. Consequently, our proposed 0-DUS system meets the stringent quality assurance requirements, thereby expanding the potential applications of inkjet printing across a wide spectrum of industrial sectors.

Classification of melt pool states for defect detection in laser directed energy deposition using FixConvNeXt model

Abstract Laser directed energy deposition (L-DED) has emerged as a promising technique for rapid prototyping due to its cost-effectiveness and efficiency. However, the intricate and multi-scale physics of the process hinder its widespread application. This paper addresses the challenge by focusing on real-time identification of melt pool states to detect defects early and minimize resource wastage. To achieve this, a FixConvNeXt model was developed for fast and accurate monitoring of melt pool states. This model was trained using 5000 melt pool images captured during the printing of single-track deposits from a charge-coupled device. To evaluate its performance, FixConvNeXt was compared with other models using various metrics. Experimental results demonstrated that FixConvNeXt achieved superior performance in accurately identifying melt pool states with 99.1% accuracy, while also reducing computation burden and processing time. The mechanism of classification by FixConvNeXt was explained using gradient-weighted class activation mapping. The research findings highlight the potential application of online process monitoring in L-DED. This study lays the foundation for future development of an efficient deep learning network for automatic defect detection and feedback control.

Dynamic control of cardboard-blank picking by using reinforcement learning

Abstract Standard packaging lines with high output rates often struggle when dealing with uncertainties in the conditions of the handled materials. This paper focuses on a piece of machinery in an automatic packaging line, namely an automated apparatus that extracts cardboard blanks from a buffer and transfers them to the next section through suction cups. In this context, the success of the operation depends on various controllable parameters, disturbances, and time-dependent variables, whose mutual relationships are not easily identifiable and whose understanding has so far been entrusted to the experiential knowledge of human operators. Currently, drops in picking success rates require the machine to be stopped and operators to intervene on-site, making use of their expertise to identify the issue and recalibrate the machine. To address the problem, this paper presents an artificial-intelligence-enabled controller, capable of continuously and autonomously recalibrating the apparatus and compensating for disturbances, in order to avoid missed or incorrectly picked cardboard blanks. In particular, this work exploits experimental data to build a model of the system, on which a reinforcement-learning algorithm is trained. The controller is tasked with regulating the controllable parameters while monitoring process variables. The developed agent is tested on the real apparatus to assess its performance.

A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry

The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance.

New plant health monitoring framework

With the continuous advancement of technology and the expanding demands of the agricultural market, plant health monitoring holds significant importance in the agricultural sector. However, according to statistics, most current monitoring models exhibit low detection efficiency and inadequate accuracy. To address these issues and specifically enhance the efficiency and accuracy of plant health monitoring, this paper proposes a recognition strategy and plan composed of two parts: (1) identification of plant species and (2) health monitoring, diagnosis, and treatment recommendations. During the health monitoring process, issues such as plant diseases, pests, nutrient deficiencies, and inadequate environmental conditions can be detected. This strategy, implemented through a systematic automated monitoring system, will greatly benefit the large-scale development of agriculture.

Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach

Data-driven models have become powerful tools for structural and condition monitoring of engineering systems, particularly wind turbines. This paper presents a comparative analysis of common machine learning (ML) algorithms (artificial neural networks, linear regression, random forests, and gradient boosting) and a cointegration-based approach for fault detection using Supervisory Control and Data Acquisition (SCADA) data. While ML models offer early fault prediction, the cointegration method is simpler, requires less training data, and has lower computational costs. However, it is less effective for early detection. To balance these trade-offs, we propose a cascading monitoring framework, where the ML model provides long-term predictions (outer monitoring process) and the cointegration model offers short-term verification (inner monitoring process). The cointegration model serves to confirm anomalies flagged by the ML model. By combining both models in a cascade structure, the system reduces the risk of false alarms triggered by uncertainties in the ML model alone. Furthermore, the short-term cointegration-based prediction model helps pinpoint immediate risks and mitigate the issue of prolonged downtime. This combination enhances both accuracy and reliability, as demonstrated through testing on a five-year SCADA dataset from a commercial wind turbine with a known gearbox fault.