Explore the vast range of titles available.

Manufacturing condition monitoring in low-frequency sensing environments presents significant challenges for traditional anomaly detection methods, which depend on dense temporal observations or instantaneous thresholding. In these contexts, transient fluctuations often overshadow individual measurements, resulting in unstable and unreliable alarm responses. This paper addresses these challenges by framing anomaly monitoring as an alarm-centric decision problem specifically designed for low-frequency manufacturing sensor data. The proposed framework assesses deviations relative to stable idle-state reference values using absolute relative error (ARE), which provides a normalized and dimensionless representation of proportional degradation across diverse sensor features. Alarm decisions are then based on the persistence of threshold exceedances over consecutive idle-state observations, rather than relying on single-sample anomalies. By distinctly separating deviation modeling from alarm decision-making, the framework facilitates stable and interpretable alarm generation without depending on waveform reconstruction or parametric distribution assumptions. Validation of the framework is conducted using real industrial monitoring data under controlled fault-simulation conditions. The results indicate that persistence-based decision logic significantly enhances alarm reliability for both absolute and squared deviation baselines, while the ARE-based deviation yields superior discrimination for sustained proportional degradation. By combining ARE-based deviation modeling with persistence-based alarm decision logic, the proposed ARE-based persistence strategy achieves the highest reliability in alarm behavior among all methods compared, demonstrating its efficacy for low-frequency manufacturing monitoring.

Background β-Carotene is a natural product that has garnered significant commercial interest. Considerable efforts have been made to meet such demand through the metabolic engineering of microorganisms, yet there is still potential for improvement. In this study, engineering approaches including carbon and redox rebalancing were used to maximize β-carotene production in Yarrowia lipolytica . Results The initial production level was increased by iterative overexpression of pathway genes with lycopene inhibition removal. For further improvement, two approaches that redirect the central carbon pathway were evaluated to increase NADPH regeneration and reduce ATP expenditure. Pushing flux through the pentose phosphate pathway and introducing NADP + -dependent glyceraldehyde-3-phosphate dehydrogenase were found to be more effective than the phosphoketolase-phosphotransacetylase (PK-PTA) pathway. Furthermore, flux to the lipid biosynthesis pathway was moderately increased to better accommodate the increased β-carotene pool, resulting in the production level of 809.2 mg/L. Conclusions The Y. lipolytica -based β-carotene production chassis was successfully developed through iterative overexpression of multiple pathways, central carbon pathway engineering and lipid pathway flux adjustment. The approach presented here provides insights into future endeavors to improve microbial terpenoid production capability.

Thermal power plants (TPPs) are critical to supplying energy to society, and ensuring their safe and efficient operation is a top priority. To minimize maintenance shutdowns and costs, modern TPPs have adopted advanced fault detection and diagnosis (FDD) techniques. These FDD approaches can be divided into three main categories: model-based, data-driven-based, and statistical-based methods. Despite the practical limitations of model-based methods, a multitude of data-driven and statistical techniques have been developed to monitor key equipment in TPPs. The main contribution of this paper is a systematic review of advanced FDD methods that addresses a literature gap by providing a comprehensive comparison and analysis of these techniques. The review discusses the most relevant FDD strategies, including model-based, data-driven, and statistical-based approaches, and their applications in enhancing the efficiency and reliability of TPPs. Our review highlights the novel and innovative aspects of these techniques and emphasizes their significance in sustainable energy development and the long-term viability of thermal power generation. This review further explores the recent advancements in intelligent FDD techniques for boilers and turbines in TPPs. It also discusses real-world applications, and analyzes the limitations and challenges of current approaches. The paper highlights the need for further research and development in this field, and outlines potential future directions to improve the safety, efficiency, and reliability of intelligent TPPs. Overall, this review provides valuable insights into the current state-of-the-art in FDD techniques for TPPs, and serves as a guide for future research and development.

With the wave of the Fourth Industrial Revolution, the construction industry is also witnessing the application of numerous state-of-the-art technologies. Among these, augmented reality (AR) technology has the advantage of utilizing existing 3D models and BIM data and is thus an area of active research. However, the main area of research to date has either been in visualizing information during the design phase, where architects and project stakeholders can share viewings, or in confirming the required information for construction management through visualization during the construction phase. As such, more research is required in the application of AR during the facility management (FM) phase. Research utilizing BIM in the FM phase, which constitutes the longest period during the lifecycle of a building, has been continuously carried out but has faced challenges with regard to on-site application. The reason for this is that information required for BIM during the design, construction and FM phases is different, and the reproduced information is vast, so identifying the required BIM data for FM and interfacing with other systems is difficult. As a measure to overcome this limitation, advanced countries such as the US and UK have developed and are using Construction Operations Building information exchange (COBie), which is an open-source BIM-based information exchange system. In order to effectively convert open-source BIM data to AR data, this research defined COBie data for windows and doors, converted them to a system and validated that it could actually be applied for on-site FM. The results of this system’s creation and validation showed that the proposed AR-based smart FMS demonstrated faster and easier access to information compared with existing 2D blueprint-based FM work, while information obtained through AR allowed for immediate, more visual and easier means to express the information when integrated with actual objects.

As laminated composites are applied more commonly, Prognostics and Health Management (PHM) techniques for the maintenance of composite systems are also attracting attention. However, applying PHM techniques to a composite system is challenging due to the data imbalance problem from the lack of failure data and unpredictable failure cases. Despite numerous studies conducted to address this limitation, including techniques like data augmentation and transfer learning, significant challenges remain. In this study, the Wasserstein Generative Adversarial Network (WGAN) model using a time-series data augmentation technique is proposed as a solution to the data imbalance problem. To ensure the performance of the WGAN model, time-series data augmentation of experimental data is executed with a frequency analysis. After that, a One-Dimensional Convolutional Neural Network (1D CNN) is used for fault diagnosis in laminated composites, validating the performance improvement after data augmentation. The proposed data augmentation significantly elevated the performance of the 1D CNN classification model compared to its non-augmented counterpart. Specifically, the accuracy increased from 89.20% to 91.96%. The precision improved remarkably from 29.76% to 74.10%, and its sensitivity rose from 33.33% to 94.39%. Collectively, these enhancements highlight the vital role of data augmentation in improving fault diagnosis performance.

A robot is essential in many industrial and manufacturing facilities due to its efficiency, accuracy, and durability. However, continuous use of the robotic system can result in various component failures. The servo motor is one of the critical components, and its bearing is one of the vulnerable parts, hence failure analysis is required. Some previous prognostics and health management (PHM) methods are very limited in considering the realistic operating conditions of industrial robots based on various operating speeds, loading conditions, and motions, because they consider constant speed data with unloading conditions. This paper implements a PHM for the servo motor of a robotic arm based on variable operating conditions. Principal component analysis-based dimensionality reduction and correlation analysis-based feature selection are compared. Two machine learning algorithms have been used to detect fault features under various operating conditions. This method is proposed as a robust fault-detection model for industrial robots under various operating conditions. Features from different domains not only improved the generalization of the model’s performance but also improved the computational efficiency of massive data by reducing the total number of features. The results showed more than 90% accuracy under various operating conditions. As a result, the proposed method shows the possibility of robust failure diagnosis under various operating conditions similar to the actual industrial environment.

Regular scaffolding quality inspection is an essential part of construction safety. However, current evaluation methods and quality requirements for temporary structures are based on subjective visual inspection by safety managers. Accordingly, the assessment process and results depend on an inspector’s competence, experience, and human factors, making objective analysis complex. The safety inspections performed by specialized services bring additional costs and increase evaluation times. Therefore, a temporary structure quality and safety evaluation system based on experts’ experience and independent of the human factor is the relevant solution in intelligent construction. This study aimed to present a quality evaluation system prototype for scaffolding parts based on computer vision. The main steps of the proposed system development are preparing a dataset, designing a neural network (NN) model, and training and evaluating the model. Since traditional methods of preparing a dataset are very laborious and time-consuming, this work used mixed real and synthetic datasets modeled in Blender. Further, the resulting datasets were processed using artificial intelligence algorithms to obtain information about defect type, size, and location. Finally, the tested parts’ quality classes were calculated based on the obtained defect values.

Due to its simplicity, accuracy, and adaptability, Crimp Force Monitoring (CFM) has long been the standard for fault detection in wiring harness manufacturing. However, it necessitates frequent reconfigurations based on the variability in materials, dependency on operator skill, and high costs of implementation, and thus reconfiguration presents significant challenges. To solve these problems, this paper introduces a fault detection system that employs an Artificial Intelligence (AI) classification model to enhance the performance and cost-efficiency of the quality control process of wiring harness manufacturing. Since there are no labeled data to train the classification model at the onset of manufacturing, a small number of normal data from each production run are manually extracted to train the model. To address the constraint of the limited available data, the system generates synthetic data from normal data, simulating potential defects by using Regional Selective Data Scaling (RSDS). This innovative method performs upscaling or downscaling on specific regions of the original data to produce synthetic abnormal data, which enables the fault detection system to efficiently train its classification model with a dataset consisting solely of normal operation data.

This review paper addresses the critical need for structural prognostics and health management (SPHM) in aircraft maintenance, highlighting its role in identifying potential structural issues and proactively managing aircraft health. With a comprehensive assessment of various SPHM techniques, the paper contributes by comparing traditional and modern approaches, evaluating their limitations, and showcasing advancements in data-driven and model-based methodologies. It explores the implementation of machine learning and deep learning algorithms, emphasizing their effectiveness in improving prognostic capabilities. Furthermore, it explores model-based approaches, including finite element analysis and damage mechanics, illuminating their potential in the diagnosis and prediction of structural health issues. The impact of digital twin technology in SPHM is also examined, presenting real-life case studies that demonstrate its practical implications and benefits. Overall, this review paper will inform and guide researchers, engineers, and maintenance professionals in developing effective strategies to ensure aircraft safety and structural integrity.

This study presents a computational framework for the topology optimization of a patient-specific femoral component used in the total knee endoprosthesis. The motivation stems from the growing need to enhance implant longevity and biomechanical compatibility by optimizing internal structural design according to physiological loading conditions. A finite element–based density optimization method was employed to determine the optimal material distribution within the femoral component while maintaining anatomical geometry and functional constraints. The model was developed using realistic boundary conditions derived from knee joint mechanics, and the resulting design was compared with a conventional reference geometry. The optimized configuration exhibited more uniform stress distribution, reduced peak von Mises stresses, and improved mass efficiency without compromising mechanical stiffness. These findings demonstrate that the proposed method can significantly improve the structural performance and reliability of knee prostheses. The study concludes that integrating patient-specific modeling with topology optimization offers a promising pathway for developing advanced, individualized orthopedic implants and supports future experimental validation through 3D printing and biomechanical testing.