Search Results (1,069)

This paper addressed the problem of oil diagnostics lubricants applied to the predictive maintenance of industrial gearboxes, proposing the integration of an artificial intelligence (AI) system into the process analysis. The main objective was to overcome the critical issues of the traditional method, characterized by long analysis times and a marked dependence on the subjective interpretation of operators. The method includes a detailed statistical analysis of the common ways to assess the condition of lubricants, such as optical emission spectroscopy, particle counting, measuring viscosity and density, and Fourier-transform infrared spectroscopy (FT-IR). These methods are then combined with an artificial intelligence model. Tested on commercial gearbox data, the proposed approach demonstrates agreement between IA and expert evaluation. The application has shown that it can effectively support diagnoses, reduce processing time by 60%, and minimize human errors. It also improves knowledge sharing through an increase in the stability and repetitiveness of diagnoses and promotes consistency and clarity in reporting. Full article

The classical V-Model has served as the foundational framework for aerospace systems engineering, but its scope terminates upon aircraft certification, creating a significant gap in addressing reliability degradation during operational service. This study introduces the W-model framework—a comprehensive lifecycle management approach that extends the V-Model to systematically integrate reliability-centered component modifications with established aerospace development practices. The W-model incorporates a structured six-phase reliability-centered modification methodology that transforms operational data into certified design improvements through systematic reliability monitoring, candidate selection, design reviews, development, and certification processes. A detailed case study on the aviation pneumatic bypass valve demonstrates the methodology. Application of the W-model resulted in a 36% improvement in the mean time between failures and a significant reduction in unscheduled removals. The W-model represents a paradigm shift from reactive maintenance strategies to proactive, data-driven reliability enhancement, providing a systematic approach that maintains the rigor and traceability required for commercial aviation while enabling continuous reliability growth throughout the complete aircraft lifecycle. Full article

The development of Industry 4.0 has accelerated the adoption of sophisticated technologies, including Digital Twins (DTs), Artificial Intelligence (AI), and cybersecurity, within Additive Manufacturing (AM). Enabling real-time monitoring, process optimization, predictive maintenance, and secure data management can redefine conventional manufacturing paradigms. Although their individual importance is increasing, a consistent understanding of how these technologies interact and collectively improve AM procedures is lacking. Focusing on the integration of digital twins (DTs), modular AI, and cybersecurity in AM, this review presents a comprehensive analysis of over 137 research publications from Scopus, Web of Science, Google Scholar, and ResearchGate. The publications are categorized into three thematic groups, followed by an analysis of key findings. Finally, the study identifies research gaps and proposes detailed recommendations along with a framework for future research. The study reveals that traditional AM processes have undergone significant transformations driven by digital threads, digital threads (DTs), and AI. However, this digitalization introduces vulnerabilities, leaving AM systems prone to cyber-physical attacks. Emerging advancements in AI, Machine Learning (ML), and Blockchain present promising solutions to mitigate these challenges. This paper is among the first to comprehensively summarize and evaluate the advancements in AM, emphasizing the integration of DTs, Modular AI, and cybersecurity strategies. Full article

Personal Rapid Transit (PRT) and Group Rapid Transit (GRT) are classes of fully automated public transport systems, where passengers can travel in small vehicles on an interconnected, grade-separated network of guideways, non-stop, from origin to destination. PRT and GRT are considered sustainable as they are low-emission and able to attract car drivers. The parameterized cost modeling framework developed in this paper has the advantage that profitability of different PRT/GRT systems can be rapidly verified in a transparent way and in function of a variety of relevant system parameters. This framework may contribute to a more transparent, rapid, and low-cost evaluation of PRT/GRT schemes for planning and decision-making purposes. The main innovation is the introduction of the “peak hour network saturation” S: the number of vehicles in circulation during peak hour divided by the maximum number of vehicles running at line speed with minimum time headways. It is an index that aggregates the main uncertainties in the planning process, namely the demand level relative to the supply level. Furthermore, a maximum S can be estimated for a PRT/GRT project, even without a detailed demand estimation. The profit per trip is analytically derived based on S and a series of more certain parameters, such as fares, capital and maintenance costs, daily demand curve, empty vehicle share, and physical properties of the system. To demonstrate the ability of the framework to analyze profitability in function of various parameters, we apply the methods to a single vehicle PRT, a platooned PRT, and a mixed PRT/GRT. The results show that PRT services with trip length proportional fares could be profitable already for $S>0.25$. The PRT capacity, profitability, and robustness to tripled infrastructure costs can be increased by vehicle platooning or GRT service during peak hours. Full article

Corrosion in maritime ballast tanks is a major driver of maintenance costs and operational risks for maritime assets. Inspections are hampered by complex geometries, hazardous conditions, and the limitations of conventional methods, particularly visual assessment, which struggles with subjectivity, accessibility, and early detection, especially under coatings. This paper critically examines these challenges and explores the potential of Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) to form the basis of improved inspection approaches. We discuss LiDAR’s utility for accurate 3D mapping and providing a spatial framework and HSI’s potential for objective material identification and surface characterization based on spectral signatures along a wavelength range of 400-1000nm (visible and near infrared). Preliminary findings from laboratory tests are presented, demonstrating the basic feasibility of HSI for differentiating surface conditions (corrosion, coatings, bare metal) and relative coating thickness, alongside LiDAR’s capability for detailed geometric capture. Although these results do not represent a deployable system, they highlight how LiDAR and HSI could address key limitations of current practices and suggest promising directions for future research into integrated sensor-based corrosion assessment strategies. Full article

Bridges, owing to their intricacy, represent pivotal yet relatively underexplored assets within the domain of maintenance services in civil engineering. While international evaluation methodologies exist to gauge the overall condition of bridges, they often fall short in establishing interrelationships among individual elements, thereby neglecting insights into the influence exerted by each element’s condition on the bridge’s overall performance. This research introduces an integral fuzzy model evaluation with a preventive approach, designed to assess both the integral condition of a bridge and its constituent elements. Furthermore, the study generates maintenance recommendations, subsequently evaluated by professionals to determine the most suitable course of action based on available resources. To validate the efficacy of the proposed model, a case study involving Bridge 15-016-00.0-0-04.0 PIV, known as “La Cuesta” in Mexico, is presented. The findings indicate that the bridge is in a satisfactory condition and warrants high-priority attention. Bridge analysis is compared with evaluations conducted using the methods of the Secretariat of Infrastructure, Communications, and Transportation (SICT), the American Association of State Highway and Transportation Officials (AASHTO), and the Ministry of Transport and Communications of Peru. The comparative evaluation reveals that our proposed model provides a more detailed representation of deterioration, facilitating more efficient maintenance planning by considering the hierarchical relationships between the bridge’s modules and elements. Full article

Electric submersible pumps (ESPs) are critical for artificial lift operations; however, they are prone to frequent failures, often resulting in high operational costs and production downtime. Traditional ESP failure classifications are limited by lack of standardization and the conflation of failure modes with root causes. To address these limitations, this study proposes a new two-step integrated failure modes and root cause (IFMRC) classification system. The new framework clearly distinguishes between failure modes and root causes, providing a systematic, structured approach that enhances fault diagnosis and failure analysis and can lead to better failure prevention strategies. This methodology was validated using a case study of over 4000 ESP installations. The data came from Egypt’s Western Desert, covering a decade of operational data. The sources included ESP databases, workover records, and detailed failure investigation (DIFA) reports. The failure modes were categorized into electrical, mechanical, hydraulic, chemical, and operational types, while root causes were linked to environmental, design, operational, and equipment factors. Statistical analysis, in this case study, revealed that motor short circuits, low flow conditions, and cable short circuits were the most frequent failure modes, with excessive heat, scale deposition, and electrical grounding faults being the dominant root causes. This study underscores the importance of accurate root cause failure classification, robust data acquisition, and expanded failure diagnostics to improve ESP reliability. The proposed IFMRC framework addresses limitations in conventional taxonomies and facilitates ongoing enhancement of ESP design, operation, and maintenance in complex field conditions. Full article

The scope of this paper is the development of a cost-effective wireless torque measurement system for vehicle drivetrain shafts. The prototype integrates strain gauges, an HX711 conditioner, a Wemos D1 Mini ESP8266, and a rechargeable battery directly on the rotating shaft, forming a self-contained sensor node. Calibration against a certified dynamometric wrench confirmed an operating span of ±5–50 N·m. Within this range, the device achieved a mean absolute error of 0.559 N·m. It also maintained precision better than ±2.5 N·m at 95% confidence, while real-time data were transmitted via Wi-Fi. The total component cost is below EUR 30 based on current prices. The novelty of this proof-of-concept implementation demonstrates that reliable, IoT-enabled torque sensing can be realized with low-cost, readily available parts. The paper details assembly, calibration, and deployment procedures, providing a transparent pathway for replication. By aligning with Industry 4.0 requirements for smart, connected equipment, the proposed torque measurement system offers an affordable solution for process monitoring and predictive maintenance in automotive and industrial settings. Full article

Industrial maintenance has shifted from reactive repairs and calendar-based servicing toward data-driven predictive strategies. This paper presents a non-intrusive, low-cost IoT hardware platform for sustainable predictive maintenance of rotating machinery. The system integrates an ESP32-S3 sensor node that captures vibration (100 kHz) and temperature data, performs local logging, and communicates wirelessly. An automated spectral band segmentation framework is introduced, comparing equal-energy, linear-width, nonlinear, clustering, and peak–valley partitioning methods, followed by a weighted feature scheme that emphasizes high-value bands. Three unsupervised one-class classifiers—transformer autoencoders, GANomaly, and Isolation Forest—are evaluated on these weighted spectral features. Experiments conducted on a custom pump test bench with controlled anomaly severities demonstrate strong anomaly classification performance across multiple configurations, supported by detailed threshold-characterization metrics. Among 150 model–segmentation configurations, 25 achieved perfect classification (100% precision, recall, and F1 score) with ROC-AUC = 1.0, 43 configurations achieved ≥90% accuracy, and the lowest-performing setup maintained 81.8% accuracy. The proposed end-to-end solution reduces the downtime, lowers maintenance costs, and extends the asset life, offering a scalable, predictive maintenance approach for diverse industrial settings. Full article

The efficient and reliable maintenance and repair of industrial machinery depend critically on circuit diagrams, which serve as essential references for troubleshooting and must be updated when machinery is modified. However, many circuit diagrams are not available in structured, machine-readable format; instead, they often exist as unstructured PDF files, rendered images, or even photographs. Existing digitization methods often address isolated tasks, such as symbol detection, but fail to provide a comprehensive solution. This paper presents a novel pipeline for extracting the underlying graph structures of circuit diagrams, integrating image preprocessing, pattern matching, and graph extraction. A U-net model is employed for noise removal, followed by gray-box pattern matching for device classification, line detection by morphological operations, and a final graph extraction step to reconstruct circuit connectivity. A detailed error analysis highlights the strengths and limitations of each pipeline component. On a skewed test diagram from a scan with slight rotation, the proposed pipeline achieved a device detection accuracy of 88.46% with no false positives and a line detection accuracy of 94.7%. Full article

Background/Objectives: Bisphenol A (BPA) is found throughout the environment and exposure to it has been shown to cause several health problems, including cancer. The problem with BPA is that it is a xenoestrogen that is chemically very similar to 17β-estradiol. Chronic exposure to BPA overstimulates the estrogen receptors and leads to inflammation that triggers several pathways leading to cancer progression. This is especially true in the case of hormone-sensitive breast cancers. This article reviewed the main pathways thought to be involved in the formation and/or progression of the most common forms of hormone-sensitive breast cancers due to BPA exposure. The main results were compiled and presented in tables along with a more detailed discussion of each pathway within the text. In most cases, chronic BPA exposure led to inflammation, which then triggered pathways leading to cancer stem cell formation and maintenance. In other cases, BPA exposure led to the formation of reactive oxygen species that damaged DNA and caused the formation of mutated p53 and tumorigenesis. Conclusions: The article summarizes the key pathways that are currently known, pertaining to how BPA leads to the progression and maintenance of breast cancer. The article then concludes by discussing how prenatal and perinatal BPA exposure may also predispose women to hormone-sensitive breast cancers later in life. Full article

Crack detection in complex pavement scenarios remains challenging due to the sparse small-target features and computational inefficiency of existing methods. To address these limitations, this study proposes an enhanced architecture based on Mask2Former. The framework integrates two key innovations. A Local Perception Module (LPM) reconstructs geometric topological relationships through a Sequence-Space Dynamic Transformation Mechanism (DS2M), enhancing neighborhood feature extraction via depthwise separable convolutions. Simultaneously, an Auxiliary Convolutional Layer (ACL) combines lightweight residual convolutions with shallow high-resolution features, preserving critical edge details through channel attention weighting. Experimental evaluations demonstrate the model’s superior performance, achieving improvements of 3.2% in mIoU and 2.7% in mAcc compared to baseline methods, while maintaining computational efficiency with only 12.8 GFLOPs. These results validate the effectiveness of geometric relationship modeling and hierarchical feature fusion for pavement crack detection, suggesting practical potential for infrastructure maintenance systems. The proposed approach balances precision and efficiency, offering a viable solution for real-world applications with complex crack patterns and hardware constraints. Full article

Unsupervised learning has emerged as a pivotal methodology in scenarios where labeled data is scarce, expensive, or impractical to obtain. This article presents a robust framework combining autoencoders and Gaussian Mixture Models (GMMs) for unsupervised classification and Remaining Useful Life (RUL) prediction in mechanical systems, focusing on turbofan engines. The methodology addresses critical gaps in predictive maintenance by overcoming the reliance on labeled data, commonly a bottleneck in industrial applications, and effectively capturing subtle, high-dimensional degradation patterns to enable both robust unsupervised health state classification and accurate RUL estimation. A detailed mathematical foundation, implementation in MATLAB, and empirical validation are provided alongside discussions on hyperparameter tuning, computational complexity, and comparative analysis with traditional methods. The article concludes with practical insights into industrial applications and future research directions. Full article

The integration of Artificial Intelligence (AI) into manufacturing is transforming the industry by advancing predictive maintenance, quality control, and supply chain optimisation, while also driving the shift from Industry 4.0 towards a more human-centric and sustainable vision. This emerging paradigm, known as Industry 5.0, emphasises resilience, ethical innovation, and the symbiosis between humans and intelligent systems, with AI playing a central enabling role. However, challenges such as the “black box” nature of AI models, data biases, ethical concerns, and the lack of robust frameworks for trustworthiness hinder its widespread adoption. This paper provides a comprehensive survey of AI trustworthiness in the manufacturing industry, examining the evolution of industrial paradigms, identifying key barriers to AI adoption, and examining principles such as transparency, fairness, robustness, and accountability. It offers a detailed summary of existing toolkits and methodologies for explainability, bias mitigation, and robustness, which are essential for fostering trust in AI systems. Additionally, this paper examines challenges throughout the AI pipeline, from data collection to model deployment, and concludes with recommendations and research questions aimed at addressing these issues. By offering actionable insights, this study aims to guide researchers, practitioners, and policymakers in developing ethical and reliable AI systems that align with the principles of Industry 5.0, ensuring both technological advancement and societal value. Full article

The tensile strength of loess, a key mechanical parameter for crack-related failures, has not received much attention in previous research, with the literature demonstrating a lack of systematic studies. Therefore, in this study, the variations in the tensile strength, crack distribution, crack number, and internal stress of an undisturbed loess were studied in detail by combining the unconfined penetration test (UPT) and a discrete element method (DEM)-based simulation. The tensile strengths of undisturbed loess samples with different height–diameter ratios (1, 1.5, and 2) were investigated by using the UPT with loading plates of different diameters (12.86 mm, 15.56 mm, and 19.02 mm). DEM simulation was then conducted based on the experimental results. The results showed that (1) the tensile strength of undisturbed loess decreased with increased height–diameter ratio, while it increased with an increase in the diameters of the loading plates; (2) the DEM simulation allowed us to study the tensile characteristics of the undisturbed loess, and the simulated tensile strengths obtained via DEM simulation agreed with those determined via the UPT; (3) the distribution of internal stress and crack number in the DEM model were significantly influenced by the height–diameter ratio and loading plate diameter; (4) the number of cracks in the DEM model increased with an increase in the diameter of the loading plate, while the number of cracks first increased and then decreased with an increase in the height–diameter ratio. This study helps us to understand the variation in the tensile strengths of undisturbed loess samples from both macroscopic and microscopic perspectives. It is expected to serve as a reference for design, construction, and maintenance in engineering projects hinging upon the Loess Plateau region in China. Full article