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As infrastructure systems evolve into intelligent, interconnected cyber-physical systems (CPSs), traditional reliability-centered approaches are proving insufficient. This article introduces dependability-centered asset management (DCAM)-a forward-looking, system-level framework that unifies engineering, computing, and sustainability to manage assets in the CPS era. DCAM addresses the limitations of fragmented reliability and dependability practices by integrating lifecycle awareness, artificial intelligence (AI) and digital twins, adaptive decision-making, and distributed intelligence infrastructure. It emphasizes not only the use of AI to enhance asset reliability but also the importance of managing AI itself as a dependable asset. Real-world applications across energy, transportation, healthcare, and public infrastructure illustrate DCAM's potential to deliver resilient, trustworthy, and sustainable systems. This article concludes with future directions, including the role of emerging technologies such as blockchain and extended reality (XR), the need for new metrics, and the importance of interdisciplinary education and standards.

From controlling engine performance to enabling life-saving advanced driver-assistance systems (ADAS), the modern vehicle relies on a network of electronic systems that demand exceptional precision and reliability. At the heart of these systems are ball grid arrays (BGAs)—the tiny solder joints that provide the electrical and mechanical connections essential for smooth operation (see Figure 1). While they may be invisible to the casual observer, BGAs are integral to automotive technology.

In condition monitoring, the reliability of a predictive maintenance program is critically dependent on the precision of data obtained from measurement systems. With increased availability, a significant challenge is evaluating the capability of these measurement systems to ensure data precision, which is fundamental for informed system selection. To address this challenge, this study proposes a systematic framework for evaluating the capability of these measurement systems using Gage repeatability and reproducibility (Gage R&R) technique, subsequently judging the acceptability level and guiding their selection to guarantee the data precision. Our study investigates the capability of these systems in terms of repeatability and reproducibility, quantifying the contributions of different sources to the systems' capability and providing directions for measurement system correction and enhancement. Another distinctive innovation of our approach is the use of three-region graphs, incorporating metrics including percentage of Gage R&R to total variation, precision-to-tolerance ratio, and signal-to-noise ratio, which presents a comprehensive overview of the systems' capability within one single figure. Two comparative experiments in distinct application scenarios were conducted to validate the effectiveness of the proposed framework. The insights presented serve as a valuable reference to replace the commonly used experience-based system selection in condition monitoring. Through this framework, we present a promising data-based approach aimed at enhancing the widely employed time-based calibration strategies, ultimately contributing to the improvement of data quality and the overall success of condition monitoring initiatives.

Fatigue life estimation and accelerated life testing (ALT) are foundational methodologies in quality and reliability engineering. They ensure the durability and performance of materials and systems exposed to operational stresses such as thermal cycling, mechanical vibrations, humidity, corrosion, and multiaxial loads. Despite their shared objective of reliability assessment, these approaches differ in methodology and focus, offering complementary strengths. Compared to other approaches in reliability engineering, such as condition monitoring and predictive maintenance, neither fatigue life estimation nor ALT has seen significant development in the AI era.

Electric vehicle (EV) batteries play a crucial role in sustainable transportation, with reliability being pivotal to their performance, longevity, and environmental impact. This study explores battery reliability from micro (individual user), meso (industry), and macro (societal) perspectives, emphasizing interconnected factors and challenges across the lifecycle. A novel lifecycle framework is proposed, introducing the concept of “Zero-Life” reliability to expand traditional evaluation methods. By integrating the reliability ecosystem with a dynamic system approach, this research offers comprehensive insights into the optimization of EV battery systems. Furthermore, an expansive Social–Industrial Large Knowledge Model (S-ILKM) is presented, bridging micro- and macro-level insights to enhance reliability across lifecycle stages. The findings provide a systematic pathway to advance EV battery reliability, aligning with global sustainability objectives and fostering innovation in sustainable mobility.

Maintenance performance measurement is critical to the growth of businesses, particularly those in the process industries and those that rely on heavy machinery and/or assets for production. Such businesses cannot ignore or undermine the importance of MPM within their maintenance function. However, throughout the years, maintenance has been viewed as an additional cost of operating rather than an enabler for improved RAMS, product quality, decreased incidence of litigation, upholding one's reputation with stakeholders, and increased profit. Without measuring the performance of the maintenance function, it can be difficult to determine whether the maintenance goals have been achieved and neither can the maintenance activities be enhanced nor optimized. As a result, it may become the last place to invest, causing the company to forego the benefits that proper maintenance delivers. The main purpose of this study was to develop an integrated KPI framework for measuring maintenance performance in the mining industry. The 134 KPIs in the proposed KPI framework were classified as either technical KPIs (asset operations management KPIs with 23 KPIs) connected to the assets and/or machines or soft KPIs (maintenance process management and maintenance resources management with 85 and 26 KPIs respectively) related to workflow. The implementation of these KPIs has been discussed, and dates, definitions, and general formulas have been proposed. Results from this study will serve as a guideline for the implementation of the KPIs.

Fault detection in 3D printers is crucial for safety and quality assurance, emphasizing proactive prediction over reactive rectification based on manufacturing factors. Presently, most detection techniques rely on shallow models with limited representational capabilities, necessitating manual feature extraction from the captured signals. This manual process is not only cumbersome and potentially costly but often requires intricate domain-specific knowledge. Additionally, these handcrafted features might not optimally distinguish between normal and faulty samples, potentially reducing prediction accuracy. In this study, we introduce an end-to-end approach using the Deep Support Vector Data Description model for fault detection in 3D printers. This design inherently facilitates automatic feature learning, where the features are synergistically optimized for fault detection. Our experiments leverage magnetic field signals for fault detection in 3D printers, using 1D convolutional layers to discern temporal signal patterns and wide kernels in the initial layer to mitigate high-frequency noise. Furthermore, our model can be easily adapted to integrate multi-channel signals for enhanced accuracy. Evaluations on real-world data from a delta 3D printer underscore the superiority of our method compared to existing alternatives.

Despite the advancements in artificial lighting technology, such as LEDs, a gap persists between expected and actual performance outcomes, attributed to conventional reliability evaluation methods that inadequately mirror real-world operational conditions. This study explores the crucial roles of failure analysis and fault diagnosis in improving lighting source reliability. It reveals a focus on LED lighting failure analysis based on test data, highlighting a disconnect from operational fault diagnosis. By detailing fault types and diagnostic tools, the study proposes a practical fault diagnosis framework. This framework aims to enhance lighting reliability by incorporating diagnostic insights back into the design and manufacturing stages, creating a closed-loop system between failure analysis and fault diagnosis. This approach bridges the gap between theory and practice, advancing lighting reliability in public libraries. However, the insights into failure analysis and fault diagnosis can be applied to other scenarios.

In an era of rapidly advancing lighting technology and evolving public library roles, this study introduces a groundbreaking strategy for human-centric and integrative lighting asset management. Embracing both visual and non-visual effects, “integrative lighting” aims to enhance users’ physiological and psychological well-being. Despite technological progress, notably with LEDs, current asset management often lags, relying on reactionary measures rather than proactive strategies. As public libraries transform into dynamic learning hubs, the significance of indoor lighting, impacting both physical health and holistic well-being, cannot be understated. Yet, many existing solutions are based on controlled lab tests, bypassing the diverse real-world needs of public libraries. Aiming to explore and develop human-centric and integrative lighting asset management strategies to optimize lighting environments in public libraries, this research offers a cohesive approach encompassing context identification, a management framework, and a maturity assessment model. Additionally, this study highlights the synergy between the role of the lighting asset manager, ISO 55000 principles, and these foundational strategies. This holistic approach not only reinvents lighting in public libraries but also aligns it with the broader Sustainable Development Goals (SDGs), advocating for light as a conduit of comprehensive human betterment. The current study is primarily qualitative in nature. While this study is based on public libraries in Nordic countries, the implications and findings can be of interest and value to a broader international audience.