In public buildings located in geographies with extreme and highly variable environmental conditions, such as Sweden, managing lighting systems reliably presents unique challenges. These arise not only from technical concerns like lifespan and energy use, but also from the need to ensure human well-being under seasonal extremes of natural light availability. This paper proposes a novel, context-driven framework for asset diagnostics and performance monitoring using two metrics: critical integrative levels—which classify lighting zones based on user activity, exposure time, and age group—and mean time of exposure, a newly introduced variable that quantifies user interaction with light in space and time. Three case studies across Swedish public libraries (from Luleå to southern Sweden) were used to validate the approach. The results show that integrating human-centric parameters into lighting diagnostics enables more responsive, context-aware threshold setting, compared to traditional functionality-based criteria (e.g. luminous flux percentage). Practical implications include earlier anomaly detection, prioritization of maintenance, and the ability to tailor lighting reliability metrics to real user needs, particularly under non-stationary environmental conditions. The proposed model provides a scalable structure for use in smart public infrastructure, aligning with Industry 5.0 principles. Future research will focus on extending the framework to automated digital twin environments, as well as exploring generalization across sectors such as healthcare, education, and transportation hubs.

This white paper, part of the IEEE Reliability Society's roadmap series, provides a high-level summary of the critical needs, challenges, and potential solutions for enhancing battery reliability over the next decade. It specifically examines batteries operating in harsh environments, with detailed focus areas including Data Centers, Electric Vehicles (EVs), and Aerospace applications. The paper highlights the discrepancy between theoretical and actual battery life, the importance of accurate sensor measurements, and the need to integrate the "zero-life" stage into battery lifecycles for better reliability assessment. Emphasizing a holistic approach, it delves into application-specific qualification methodologies for data center batteries, the evolving reliability ecosystem and lifecycle framework for EV batteries, and advanced health monitoring requirements for aerospace batteries. Ultimately, this document serves as a foundational assessment, inviting experts from academia, industry, and research to collaborate on a more in-depth roadmap for dependable, safe, and secure energy storage solutions across diverse applications for the betterment of humanity.

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.

Yet, as vehicles become more intelligent, interconnected, and electrified, the demands on BGAs grow. Extreme environments, continuous operation, and evolving functionality challenge their reliability at every turn. How can the automotive industry ensure these vital components remain robust under such relentless stress? This article dives into the challenges BGAs face and the innovations redefining their reliability

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.

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.