Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms was recently introduced for intelligent fault detection and diagnosis in SPPs. However, these multivariate algorithms are highly dependent on the number of input process variables (sensors). Therefore, this work proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler waterwall tube leakage. Finally, a real SPP test case is employed to validate the proposed model’s effectiveness. The results indicate that the proposed model can successfully detect waterwall tube leakage with improved accuracy vs. other comparable models.

Energy efficiency, longevity, and environmental benefits have made light emitting diodes (LEDs) indispensable in modern lighting and display applications. However, degradation mechanisms influenced by thermal stress, electrical overstress, and environmental conditions mean that their reliability remains a significant challenge. Prognostics and Health Management (PHM) has emerged as a promising approach for monitoring and predicting LED failures, enabling predictive maintenance whilst optimizing operational efficiency. This review comprehensively explores PHM methodologies for LEDs, encompassing physics-of-failure (PoF) models, data-driven approaches, and hybrid techniques that integrate both methodologies. While PoF models offer insights into physics-based failure, data-driven methods leverage statistical analysis, machine learning (ML), and deep learning (DL) for predictive analytics. Hybrid PHM frameworks combine these approaches to enhance prediction accuracy and robustness. The integration of Internet of Things (IoT)-enabled real-time monitoring, digital twins, and edge computing has further improved LED PHM capabilities. Despite these advances, challenges persist in sensor placement limitations, variability in LED architecture, data availability issues, and high computational costs. Overcoming these challenges through standardization, the development of adaptive hybrid models, and the application of advanced Artificial Intelligence (AI)-driven analytics will be essential for enabling the widespread adoption of PHM in LED applications across various industrial sectors. This review highlights key advances, current limitations, and future research directions to improve LED reliability and extend operational life through PHM strategies.

Shape memory alloy (SMA)-textile actuators have attracted significant attention across various fields, including soft robotics and wearable technology. These smooth actuators are developed by combining SMA and simple textile fibers and then knitting them into two loop patterns known as the knit (K-loop) and plain (P-loop) patterns. Both loops are distinguished by opposite bending characteristics owing to loop head geometry. However, the knitting processes for these actuator sheets require expertise and time, resulting in high production costs for knitted loop actuation sheets. This study introduces a novel method by which to assess the strain in SMA textile-based actuators, which experience large deformations when subjected to voltage. Owing to the highly nonlinear constitutive equations of the SMA material, developing an analytical model for numerical analysis is challenging. Therefore, this study employs a novel approach that utilizes a linear constitutive equation to analyze large deformations in SMA material with nonlinear geometry considerations. The user-defined material (UMAT) subroutine integrates the linear constitutive equation into the ABAQUS software suite. This equivalent unit cell (EUC) model is validated by comparing the experimental bending actuation results of K-loops and P-loops.