Condition monitoring (CM) and predictive maintenance (PdM) are essential for ensuring reliability and efficiency in intelligent manufacturing. While bearings and gears have been extensively studied, roller chains have received limited attention, primarily due to the difficulty of installing contact accelerometers on moving chains and the high cost of deploying sensors across long spans. Consequently, effective CM techniques for roller chains remain an open challenge. This study introduces a sensorless CM framework that relies solely on motor driver signals, eliminating the need for additional physical sensors. Motor torque and position data are acquired and processed using a Position-guided Multi-Step Deep Decomposition Network (PMSDDN), a lightweight neural architecture designed to construct a reliable health indicator (HI). PMSDDN segments the torque signal based on the motor position, decomposes each segment into low- and high-frequency components, and predicts them independently through efficient linear modules. This decomposition reduces noise and fluctuations, producing a smooth degradation trend that enhances interpretability. Compared with traditional HIs and state-of-the-art deep learning models, PMSDDN delivers higher effectiveness and computational efficiency. For anomaly detection, a Weighted K-Nearest Neighbors (WKNN) method is developed, combining three different statistical measures with uncertainty quantification to improve robustness and sensitivity to early degradation. The framework is validated on nine roller chains under three operating conditions. Results confirm superior performance in both health assessment and anomaly detection, highlighting its potential as a practical and scalable CM solution for roller chain systems in modern manufacturing environments.
In regulated domains such as aerospace or automotive software, compliance with standards like ECSS and ISO/IEC 26262 is mandatory to ensure safety and reliability. Artifacts, such as process or product models, lay the foundation for planning and managing development projects, as well as for measurement and evaluation tasks. Manually extracting such artifacts from PDF-based standards is time-consuming and error-prone. To address this issue, we developed a large language model (LLM)-based approach for automatically generating artifact models from standards. However, the evolution of AI models constitutes a challenge, since the extraction results might differ when updating the LLM to a newer version. In this article, we present an enhanced approach that strengthens the artifact-generation process against such challenges. The approach generates machine-readable artifact models that allow for automated measurement systems, for example, for monitoring compliance and controlling complex projects. The performance of the artifact model generation was evaluated using the ECSS standards from the aerospace domain and resulted in an average completeness of 100.00% and an average precision of 71.33% of the generated models.
Gearboxes are critical mechanical components widely deployed in industrial applications, where their reliable operation directly impacts system safety and efficiency. However, conventional fault diagnostic approaches face significant challenges when operating under extraneous transient noise conditions, particularly with limited labeled fault samples. These challenges manifest as performance degradation with extremely sparse labeled datasets, vulnerability in pseudo-label generation mechanisms under intense transient noise, and inconsistent feature scale representations due to noise-induced interference. Furthermore, existing methods struggle to maintain diagnostic accuracy when confronted with both data scarcity and transient disturbances, often resulting in compromised model generalization and unreliable fault classification. To address these limitations, this research proposes the Semi-Supervised Transfer Graph Representation Learning with Few-Shot Adaptation (SSTGRL-FSA) framework, featuring three innovative components: a novel pseudo-label reliability enhancement mechanism leveraging systematic knowledge transfer from established source domains, an advanced label transmission and matching strategy exploiting homologous signal patterns across operational domains, and an integrated first-order Markov state probability transition matrix with amplitude-constrained scaling. SSTGRL-FSA significantly advances the field by effectively handling both labeled data scarcity and transient noise interference while enhancing model robustness through sophisticated temporal dependency modeling and stable feature scale maintenance, ultimately providing a more reliable and practical solution for industrial gearbox fault diagnosis under challenging operational conditions.
Microplastics (MPs), due to their small size, large specific surface area, and strong adsorption, have become ubiquitous and persistent emerging pollutants in water environments, seriously affecting the ecological environment and human health. Constructed wetlands (CWs), as cost-effective and nature-based treatment systems, demonstrate significant potential for retaining and transforming MPs. This review systematically discusses the mechanism of removing MPs from constructed wetlands, including plant retention, substrate adsorption, microbial activities, and faunal-mediated processes, as well as the impact of MPs on constructed wetland systems, MP accumulation within the system can pose ecological risks, such as disrupting nutrient cycling, inhibiting plant growth, and causing secondary pollution. In addition, this review also explores in detail the impact and cycling of MPs on nitrogen and phosphorus removal in CWs, as well as their potential interactions with other pollutants in water environments. Finally, this review proposes directions for future focus and existing issues. This review provides ideas for analyzing the impact of MPs on constructed wetland systems from different perspectives, deepening people's understanding and recognition of new pollutants.
PurposeInnovation hubs have become ubiquitous in the entrepreneurial ecosystems of African countries. However, it remains contested how far they promote start-up development at the micro level and shape the entrepreneurial ecosystem at the macro level, thus necessitating further examination.Design/methodology/approachThis paper draws on institutional theory to explore how innovation hubs promote start-ups and to what extent they develop into key stakeholders in entrepreneurial ecosystems characterized by institutional voids. We followed a qualitative research design and conducted 28 semi-structured interviews with start-ups affiliated with Innohub (Accra) and iHub (Nairobi).FindingsWe find that innovation hubs are key actors of entrepreneurial ecosystems in African countries, helping to overcome institutional voids by providing numerous services, such as access to capital, reliable and cost-effective infrastructure and meaningful events, including training, workshops and coaching, to start-ups. Additionally, they create an internal and external community of like-minded entrepreneurs who strongly benefit from peer-to-peer learning and practical collaborations.Originality/valueWe argue that innovation hubs are institutional intermediaries that help overcome institutional voids in African entrepreneurial ecosystems. At the same time, we challenge the deficit-focused view of institutional voids theory by showing that hubs create hybrid institutional forms rooted in local contexts, serving as sites of institutional emergence. We also link micro-level hub practices to macro-level ecosystem dynamics, offering grounded insights into how innovation hubs shape entrepreneurial ecosystems in African countries.
