Flanders Make (Belgium)

Out-of-warranty repair of small household appliances remains limited due to high labour costs, spare part barriers, and low consumer willingness to pay for repair. This study introduces the Hobby Repair model, a structured approach that enables skilled individuals to repair products for modest compensation. Using a design-based research methodology, three field trials conducted in Belgium between 2022 and 2026 evaluated four operational sub-models ranging from independent repair to reuse store partnerships. Across all trials, 165 products were processed, achieving a 70% repair success rate and €15,550 in revenue. Results show that organisational configuration strongly influences repair viability: institutionally supported models achieved higher stability, success rates, and financial performance than independent pathways. Results indicate that hobby-based repair can expand repair capacity for low-value appliances that are typically excluded from professional repair markets, providing a scalable complementary pathway within circular economy systems.

The rapid digital transformation of industrial systems in the 21st century has led to an exponential growth in data generated by manufacturing processes and end-user products, particularly in the automotive sector. While this big data creates new opportunities for monitoring and diagnostics, it also introduces significant challenges related to system complexity, scalability, and nonlinearity, as well as the increasing shortage of experienced domain experts. These challenges motivate the adoption of intelligent, automated fault and anomaly detection techniques capable of operating reliably under real-world conditions. The primary objective of this paper is to provide a comprehensive and structured review of the anomaly detection methodologies for automotive applications, with particular emphasis on intelligent fault diagnosis, tolerance, and monitoring architectures. To this end, the paper systematically categorizes existing approaches, including model-based, data-driven, and hybrid techniques, and analyzes their underlying principles, data requirements, computational complexity, and applicability to safety-critical systems. Based on this analysis, the paper highlights current limitations, open research challenges, and emerging trends, including the integration of machine learning and artificial intelligence with domain knowledge and control-oriented frameworks. The main contribution of this work is a unified perspective that supports researchers and practitioners in selecting, designing, and deploying effective anomaly detection solutions for next-generation automotive and cyber-physical systems.

Data-driven innovation has recently changed the mindset in data sharing from centralized architectures and monolithic data exploitation by data providers (data platforms) to decentralized architectures and different data sharing options among all involved participants (data ecosystems). Data sharing is further strengthened through the establishment of several legal frameworks (e.g., European Strategy for Data, Data Act, Data Governance Act) and the emerging initiatives that provide the means to build data ecosystems, which is evident in the formulated communities, established use cases, and the technical solutions. However, the data ecosystems have not been thoroughly studied so far. The differences between the various data ecosystems are not clear, making it hard to choose the most suitable for each use case, negatively impacting their adoption. Since the domain is growing fast, a review of the state-of-the-art data ecosystem initiatives is needed to analyze what each initiative offers, identify collaboration prospects, and highlight features for improvement and open research topics. In this paper, we review the state-of-the-art data ecosystem initiatives, describe their innovative aspects, compare their technical and business features, and identify open research challenges. We aim to assist practitioners in choosing the most suitable data ecosystem for their use cases and scientists to explore emerging research opportunities. Furthermore, we will provide a framework that outlines the key criteria for evaluating these initiatives, ensuring that stakeholders can make informed decisions based on their specific needs and objectives. By synthesizing our findings, we hope to foster a deeper understanding of the evolving landscape of data ecosystems and encourage further advancements in this critical field.

One of the key challenges in enhancing wind turbine reliability and longevity is mitigating tower oscillations, which arise from dynamic wind loads on the rotor and wave-induced excitations at the tower base. This can be achieved using active tower damping techniques. In this study, a model-based approach is used to quantify the impact of active tower dampers on tower oscillations. By focusing on generator torque and pitch angle control, the research explores the coupled dynamics of fore-aft and side-to-side oscillations. A linearized representation of the non-linear system is developed to assess tower oscillations in the full-load operating region. The proposed method quantitatively evaluates the effects of damping controllers directly from the linearised model. The results indicate that implementing fore-aft active tower damping leads to a reduction in tower oscillations in both directions up to 4.7 dB near the tower’s natural frequency. The findings are validated through simulations conducted in OpenFAST, using the 5 MW NREL monopile offshore wind turbine as a case study.

ABSTRACT This paper discusses two algorithms tailored to discrete‐time deterministic finite‐horizon nonlinear optimal control problems or so‐called trajectory optimization problems. Our key aim is to probe the optimization landscape more efficiently during iterations than traditional gradient‐based approaches do. This is achieved by first reformulating the problem as a risk‐sensitive stochastic optimal control (RSOC) and introducing probabilistic policies. The problem can then be cast as an instance of probabilistic optimal control. In turn this allows us to address the problem using the expectation‐maximization (EM) algorithm which produces a fixed‐point iteration of probabilistic policies that converge to the original optimum. These manipulations facilitate an alternative manner to search the original optimization space without affecting the outcome. In practice, we approximate the probabilistic policies using Gaussian linear affine controllers and rely on sigma‐point uncertainty quantification methods to propagate uncertainty through the system dynamics. The proposed algorithms are structurally closest related to the differential dynamic programming algorithm and related methods that use sigma‐point methods to avoid direct gradient evaluations. However, instead of establishing an ad hoc numerical iteration, a principled recursion is established that provably converges to the true optimum. The algorithms feature improved numerical stability and accelerated convergence as is demonstrated through numerical simulations on different nonlinear systems.