@article{https://doi.org/10.1002/joom.1295,

title = {Transition paths for condition-based maintenance-driven smart services},

author = {Henk Akkermans and Rob Basten and Quan Zhu and Luk Van Wassenhove},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/joom.1295},

doi = {https://doi.org/10.1002/joom.1295},

year  = {2024},

date = {2024-01-29},

urldate = {2024-01-29},

journal = {Journal of Operations Management},

volume = {n/a},

number = {n/a},

abstract = {Abstract This research investigates growth inhibitors for smart services driven by condition-based maintenance (CBM). Despite the fast rise of Industry 4.0 technologies, such as smart sensoring, internet of things, and machine learning (ML), smart services have failed to keep pace. Combined, these technologies enable CBM to achieve the lean goal of high reliability and low waste for industrial equipment. Equipment located at customers throughout the world can be monitored and maintained by manufacturers and service providers, but so far industry uptake has been slow. The contributions of this study are twofold. First, it uncovers industry settings that impede the use of equipment failure data needed to train ML algorithms to predict failures and use these predictions to trigger maintenance. These empirical settings, drawn from four global machine equipment manufacturers, include either under- or over-maintenance (i.e., either too much or too little periodic maintenance). Second, formal analysis of a system dynamics model based on these empirical settings reveals a sweet spot of industry settings in which such inhibitors are absent. Companies that fall outside this sweet spot need to follow specific transition paths to reach it. This research discusses these paths, from both a research and practice perspective.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ANBALAGAN20242730,

title = {Assessing Factory's Industry 4.0 Readiness: A Practical Method for IIoT Sensor and Network Analysis},

author = {Sabari Nathan Anbalagan and Melissa Schwarz and Rob Bemthuis and Paul Havinga},

url = {https://www.sciencedirect.com/science/article/pii/S1877050924002679},

doi = {https://doi.org/10.1016/j.procs.2024.02.090},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Procedia Computer Science},

volume = {232},

pages = {2730–2739},

publisher = {Elsevier},

abstract = {Manufacturing industries are aware of the benefits of Industry 4.0 (I4.0) and the pivotal role of the Industrial Internet of Things (IIoT) to facilitate the transition. However, they often underestimate the intricate connection between the implementation of IIoT and their existing sensing and communication systems. Hindering their progress towards I4.0 implementation, many factories still rely on rigid sensing systems, characterized by limited adaptability and inflexibility. This raises questions regarding the industrial readiness for I4.0 implementation and its ability to meet the sensing and connectivity standards of IIoT. Specifically, it is unclear whether the current brownfield installations, consisting of sensors and networking systems, adequately support the demanding I4.0 applications that necessitate a multi-functional and collaborative IIoT system. Our objective is to assess a factory's progress towards I4.0 by examining the readiness of its sensing system and the communication capabilities of its network. We propose a two-stage analysis of factory readiness, including a functional segment-based sensor analysis and an application class-based network analysis. We present a case study conducted at an iron-making plant in The Netherlands to illustrate our method. Key findings of the case study include: (1) a lack of multi-functionality across segments for the majority of sensors (90%), (2) a considerable portion of network traffic (73%) requires high reliability, and (3) only 3% of the current network traffic necessitate ultra-reliable, low latency communication. Furthermore, we discuss how our method provides decision-makers with valuable guidance for the digital transformation of established and newly built manufacturing industries.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{telecom5020019,

title = {Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors},

author = {Eyuel Debebe Ayele and Stylianos Gavriel and Javier Ferreira Gonzalez and Wouter B. Teeuw and Panayiotis Philimis and Ghayoor Gillani},

url = {https://www.mdpi.com/2673-4001/5/2/19},

doi = {10.3390/telecom5020019},

issn = {2673-4001},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Telecom},

volume = {5},

number = {2},

pages = {369–399},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2404-01726,

title = {A Stability-Based Abstraction Framework for Reach-Avoid Control of  Stochastic Dynamical Systems with Unknown Noise Distributions},

author = {Thom S. Badings and Licio Romao and Alessandro Abate and Nils Jansen},

url = {https://arxiv.org/abs/2404.01726},

doi = {https://doi.org/10.48550/arXiv.2404.01726},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {CoRR},

volume = {abs/2404.01726},

abstract = {Finite-state abstractions are widely studied for the automated synthesis of correct-by-construction controllers for stochastic dynamical systems. However, existing abstraction methods often lead to prohibitively large finite-state models. To address this issue, we propose a novel abstraction scheme for stochastic linear systems that exploits the system's stability to obtain significantly smaller abstract models. As a unique feature, we first stabilize the open-loop dynamics using a linear feedback gain. We then use a model-based approach to abstract a known part of the stabilized dynamics while using a data-driven method to account for the stochastic uncertainty. We formalize abstractions as Markov decision processes (MDPs) with intervals of transition probabilities. By stabilizing the dynamics, we can further constrain the control input modeled in the abstraction, which leads to smaller abstract models while retaining the correctness of controllers. Moreover, when the stabilizing feedback controller is aligned with the property of interest, then a good trade-off is achieved between the reduction in the abstraction size and the performance loss. The experiments show that our approach can reduce the size of the graph of abstractions by up to 90% with negligible performance loss.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}