Ontology-based approach to support life cycle engineering: Development of a data and knowledge structure

Wilde, A.; Wanielik, F.; Rolinck, Maximilian; Mennenga, Mark; Abraham, Tim; Cerdas, Felipe; Herrmann, Christoph

doi:10.1016/j.procir.2022.02.066

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…The average F1-score of M3, M4, and M5 presented values of 0.67, 0.44, and 0.42, respectively. For this configuration, M3 presented the best results, however, it is important to remark that the anomalies (14)(15)(16)(17)(18)(19) are not detected. In contrast, M4 and M5 detected the faults (14,17,18), though the average scores are lower than M3 scores.…”

Section: E Discussionmentioning

confidence: 90%

“…The knowledge base plays a crucial role in decision assistance systems because it provides the information that supports the user when a (faulty) condition is active [12]. There are different ways to build a knowledge base, namely using ontologies [12] [14] [15], knowledge graphs [11] [16], failure mode and effects analysis (FMEA) and engineering knowledge [13] [17], and knowledge-based frameworks for multi-modal and multi-structured data [18] [19] [20] [21]. Notable examples of knowledge-based frameworks can be found in industrial applications for (prescriptive) maintenance [22] [23], machine condition assessment [13], providing work instructions [24], life cycle engineering [18], product lifecycle management [19] [25], and cyber-physical production systems [26] [27].…”

Section: A Fault Detection and Decision Assistance Systemsmentioning

confidence: 99%

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Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Arévalo,

Alison,

Ibrahim

et al. 2024

IEEE Access

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Fault detection systems support the operator, providing insight during the decision-making while having an (unknown) fault. Data-based models are a common option for a detection system. However, systems that rely purely on data-based models are normally trained with a specific set of data, which cannot necessarily prevent data drift. Thus, an anomaly or unknown condition detection mechanism is required to handle data with new fault cases. Besides, the model's capability to adapt to the unknown condition is equally important to anomaly detection-in other words, its capability to update itself automatically. Alternatively, expert-centered models are powered by the knowledge of operators, which provides the models with production context and expert domain knowledge. The challenge lies in combining both systems and which framework can be used to achieve this fusion. We propose an adaptive information fusion methodology to define fault detection systems using evidence theory and uncertainty quantification. The main contribution of this paper is providing a general framework for the fusion of n number of information sources using the evidence theory. The fusion provides a more robust prediction and an associated uncertainty that can be used to assess the prediction likeliness. Moreover, we provide a methodology for the information fusion of two primary sources: an ensemble classifier based on machine data and an expert-centered model. We demonstrate the information fusion approach using data from an industrial setup, which rounds up the application part of this research. Furthermore, we address the problem of data drift by proposing a methodology to update the data-based models using an evidence theory approach. We validate the approach using the Benchmark Tennessee Eastman while doing an ablation study of the model update parameters.

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Section: E Discussionmentioning

confidence: 90%

Section: A Fault Detection and Decision Assistance Systemsmentioning

confidence: 99%

Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Arévalo,

Alison,

Ibrahim

et al. 2024

IEEE Access

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“…In particular, data on products and assets already in use is critical because it allows for early assessments of an appropriate circular economy strategy at the end of the first life cycle [28]. Ideally, the data acquisition can be achieved through integrated cyber-physical systems [50] or, in other words, an Industry 4.0 (I4.0) application, such as smart products utilising the Internet of Things (IoT) [14]. These technologies enable real-time monitoring and tracking of product usage, performance, and condition.…”

Section: Existing Materials Passport Concepts: Mp Dpp and Cmpmentioning

confidence: 99%

An Approach to Advance Circular Practices in the Maritime Industry through a Database as a Bridging Solution

Okumus,

Gunbeyaz,

Kurt

et al. 2024

Sustainability

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The concept of maritime circularity has gained increasing attention to address challenges arising from the net-zero targets of the maritime industry. The circular economy provides potential solutions to address these challenges through reuse, remanufacturing, and recycling practices. However, the industry faces complex challenges, including inefficient reverse supply chains, a lack of awareness about circular economy principles, standardisation issues, and the need for digital infrastructure to provide vital information in the sector. These challenges prevent the implementation of circularity practices, as access to crucial data throughout the vessel’s life cycle is obstructed. This novel research aims to create a robust first-of-its-kind database solution specifically designed to support the industry’s shift towards circularity. The database will facilitate fast and transparent information flow between the stakeholders, providing foundations for asset tracking and a robust reverse supply chain. A case study was conducted to show that a database could help extract higher financial value from end-of-life ships by over 80%. The ageing fleet increases the urgency of utilising such a database, which could be a pivotal strategy for a sustainable and circular industry. This digital solution offers significant benefits to all industry stakeholders and allows holistic resource management, influencing maritime operations’ sustainability, resilience, and profitability.

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“…Data acquisition is vital to maintain up-to-date passport and to ensure accurate decision support on which circular actions to apply. The acquisition of data along the life cycle is essential and can be achieved through the use of emerging so-called Life Cycle Technologies [12]. Furthermore, wherever data is missing, modelling techniques should be used to fill the data gaps.…”

Section: Product Passports and Product Databasesmentioning

confidence: 99%

“…Efforts have been made to connect the LCA and CAE production databases [18]. Furthermore, efforts , based on ontologies, have been made to automate process selection given a certain product state and possible circular strategies [12], [19]. These efforts can be utilized as an example to connect the digital product passport database to the ecoinvent database.…”

Section: Process and Technologies Databasesmentioning

confidence: 99%

Circularity and LCA - material pathways: the cascade potential and cascade database of an in-use building product

Schaubroeck

Dewil

Allacker

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Improving circularity in the building sector entails ensuring greater material efficiency to avoid virgin material extraction. To assist stakeholders in decisions regarding salvaging an in-use building product, the potential further uses of this salvaged product and its materials should be determined. In other words, its cascade potential should be determined given circular strategies, i.e. to reuse, repurpose, repair sequentially. To predict the cascade potential, a database is required that combines (acquired) product data, assembly characteristics and process data, e.g., info on the process to melt old steel. This database is referred to as a cascade database. The aim of this research is to study steps to form this database, specifically, the combination of product passports and an LCA process database. Hereto, major existing European data tools are considered. For product data, material passports and digital product passports initiatives are identified and compared. For process data, LCA databases are considered. Interactions between these data tools are a possible way to set up a partial cascade database. A workflow is then described- on how to acquire data on the cascade potential of a product from such a database. We note that the cascade database also relies on extensive (economic) forecast models, data collection and stakeholder interaction. Setting up such an extensive database and generating all possible cascading paths is a challenging, if not unachievable, feat. Nevertheless, even limited applications of this database might help reach a more circular economy.

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Ontology-based approach to support life cycle engineering: Development of a data and knowledge structure

Cited by 11 publications

References 24 publications

Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

An Approach to Advance Circular Practices in the Maritime Industry through a Database as a Bridging Solution

Circularity and LCA - material pathways: the cascade potential and cascade database of an in-use building product

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