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Abstract: The results of the presented research are directly applicable to enhancing the maintenance strategies of transmission system operator (TSO) assets, particularly high-voltage switchyard equipment, and are extendable to other TSO systems. Furthermore, the proposed methodology lays the groundwork for the implementation of predictive maintenance. This paper introduces a novel methodology for assessing the technical condition of high-voltage switchyard devices—specifically, circuit breakers, disconnectors, and instrument transformers—within a high-voltage switching bay. The proposed model integrates multiplicative criteria, which reflect maintenance actions, and additive criteria, which capture the operational and maintenance history of individual devices. Supported by a newly developed data model, the methodology enables an automated assessment process that generates a c-curve representing the condition trajectory of each device or device type. Leveraging real-time data from the maintenance information system, this automated approach allows for the timely evaluation of a device’s technical state prior to scheduled maintenance. The resulting c-curve analysis supports strategic maintenance planning and prioritization. The proposed solutions have been implemented experimentally by TSO ELES.
Keywords: condition-based maintenance, health index, circuit breaker, disconnector, instrument transformer, substation, failure mode analysis
Published in DKUM: 23.06.2025; Views: 0; Downloads: 7
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Abstract: The role of NIMBY (“Not in My Backyard”) phenomena in sustainable urban development and tourism was analyzed, with a focus on stakeholder dynamics and implications for spatial planning. Based on an analysis of worldwide case studies, the main barriers and opportunities presented by these phenomena were identified, including the impact of project preparation time (tp) and benefit realization time (tb ). A mixed methods approach was used, combining qualitative analysis of case studies with quantitative evaluation of project timelines. The results show that effective stakeholder engagement significantly reduces tp, while maximizing tb increases long-term benefits for communities and the environment. This research emphasizes the systemic interdependence of urban planning, environmental sustainability, tourism, and cultural heritage preservation, offering actionable insights for policymakers and developers. The conclusions emphasize the importance of participatory approaches to ensure equitable and sustainable urban transformation.
Keywords: NIMBY, spatial planning, sustainable urban development, sustainable tourism, maintenance, cultural heritage, tourism impact, project preparation, (negative) indirect benefit
Published in DKUM: 02.04.2025; Views: 0; Downloads: 2
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Abstract: Since the implementation of modern approaches in operation maintenance, drivers are expected to be integrated into the entire system of maintenance in order to take over the professional competencies of maintenance workers. For this purpose, an international study was conducted on a sample of 249 truck drivers, with the aim to determine how maintenance in transport companies affects the role of heavy truck drivers in fleet maintenance. Based on the developed SEM model, it was determined that building an efficient maintenance infrastructure in transport companies enables the active participation of truck drivers in the company’s maintenance system. Drivers are capable of repairing minor failures, but only if they are proactive, which will affect their preventive behaviour. This can greatly benefit transport companies, as the results show that the vehicles are utilised more efficiently and in a better technical condition. The results therefore represent an important guideline for improving maintenance in transport companies, developing new competencies of truck drivers and upgrading existing knowledge in the development of modern operation maintenance.
Keywords: maintenance, maintenance strategy, heavy trucks, truck drivers
Published in DKUM: 18.12.2024; Views: 0; Downloads: 6
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Abstract: Software evolution is driven by changes made during software development and maintenance. While source control systems effectively manage these changes at the commit level, the intent behind them are often inadequately documented, making understanding their rationale challenging. Existing commit intent classification approaches, largely reliant on commit messages, only partially capture the underlying intent, predominantly due to the messages’ inadequate content and neglect of the semantic nuances in code changes. This paper presents a novel method for extracting semantic features from commits based on modifications in the source code, where each commit is represented by one or more fine-grained conjoint code changes, e.g., file-level or hunk-level changes. To address the unstructured nature of code, the method leverages a pre-trained transformer-based code model, further trained through task-adaptive pre-training and fine-tuning on the downstream task of intent classification. This fine-tuned task-adapted pre-trained code model is then utilized to embed fine-grained conjoint changes in a commit, which are aggregated into a unified commit-level vector representation. The proposed method was evaluated using two BERT-based code models, i.e., CodeBERT and GraphCodeBERT, and various aggregation techniques on data from open-source Java software projects. The results show that the proposed method can be used to effectively extract commit embeddings as features for commit intent classification and outperform current state-of-the-art methods of code commit representation for intent categorization in terms of software maintenance activities undertaken by commits.
Keywords: software maintenance, code commit, mining software repositories, adaptive pre-training, fine-tuning, semantic code embedding, CodeBERT, GraphCodeBERT, classification, code intelligence
Published in DKUM: 14.08.2024; Views: 87; Downloads: 17
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Keywords: predictive maintenance, autoencoder, anomaly detection, nature-inspired algorithms, optimization, high-performance computing, unsupervised learning
Published in DKUM: 26.01.2024; Views: 447; Downloads: 0