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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: 15
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Abstract: Background: This paper addresses the problem of products’ terminal call rate (TCR) prediction during the warranty period. TCR refers to the information on the amount of funds to be reserved for product repairs during the warranty period. So far, various methods have been used to address this problem, from discrete event simulation and time series, to machine learning predictive models. Objectives: In this paper, we address the above named problem by applying deep learning models to predict terminal call rate. Methods/Approach: We have developed a series of deep learning models on a data set obtained from a manufacturer of home appliances, and we have analysed their quality and performance. Results: Results showed that a deep neural network with 6 layers and a convolutional neural network gave the best results. Conclusions: This paper suggests that deep learning is an approach worth exploring further, however, with the disadvantage being that it requires large volumes of quality data.
Keywords: manufacturing, product lifecycle, management product failure, machine learning, prediction
Published in DKUM: 21.01.2025; Views: 0; Downloads: 6
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Keywords: hypertension, hyperglycemia, embolism, thrombosis, heart failure, vascular disease
Published in DKUM: 22.08.2024; Views: 53; Downloads: 12
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Abstract: In heart failure (HF), acute decompensation can occur quickly and unexpectedly because of worsening of chronic HF or to new-onset HF diagnosed for the first time ('de novo'). Patients presenting with acute HF (AHF) have a poor prognosis comparable with those with acute myocardial infarction, and any delay of treatment initiation is associated with worse outcomes. Recent HF guidelines and recommendations have highlighted the importance of a timely diagnosis and immediate treatment for patients presenting with AHF to decrease disease progression and improve prognosis. However, based on the available data, there is still uncertainty regarding the optimal 'time-to-treatment' effect in AHF. Furthermore, the immediate post-worsening HF period plays an important role in clinical outcomes in HF patients after hospitalization and is known as the 'vulnerable phase' characterized by high risk of readmission and early death. Early and intensive treatment for HF patients in the 'vulnerable phase' might be associated with lower rates of early readmission and mortality. Additionally, in the chronic stable HF outpatient, treatments are often delayed or not initiated when symptoms are stable, ignoring the risk for adverse outcomes such as sudden death. Consequently, there is a dire need to better identify HF patients during hospitalization and after discharge and treating them adequately to improve their prognosis. HF is an urgent clinical scenario along all its stages and disease conditions. Therefore, time plays a significant role throughout the entire patient's journey. Therapy should be optimized as soon as possible, because this is beneficial regardless of severity or duration of HF. Time lavished before treatment initiation is recognized as important modifiable risk factor in HF.
Keywords: heart failure, prognosis, treatment
Published in DKUM: 05.08.2024; Views: 99; Downloads: 9
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Abstract: Rationale: Atrial fibrillation (AF) and heart failure often coexist, but their interaction is poorly understood. Clinical data indicate that the arrhythmic component of AF may contribute to left ventricular (LV) dysfunction. Objective: This study investigates the effects and molecular mechanisms of AF on the human LV. Methods and results: Ventricular myocardium from patients with aortic stenosis and preserved LV function with sinus rhythm or rate-controlled AF was studied. LV myocardium from patients with sinus rhythm and patients with AF showed no differences in fibrosis. In functional studies, systolic Ca2+ transient amplitude of LV cardiomyocytes was reduced in patients with AF, while diastolic Ca2+ levels and Ca2+ transient kinetics were not statistically different. These results were confirmed in LV cardiomyocytes from nonfailing donors with sinus rhythm or AF. Moreover, normofrequent AF was simulated in vitro using arrhythmic or rhythmic pacing (both at 60 bpm). After 24 hours of AF-simulation, human LV cardiomyocytes from nonfailing donors showed an impaired Ca2+ transient amplitude. For a standardized investigation of AF-simulation, human iPSC-cardiomyocytes were tested. Seven days of AF-simulation caused reduced systolic Ca2+ transient amplitude and sarcoplasmic reticulum Ca2+ load likely because of an increased diastolic sarcoplasmic reticulum Ca2+ leak. Moreover, cytosolic Na+ concentration was elevated and action potential duration was prolonged after AF-simulation. We detected an increased late Na+ current as a potential trigger for the detrimentally altered Ca2+/Na+-interplay. Mechanistically, reactive oxygen species were higher in the LV of patients with AF. CaMKII (Ca2+/calmodulin-dependent protein kinase IIδc) was found to be more oxidized at Met281/282 in the LV of patients with AF leading to an increased CaMKII activity and consequent increased RyR2 phosphorylation. CaMKII inhibition and ROS scavenging ameliorated impaired systolic Ca2+ handling after AF-simulation. Conclusions: AF causes distinct functional and molecular remodeling of the human LV. This translational study provides the first mechanistic characterization and the potential negative impact of AF in the absence of tachycardia on the human ventricle.
Keywords: atrial fibrillation, calcium-calmodulin-dependent protein kinase type 2, excitation contraction coupling, heart failure, oxidative stress
Published in DKUM: 28.06.2024; Views: 115; Downloads: 15
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