Digital Asset Management Framework for Sustainable Energy Infrastructure Monitoring and Lifecycle Optimization
持続可能なエネルギーインフラ監視とライフサイクル最適化のためのデジタルアセット管理フレームワーク (AI 翻訳)
Shadrach Kukuchuku, Rachael Dickson, Tamunotonye Sotonye Ibanibo
🤖 gxceed AI 要約
日本語
本論文は、太陽光や風力などの再生可能エネルギー設備向けに、IoTベースの監視、アセット健全性指標（AHI）モデリング、機械学習による予知保全、ライフサイクル最適化アルゴリズムを統合したデジタルアセット管理フレームワークを提案する。ランダムフォレストやニューラルネットワークを用いて故障を予測し、遺伝的アルゴリズムなどで最適な保守スケジュールを導出する。シミュレーションにより、設備信頼性の向上と保守コスト削減が示された。
English
This paper proposes a Digital Asset Management Framework integrating IoT monitoring, Asset Health Index modelling, ML-based predictive maintenance (Random Forest, ANN, SVM), and lifecycle optimization (Genetic Algorithm, PSO) for renewable energy assets. Simulation results show improved reliability, lower maintenance costs, and reduced downtime.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では太陽光・風力発電の稼働後年数が増加し、効率的な保守管理が課題となっています。本フレームワークは、IoTとAIを活用した予知保全により、再生可能エネルギー設備の信頼性向上と運用コスト削減に貢献できる可能性があります。
In the global GX context
Asset management for renewable energy is a growing global priority as installed capacity scales. This framework integrates IoT, predictive ML, and optimization algorithms to reduce downtime and maintenance costs, supporting the operational efficiency and reliability required for the energy transition.
👥 読者別の含意
🔬研究者:This paper presents a combined IoT-ML-optimization framework for predictive maintenance of renewable assets; researchers can build upon the AHI modelling and algorithm comparison.
🏢実務担当者:Asset managers and O&M teams in renewable energy can leverage the described predictive maintenance approach to reduce unplanned downtime and optimize maintenance schedules.
🏛政策担当者:Policymakers can note the potential for data-driven asset management to improve grid reliability and lower levelized cost of renewables, informing subsidy or regulatory frameworks.
📄 Abstract（原文）
The increasing deployment of renewable energy infrastructure such as solar photovoltaic systems, wind turbines, and smart grid components has created the need for advanced asset monitoring and lifecycle management strategies. Traditional maintenance approaches are often reactive, inefficient, and costly, leading to reduced system reliability and increased operational downtime. This study proposes a Digital Asset Management Framework that integrates Internet of Things (IoT)–based monitoring, Asset Health Index (AHI) modelling, machine learning–based predictive maintenance, and lifecycle optimization algorithms for sustainable energy infrastructure. The framework enables real-time acquisition of operational parameters including temperature, vibration, voltage, and power output from energy assets. These data are processed to compute asset health conditions and predict potential failures using machine learning models such as Random Forest, Artificial Neural Networks, and Support Vector Machines. Furthermore, optimization techniques including Genetic Algorithms and Particle Swarm Optimization are employed to determine optimal maintenance schedules and improve lifecycle performance. Simulation results demonstrate improvements in asset reliability, prediction accuracy, maintenance cost reduction, and system downtime minimization. The proposed framework provides an intelligent and scalable approach for enhancing operational efficiency and supporting sustainable energy infrastructure management.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20773011first seen 2026-06-21 04:26:40 · last seen 2026-06-21 04:31:48
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