振動ベース予知保全による遠心ポンプ故障の炭素フットプリント定量化：べき乗則モデル
Quantifying Carbon Footprint of Centrifugal Pump Faults via Vibration-Based Predictive Maintenance: A Power-Law Model (原題)
Ali Abdulhussain Naeem, Ahmed Naser Hasan
🤖 gxceed AI 要約
日本語
本研究は、遠心ポンプの4種の機械的故障（不均衡・ミスアライメント・軸受劣化・緩み）による損失動力をべき乗則モデルで推定し、炭素フットプリントへの影響を定量化した。イラクの電力排出係数（0.66 kg CO₂/kWh）を用い、予知保全導入で年間1,408 kWhの省エネと故障起因排出の94%削減を試算。保守スケジューリング向けに炭素閾値を統合した新指標MEEIを提案する。
English
This study quantifies the carbon footprint of four mechanical faults in centrifugal pumps using a power-law model (R²=0.97) built on open-source CIRA/NASA datasets. Applying Iraq's emission factor (0.66 kg CO₂/kWh), predictive maintenance yielded 1,408 kWh/yr savings per 110 kW pump and a 94% cut in fault-related emissions. A novel Mechanical Environmental Efficiency Index (MEEI) links operational efficiency to carbon thresholds for maintenance scheduling.
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
日本企業の工場・プラントにおける設備保全とScope1/2削減の接点を示す。省エネ法・GXリーグ文脈で、予知保全を炭素管理指標（MEEI）として統合する視点は、国内製造業の脱炭素投資判断に示唆を与える。
In the global GX context
Adds to the growing literature linking maintenance/asset management to Scope 1/2 accounting, offering a low-cost, open-data method for quantifying fault-driven emissions. Relevant to ISSB/CSRD disclosure of operational energy efficiency and to transition finance cases for industrial decarbonization in high-carbon-intensity regions.
👥 読者別の含意
🔬研究者:機械故障と炭素排出を結ぶ定量モデルと新指標MEEIの妥当性・一般化可能性を検証する価値がある。
🏢実務担当者:予知保全投資の省エネ・CO₂削減効果を試算し、保全計画に炭素閾値を組み込む実務的枠組みとして活用できる。
🏛政策担当者:高炭素集約地域の産業脱炭素政策において、低コストな保全改善による排出削減ポテンシャルを示す参考事例となる。
📄 Abstract（原文）
Despite growing interest in predictive maintenance as an energy-efficiency strategy, no validated low-cost framework existed for quantifying the carbon footprint implications of mechanical faults in industrial pumps, particularly within high-carbon-intensity contexts such as Iraq's oil sector. This research aimed to measure the impact of implementing Smart Predictive Maintenance (PdM) on reducing the carbon footprint and improving the operational efficiency of centrifugal pumps through the analysis of open-source datasets (CIRA and NASA). The lost power associated with four primary mechanical faults (unbalance, misalignment, bearing degradation, and mechanical looseness) was calculated using a mathematical power-law model derived from the data (R² = 0.97 on an independent test set, MAE = 0.33 kW, RMSE = 0.38 kW). The incremental carbon footprint was subsequently estimated using Iraq's specific electricity emission factor (0.66 kg CO₂/kWh). Under plausible baseline assumptions (where faults constituted approximately 25% of operating time), the results indicated that implementing predictive maintenance achieved an energy saving of 1,408 kWh per year per pump (110 kW capacity) and reduced the incremental carbon footprint by 94% (a saving of 0.087 metric tons CO₂ per year). A novel Mechanical Environmental Efficiency Index (MEEI) was proposed, integrating operational efficiency with carbon footprint thresholds for maintenance scheduling. The study concluded that utilizing open-source data provided a practical, low-cost alternative for preliminary assessments, and that the methodology could be generalized to the Iraqi oil sector pending field validation.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.26389/ajsrp.a220426first seen 2026-09-17 04:57:52
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