Enhanced Helicopter Vibration Prediction With Hybrid Sampling and Cost Mining Techniques

IF 3.6 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jeonghun Kim;Keunho Choi;Donghee Yoo
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Abstract

Helicopter vibrations increase pilot workload and accelerate fatigue and wear in structural and mechanical components, potentially resulting in higher maintenance costs and reduced operational safety. To address these challenges, this study develops a machine learning-based prediction model using vibration test data from the cockpit of a Korean utility helicopter. To mitigate the issue of class imbalance in the dataset, two hybrid sampling techniques are proposed and analyzed: first oversampling and last undersampling (FOLU) and first undersampling and last oversampling (FULO). In addition to conventional evaluation based on prediction accuracy, this study adopts a cost-aware perspective by applying both cost-insensitive and cost-sensitive learning frameworks. The models are compared in terms of misclassification-related cost losses under realistic operational conditions. Experimental results confirm that the proposed hybrid sampling methods outperform traditional oversampling and undersampling techniques in prediction performance. Among all configurations, the FULO-based models using deep neural network (DNN) and random forest (RF) achieved the highest prediction accuracy. Moreover, cost-sensitive learning generally reduced misclassification losses compared to cost-insensitive learning; however, in certain cases, the cost-insensitive model yielded lower total costs. These findings indicate that predictive model selection should not be based solely on accuracy metrics, but also on economic efficiency within operational contexts. This study contributes to the literature by demonstrating the practical effectiveness of hybrid sampling in helicopter vibration prediction as well as introducing a cost-aware model evaluation framework suitable for prognostics and health management (PHM) applications in military and civilian rotorcraft operations.
基于混合采样和成本挖掘技术的直升机振动预测
直升机振动增加了飞行员的工作量,加速了结构和机械部件的疲劳和磨损,可能导致更高的维护成本,降低了操作安全性。为了应对这些挑战,本研究利用韩国通用直升机驾驶舱的振动测试数据开发了一种基于机器学习的预测模型。为了缓解数据集中的类不平衡问题,提出并分析了两种混合采样技术:第一次过采样和最后一次欠采样(FOLU)和第一次欠采样和最后一次过采样(FULO)。除了传统的基于预测准确性的评估之外,本研究采用了成本意识的视角,同时应用了成本不敏感和成本敏感的学习框架。在实际操作条件下,比较了这些模型与误分类相关的成本损失。实验结果表明,混合采样方法在预测性能上优于传统的过采样和欠采样技术。在所有配置中,基于深度神经网络(DNN)和随机森林(RF)的fuo模型的预测精度最高。此外,与成本不敏感学习相比,成本敏感学习总体上减少了误分类损失;然而,在某些情况下,成本不敏感模型产生的总成本较低。这些发现表明,预测模型的选择不应仅仅基于准确性指标,还应基于操作环境中的经济效率。本研究通过展示混合采样在直升机振动预测中的实际有效性,以及引入适用于军用和民用旋翼机操作的预测和健康管理(PHM)应用的成本意识模型评估框架,为文献做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
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