用于液压系统预测性维护的集合方法和多输出分类器比较研究

IF 6 Q1 ENGINEERING, MULTIDISCIPLINARY Results in Engineering Pub Date : 2024-09-19 DOI:10.1016/j.rineng.2024.102900
Hassan N. Noura , Thomas Chu , Zaid Allal , Ola Salman , Khaled Chahine
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引用次数: 0

摘要

从制造工艺到重型机械操作,液压系统的维护和保养在各种工业应用中发挥着举足轻重的作用。传统的策略往往面临很大的局限性,特别是在干预时间和故障发生与维护之间的延迟成本方面。在过去的几十年里,主动式策略已经崭露头角,并具有广阔的发展前景,这主要归功于它们的预测能力。这些积极主动的方法旨在预测故障和维护需求,从而降低与被动方法相关的成本和运营中断。本研究探讨了单输出、集合方法以及多输出分类器集成在数据相对有限的液压预测性维护问题中的适用性。首先,使用皮尔逊相关系数分析数据,然后使用递归特征提取法进行特征提取,旨在优化预测模型的性能,特别是随机森林和 CatBoost。结果表明,融合了 LightGBM、XGBoost、CatBoost 和随机森林的堆叠集合方法取得了最显著的改进,最终准确率达到 98.63%。本研究的结果表明,单输出模型、集合方法和多输出模型在预测液压系统健康状况方面都有令人满意的表现。此外,将单一输出、集合方法和多输出分类器集成在一起,创建了一个相对可靠和强大的预测性维护系统。
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A comparative study of ensemble methods and multi-output classifiers for predictive maintenance of hydraulic systems
The maintenance and upkeep of hydraulic systems play a pivotal role in various industrial applications, from manufacturing processes to heavy machinery operations. Traditional strategies often face significant limitations, particularly concerning intervention time and the costs associated with delays between fault occurrence and maintenance. Over the past decades, proactive strategies have emerged with promising potential, primarily due to their predictive capabilities. These proactive approaches aim to anticipate faults and maintenance needs, thereby mitigating costs and operational disruptions associated with reactive approaches. This study explores the applicability of single output, ensemble methods, and the integration of multi-output classifiers in hydraulic predictive maintenance problems with relatively limited data. First, data is analyzed using Pearson correlation coefficients, and then feature extraction is conducted using recursive feature extraction, aiming to optimize the performance of predictive models, particularly Random Forest and CatBoost. Results show that the stacking ensemble method, incorporating LightGBM, XGBoost, CatBoost, and Random Forest, yields the most notable improvement, achieving a final accuracy of 98.63%. The results obtained in this study show satisfying performances for single-output models, ensemble methods, and multi-output models in predicting the health of hydraulic systems. Moreover, combining single output, ensemble methods, and the integration of multi-output classifiers has created a relatively reliable and robust predictive maintenance system.
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来源期刊
Results in Engineering
Results in Engineering Engineering-Engineering (all)
CiteScore
5.80
自引率
34.00%
发文量
441
审稿时长
47 days
期刊最新文献
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