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:2019,28(3):179-184
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基于FTRL和XGBoost算法的产品故障预测模型
(南京财经大学 工商管理学院, 南京 210046)
Product Fault Prediction Model Based on FTRL and XGBoost Algorithm
(College of Business Administration, Nanjing University of Finance & Economics, Nanjing 210046, China)
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投稿时间:2018-09-12    修订日期:2018-10-08
中文摘要: 随着智能化设备的日益更新和计算机储存数据能力的提升,制造业企业在其产品制造过程中产生了大量的流水线数据,如何充分利用这些数据一直是工业界的一个难题.本文根据制造业企业的真实大规模生产数据,通过对其进行细致的探索性数据分析,建立了一种基于FTRL和XGBoost算法的二分类产品故障预测模型,并根据适用于非平衡数据集的MCC (Matthews Correlation Coefficient)评价指标采用交叉验证方法对其进行优化.实验结果表明,该模型对于大规模(不仅样本量大,特征量也很大)正负样本非平衡的生产流水线数据集具有运行效率高,故障预测精度高的效果.基于此模型我们可以构建更智能的产品故障检测系统,有效降低企业运营成本的同时也带来了可观的利润增长.
中文关键词: FTRL  XGBoost  故障检测  二分类  大数据
Abstract:With the update of intelligent equipment and the improvement of data storage capacity, manufacturing companies have achieved a large amount of pipeline data in the manufacturing process of their products. How to utilize these data has always been a difficult problem in the industry. Depending on the actual production data of manufacturing enterprises, this study establishes a product failure identification model based on FTRL (with Logistic Regression) and XGBoost algorithms through detailed exploratory data analysis, then uses cross-validation methods to optimize it according to MCC metric which is suitable for unbalanced datasets. The experimental results show that the model has a high efficiency and high accuracy of fault prediction for large-scale (not only large sample size but also large feature quantity) unbalanced production pipeline datasets. Based on this model, we can build a smarter product fault detection system, which effectively reduces the operating costs of the enterprise and also spurs profit growth.
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杨正森.基于FTRL和XGBoost算法的产品故障预测模型.计算机系统应用,2019,28(3):179-184
YANG Zheng-Sen.Product Fault Prediction Model Based on FTRL and XGBoost Algorithm.COMPUTER SYSTEMS APPLICATIONS,2019,28(3):179-184

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