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基于随机森林方法的柴油机涡轮增压器故障诊断
Fault diagnosis of diesel engine turbocharger based on random forest method
- DOI:
- 作者:
- 贾哲宇, 温华兵, 朱军超, 赵震宇
JIA Zhe-yu, WEN Hua-bing, ZHU Jun-chao, ZHAO Zhen-yu
- 作者单位:
- 江苏科技大学 能源与动力学院, 江苏 镇江 212003
School of Energy and Power, Jiangsu University of Science and Technology, Zhenjiang 212003, China
- 关键词:
- 涡轮增压器;故障诊断;随机森林
turbocharger; fault diagnosis; random forest
- 摘要:
- 为了提高柴油机故障诊断的精度，针对柴油机涡轮增压器故障的问题，提出基于随机森林的柴油机涡轮增压器故障诊断方法。使用AVL Boost对柴油机建立故障仿真模型，并生成故障样本。对构建的涡轮增压器模型使用随机森林方法诊断。结果表明，采用随机森林算法的故障诊断模型可以有效对涡轮增压器的故障进行分类，分类准确率超过95%。可知，随机森林方法在涡轮增压器故障诊断领域中有良好的应用价值。
In order to improve the accuracy of diesel engine fault diagnosis, aiming at the problem of diesel engine turbocharger fault, a diesel engine turbocharger fault diagnosis method based on random forest is proposed. AVL Boost is used to establish the fault simulation model of diesel engine and generate fault samples. The turbocharger model is diagnosed by random forest method. The results show that the fault diagnosis model using random forest algorithm can effectively classify the faults of turbocharger, and the classification accuracy is more than 95%. Therefore, the random forest method has good application value in the field of turbocharger fault diagnosis.
2023,45(6): 109-113 收稿日期：2022-01-30
DOI：10.3404/j.issn.1672-7649.2023.06.020
分类号：U664.121.2
作者简介：贾哲宇(1997-)，男，硕士研究生，研究方向为柴油机故障诊断
