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金属矿山 ›› 2023, Vol. 52 ›› Issue (09): 156-163.

• 机电与自动化 • 上一篇    下一篇

基于 IFOA-RotGBM 的矿用挖掘机发动机故障诊断

顾清华1,2 孙文静1,2 李学现2,3
  

  1. 1. 西安建筑科技大学资源工程学院, 陕西 西安 710055;2. 西安市智慧工业感知计算与决策重点实验室, 陕西 西安 710055;3. 西安建筑科技大学管理学院,陕西 西安 710055
  • 出版日期:2023-09-15 发布日期:2023-11-03
  • 基金资助:
    国家自然科学基金项目(编号:52074205);陕西省自然科学基础研究计划项目(编号:2020JC-44)。

Fault Diagnosis of Mining Excavator Engine Based on IFOA-RotGBM

GU Qinghua1,2 SUN Wenjing1,2 LI Xuexian2,3#br#   

  1. 1. School of Resources Engineering,Xi′an University of Architecture and Technology,Xi′an 710055,China;2. Xi′an Key Laboratory for Intelligent Industrial Perception,Calculation and Decision,Xi′an 710055,China;3. School of Management,Xi′an University of Architecture and Technology,Xi′an 710055,China
  • Online:2023-09-15 Published:2023-11-03

摘要: 针对矿山挖掘机发动机工作机理复杂、故障诊断效率低且精度不高的问题,提出了一种基于 IFOA 优化 RotGBM 的矿用挖掘机发动机故障诊断方法。 首先利用随机森林-递归特征消除法(RF-RFE)对采集的挖掘机发动机 故障数据进行特征提取,剔除冗余不相关特征;其次提出了一种改进的果蝇优化算法(IFOA)对 LightGBM 进行超参数 寻优;然后融合旋转森林和 LightGBM 生成 RotGBM,构建了新的故障诊断模型;最后利用某矿山挖掘机发动机故障数 据对模型进行了验证,并与其他常用方法进行了性能对比分析。 仿真结果表明:所提方法的诊断性能优于其他诊断 方法,能达到 98. 31%的诊断精度,0. 22%的误报率和 2. 5%的漏检率,满足矿山挖掘机发动机的故障诊断要求。

关键词: 矿用挖掘机发动机, 故障诊断, 旋转森林, LightGBM, FOA

Abstract: Aiming at the complex working mechanism,low fault diagnosis efficiency and low accuracy in mining excavator engine,a fault diagnosis method of mining excavator engine based on RotGBM optimized by IFOA is proposed. Firstly,the random forest-recursive feature elimination method (RF-RFE) is utilized to extract the features from the fault data of excavator engine and eliminate redundant irrelevant features. Secondly,an improved fruit fly optimization algorithm ( IFOA) is proposed to optimize the hyper-parameter of LightGBM. Then,RotGBM is generated by combining Rotation Forest and LightGBM,and a new fault diagnosis model is constructed. Finally,the fault data of a mine excavator engine is used to verify the model,and the performance is compared with other commonly used methods. The simulation results show that the proposed method has better diagnostic performance than other diagnosis methods,and can reach 98. 31% diagnosis accuracy,0. 22% false positive rate and 2. 5% missed detection rate,which meets the requirements of fault diagnosis of mine excavator engine.

Key words: mining excavator engine,fault diagnosis,rotation forest,LightGBM,FOA