Gearbox Fault Diagnosis Using REMD, EO and Machine Learning Classifiers
参考中译:使用REMD、EO和机器学习分类器进行变速箱故障诊断


          

刊名：Journal of Vibration Engineering & Technologies
作者：Adel Afia（Department of Mechanical and Process Engineering, Houari Boumediene University of Science and Technology）
Fawzi Gougam（Department of Mechanical Engineering, Solid Mechanics and Systems Laboratory (LMSS), University Mhamed Bougara Boumerdes）
Chemseddine Rahmoune（Department of Mechanical Engineering, Solid Mechanics and Systems Laboratory (LMSS), University Mhamed Bougara Boumerdes）
Walid Touzout（Department of Mechanical Engineering, Solid Mechanics and Systems Laboratory (LMSS), University Mhamed Bougara Boumerdes）
Hand Ouelmokhtar（Department of Mechanical Engineering, Solid Mechanics and Systems Laboratory (LMSS), University Mhamed Bougara Boumerdes）
Djamel Benazzouz（Department of Mechanical Engineering, Solid Mechanics and Systems Laboratory (LMSS), University Mhamed Bougara Boumerdes）
刊号：712HA010
ISSN：2523-3920
出版年：2024
年卷期：2024, vol.12, no.3 Pt.2
页码：4673-4697
总页数：25
分类号：TB1
关键词：Fault diagnosis；Gearbox；Feature extraction；Feature selection；Feature classification；Vibration signals
参考中译：故障诊断;齿轮箱;特征提取;特征选择;特征分类;振动信号
语种：eng
文摘：Gearboxes are critical equipment in many industrial applications such as machine manufacturing, petrochemical industry, renewable energy, etc. However, due to their complex structure and regularly harsh working environment, gearboxes are inevitably prone to a variety of faults and defects during operation. Therefore, intelligent condition monitoring techniques are crucially important for early gear and bearing fault recognition and detection to avoid any industrial failure due to machine breakdowns. In this paper, an intelligent algorithm for gear and bearing fault diagnosis is suggested based on several approaches mainly: robust empirical mode decomposition (REMD), time domain features are used for the feature extraction step, while equilibrium optimizer (EO) in the feature selection. For feature classification, random forest (RF), ensemble tree (ET) and nearest neighbors (KNN) are chosen as classifiers. REMD is used to alleviate the mode mixing problem by monitoring the sifting process and selecting the optimal iteration number. EO is a recent optimization approach based on the laws of physical theory in nature. EO reduces the high-dimensional data problem, by filtering redundant features, and increasing model generalization efficiency by avoiding the over-fitting curse. The proposed approach is applied to real-time vibration signals from a healthy gearbox and four different faulty gear and bearing conditions. According to our approach, data signals are decomposed by REMD to several intrinsic mode functions (IMFs). Thereafter, time-domain features are computed for each IMF to construct the feature matrix for every gear and bearing health status. After that, EO is applied to every matrix in the feature selection step. Finally, RF, ET and KNN are used to calculate classification accuracy and give the confusion matrix. Compared to several feature selection techniques, experimental results prove the efficiency of the proposed approach in detecting, identifying, and classifying all gear and bearing defects even under different operating modes.
参考中译：变速箱是机械制造、石油化工、可再生能源等许多工业应用中的关键设备。然而，由于变速箱结构复杂，工作环境恶劣，在运行过程中不可避免地会出现各种故障和缺陷。因此，智能状态监测技术对于齿轮和轴承故障的早期识别和检测至关重要，以避免因机器故障而导致的任何工业故障。本文提出了一种基于几种方法的齿轮轴承故障智能诊断算法：稳健经验模式分解(REMD)，特征提取采用时域特征，特征选择采用平衡优化器(EO)。对于特征分类，选择随机森林(RF)、集成树(ET)和最近邻(KNN)作为分类器。REMD通过监测筛选过程和选择最优迭代次数来缓解模式混合问题。EO是一种基于自然界物理理论定律的最新优化方法。EO通过过滤冗余特征来减少高维数据问题，并通过避免过拟合诅咒来提高模型泛化效率。将该方法应用于一个健康的齿轮箱和四个不同故障齿轮和轴承状态的实时振动信号。根据我们的方法，数据信号被REMD分解成几个固有模式函数(IMF)。之后，计算每个IMF的时域特征，以构造每个齿轮和轴承健康状态的特征矩阵。在此之后，在特征选择步骤中对每个矩阵应用EO。最后，使用RF、ET和KNN计算分类精度，并给出混淆矩阵。实验结果表明，与几种特征选择方法相比，即使在不同的运行模式下，该方法也能有效地检测、识别和分类所有的齿轮和轴承缺陷。



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