基于改进型级联宽度学习的采煤机截割部齿轮箱故障诊断  

Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning

作　　者：李鑫 李淑华 陈浩 司垒[1] 魏东 邹筱瑜 LI Xin;LI Shuhua;CHEN Hao;SI Lei;WEI Dong;ZOU Xiaoyu(School of Mechanical and Electrical Engineering,China University of Mining and Technology,Xuzhou 221116,China;National Key Laboratory of Intelligent Mining Equipment Technology,Xuzhou 221116,China)

机构地区：[1]中国矿业大学机电工程学院,江苏徐州221116 [2]智能采矿装备技术全国重点实验室,江苏徐州221116

出　　处：《工矿自动化》2025年第3期86-95,共10页Journal Of Mine Automation

基　　金：国家自然科学基金项目(52404178);江苏省自然科学基金项目(BK20231064)。

摘　　要：采煤机截割部齿轮箱振动监测数据结构复杂,且易出现类别不平衡问题,导致现有基于传统机器学习的智能故障诊断方法易出现错报现象,而基于深度学习的诊断方法模型结构复杂、学习效率低,且易陷入局部最优解,影响诊断性能。针对上述问题,提出了一种基于改进型级联宽度学习(ICBL)的采煤机截割部齿轮箱故障诊断方法。在ICBL模型的特征节点中引入随机超图卷积机制,充分挖掘采煤机截割部齿轮箱振动数据的复杂多元结构信息,增强故障特征表征能力;采用类特异性权重分配策略,根据输入数据的类间比例信息,为少数类样本赋予更高权重,提高不平衡数据下采煤机截割部齿轮箱故障诊断性能。利用采煤机截割部齿轮箱故障模拟实验台验证基于ICBL的采煤机截割部齿轮箱故障诊断方法的有效性,结果表明该方法能够有效增强故障特征的判别性,在数据不平衡度为15时诊断精度达94.52%,单一样本的故障识别耗时为0.284 ms,优于级联宽度学习系统、加权宽度学习系统、多尺度卷积神经网络、超图神经网络、多分辨率超图卷积网络等。The vibration monitoring data of the shearer cutting unit gearbox has a complex structure and is prone to class imbalance issues,leading to frequent false positives in traditional machine learning-based fault diagnosis methods.Meanwhile,deep learning-based approaches often suffer from complex model structures,low learning efficiency,and susceptibility to local optima,negatively impacting diagnostic performance.To address these issues,a fault diagnosis method was proposed for the shearer cutting unit gearbox based on improved cascaded broad learning(ICBL).A random hypergraph convolution mechanism was introduced into the feature nodes of the ICBL model to fully exploit the complex multivariate structural information in the vibration data of the shearer cutting unit gearbox,thereby enhancing the representation of fault features.Additionally,a classspecific weight allocation strategy was adopted to assign higher weights to minority class samples based on the class distribution of the input data,improving fault diagnosis performance under imbalanced data conditions.The effectiveness of the ICBL-based fault diagnosis method was validated using a shearer cutting unit gearbox fault simulation test platform.Experimental results demonstrated that the proposed method effectively enhanced the discriminability of fault features,achieving a diagnostic accuracy of 94.52% when the data imbalance ratio was 15,with a fault recognition time of 0.284 ms per sample.The method outperformed cascaded broad learning systems,weighted broad learning systems,multi-scale convolutional neural networks,hypergraph neural networks,and multi-resolution hypergraph convolutional networks,demonstrating significant engineering application value.

关 键 词：采煤机截割部 齿轮箱 故障诊断 级联宽度学习 随机超图卷积 类特异性权重 

分 类 号：TD67[矿业工程—矿山机电]