Research on fault diagnosis of rolling bearing based on ALIF and TMFDE
-
摘要: 为了提高滚动轴承的故障识别精度，提出了一种基于自适应局部迭代滤波（ALIF）和时移多尺度波动散布熵（TMFDE）的故障诊断方法。首先，利用ALIF对滚动轴承振动信号进行分解，获得一组IMF分量。其次，为了获得更集成的IMF分量，基于能量法评估各IMF分量的重要性，将前3阶分量视为有效分量。接着，利用TMFDE量化有效分量中的特征信息，构建故障特征向量。最后，将故障特征输入至粒子群优化的极限学习机中进行故障识别。利用东南大学的滚动轴承数据对该方法进行了评估，结果表明该方法能够准确地识别故障的类型，与其他方法相比，该方法在数据量较少时仍然具有优异的稳定性。
-
关键词:
- 自适应局部迭代滤波 /
- 时移多尺度波动散布熵 /
- 能量法 /
- 滚动轴承 /
- 故障检测
Abstract: In order to improve the fault identification accuracy of rolling bearing, a fault diagnosis method based on adaptive local iterative filtering (ALIF) and time-shifted multi-scale fluctuation dispersion entropy(TMFDE) was proposed. Firstly, the vibration signal of rolling bearing was decomposed by ALIF to obtain a set of IMF components. Secondly, to obtain more integrated IMF components, the importance of each IMF component was evaluated based on the energy method, and the first three components were regarded as effective components. Then, the TMFDE was used to quantify the feature information in the effective component and construct the fault feature vector. Finally, the fault features were input into the extreme learning machine optimized by particle swarm optimization for fault identification. The method was tested by using the rolling bearing data of Southeast University. The results show that the method can accurately identify the type of fault, and compared with other methods, this method still has excellent stability when the amount of data is small. -
表 1 滚动轴承和齿轮箱故障类型介绍
故障部位 类型(简写) 描述 训练/测试
样本标签 滚动轴承 滚动体(BF) 滚动体出现裂纹 40/40 1 复合(CMF) 内圈与外圈同时出现裂纹 40/40 2 正常(N) / 40/40 3 内圈(IRF) 内圈出现裂纹 40/40 4 外圈(ORF) 外圈出现裂纹 40/40 5 
下载: 导出CSV
表 3 不同方法与EMD相结合的准确率
（%） 方法 最大准确率 最小准确率 平均准确率 TMFDE 88 83 86.4 TMDE 85.5 80.5 82.7 MFDE 82 74 78.4 MDE 76.5 71 73.7
下载: 导出CSV
-
[1] Lei Y G, Lin J, He Z J, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery[J]. Mechanical Systems & Signal Processing, 2013, 35: 108-126. [2] Mogal S P, Lalwani D I. A brief review on fault diagnosis of rotating machineries[J]. Applied Mechanics & Materials, 2014, 541-542: 635-640. [3] 李永军, 马立元, 崔心瀚. 基于多尺度模糊熵的旋转机械故障诊断研究[J]. 现代制造工程, 2017(10): 146-150. [4] 曲全鹏, 曲海军, 张强. 基于VMD-MDE的柱塞泵磨损故障诊断研究[J]. 机电工程, 2021, 38(9): 1202-1206. [5] 王勉, 刘勇. 基于时移多尺度散布熵和SVM的滚动轴承故障诊断方法[J]. 机械设计与研究, 2021, 37(5): 83-87. [6] 张凡, 孙文磊, 王宏伟, 等. 改进蝙蝠算法优化支持向量机的故障诊断方法[J/OL]. 机械科学与技术: 1-9 [2022-06-17]. [7] Azami H, Arnold S E, Sanei S, et al. Multiscale fluctuation-based dispersion entropy and its applications to neurological diseases[J]. IEEE Access, 2019, 7: 68718-68733. doi: 10.1109/ACCESS.2019.2918560 [8] Wang Z Y, Li G S, Yao L G, et al. Data-driven fault diagnosis for wind turbines using modified multiscale fluctuation dispersion entropy and cosine pairwise-constrained supervised manifold mapping[J]. Knowledge-Based Systems, 2021, 228: 107276. doi: 10.1016/j.knosys.2021.107276 [9] 苟先太, 李昌喜, 金炜东. VMD多尺度熵用于高速列车横向减振器故障诊断[J]. 振动. 测试与诊断, 2019, 39(2): 292-297. [10] 刘晓波. 基于ALIF和FWEO的滚动轴承故障特征提取方法[J]. 南昌航空大学学报:自然科学版, 2019, 33(3): 25-31,40. [11] 周永强, 卜文绍. 电机轴承故障的多尺度排列熵特征提取与GK识别[J]. 组合机床与自动化加工技术, 2021(4): 70-74. [12] 陈保家, 汪新波, 赵春华, 等. 基于自适应局部迭代滤波和能量算子解调的滚动轴承故障特征提取[J]. 南京理工大学学报, 2018, 42(4): 445-452. [13] 赵志炉, 李德忠, 戴勇峰, 等. ALIF-PE算法在水轮机压力脉动信号降噪中的应用[J]. 人民长江, 2020, 51(S2): 285-289. [14] 韩美东, 张金豹, 赵永强. ALIF-MMPE结合DAG-SVM的滚动轴承故障诊断[J]. 机械科学与技术, 2020, 39(9): 1358-1365. [15] Azami H, Escudero J. Amplitude and fluctuation based dispersion entropy[J]. Entropy, 2018, 20(3): 210. doi: 10.3390/e20030210 [16] 姜万录, 赵亚鹏, 张淑清, 等. 精细复合多尺度波动散布熵在液压泵故障诊断中的应用[J]. 振动与冲击, 2022, 41(8): 7-16. [17] 张玉学, 潘宏侠. 基于LMD近似熵和PSO-ELM的齿轮箱故障诊断[J]. 机械传动, 2017, 41(8): 109-113. [18] 刘云斌, 钱俊, 潘曙明. 基于RCMDE与极限学习机的滚动轴承故障诊断[J]. 制造技术与机床, 2023(2): 123-126. [19] Zheng L K, He Y, Chen X H. Research on a fault diagnosis method for rolling bearing based on improved multiscale range entropy and hierarchical prototype[J]. Measurement Science and Technology, 2021, 32(9): 095003. doi: 10.1088/1361-6501/abfbaa -
下载:
点击查看大图
图(9) / 表(3)
计量
- 文章访问数: 167
- HTML全文浏览量: 43
- PDF下载量: 706
- 被引次数: 0
