吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (2): 450-457.doi: 10.13229/j.cnki.jdxbgxb20211139
• 车辆工程·机械工程 • 上一篇
基于改进MED⁃SSD的齿轮箱复合故障诊断方法
周杰1,2(
),王云艺2,陈传海1,2(),王立鼎2,3,刘阔3
- 1.吉林大学 数控装备可靠性教育部重点实验室，长春 130022
2.吉林大学 机械与航空航天工程学院，长春 130022
3.大连理工大学 机械工程学院，辽宁 大连 116024
Gearbox complex fault diagnosis method based on improved minimum entropy deconvolution and singular spectrum decomposition
Jie ZHOU1,2(),Yun-yi WANG2,Chuan-hai CHEN1,2(),Li-ding WANG2,3,Kuo LIU3
- 1.Key Laboratory of CNC Equipment Reliability，Ministry of Education，Jilin University，Changchun 130022，China
2.College of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
3.School of Mechanical Engineering，Dalian University of Technology，Dalian 116024，China
摘要：
针对齿轮箱在强噪声环境下复合故障信号微弱、故障特征难以提取等问题，本文提出了一种改进的最小熵反褶积（MED）与奇异谱分解（SSD）结合的方法。首先，构建边际功率谱峰度指数（MPSK），利用MPSK对MED进行参数优化；为弥补SSD的不足，将改进的MED作为SSD的前置滤波器；然后利用相关系数分析法选择有意义的奇异谱分量（SSC）；最后对信号进行频谱分析，确定具体的故障模式。采用仿真信号与齿轮箱试验台的复合故障信号对所提方法进行了应用，验证了方法的有效性和优越性。
中图分类号:
- TH132.41
| 1 | 鄢小安, 贾民平. 基于改进奇异谱分解的形态学解调方法及其在滚动轴承故障诊断中的应用[J]. 机械工程学报, 2017, 53(7): 104-112. |
| Yan Xiao-an, Jia Min-ping. Morphological demodulation method based on improved singular spectrum decomposition and its application in rolling bearing fault diagnosis[J]. Journal of Mechanical Engineering, 2017, 53(7): 104-112. | |
| 2 | Wang Z, Zhou J, Lei Y, et al. Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution[J]. Mechanical Systems and Signal Processing, 2021, 162: 108018. |
| 3 |
张龙, 徐天鹏, 王朝兵, 等. 基于卷积门控循环网络的齿轮箱故障诊断[J]. 吉林大学学报: 工学版. DOI: 10.13229/j.cnki.jdxbgxb20200930.
doi: 10.13229/j.cnki.jdxbgxb20200930 |
|
Zhang Long, Xu Tian-peng, Wang Chao-bing, et al. Gearbox fault diagnosis baed on convolutional gated recurrent network[J]. Journal of Jilin University(Engineering and Technology Edition). DOI:10.13229/j.cnki.jdxbgxb20200930.
doi: 10.13229/j.cnki.jdxbgxb20200930 |
|
| 4 | Wang Z, Zhao W, Du W, et al. Data-driven fault diagnosis method based on the conversion of erosion operation signals into images and convolutional neural network[J]. Process Safety and Environmental Protection, 2021, 149, 591-601. |
| 5 |
陈菲, 杨峥, 张志成, 等. 面向无标签数据的旋转机械故障诊断方法[J]. 吉林大学学报: 工学版. DOI: 10.13229/j.cnki.jdxbgxb20210355.
doi: 10.13229/j.cnki.jdxbgxb20210355 |
|
Chen Fei, Yang Zheng, Zhang Zhi-cheng, et al. Fault diagnosis method of rotating machinery for unlabeled data [J]. Journal of Jilin University (Engineering and Technology Edition). DOI:10.13229/j.cnki.jdxbgxb20210355.
doi: 10.13229/j.cnki.jdxbgxb20210355 |
|
| 6 | Wang Z J, He X X, Yang B, et al. Subdomain adaptation transfer learning network for fault diagnosis of roller bearings[J]. IEEE Transactions on Industrial Electronics, 2021(99): 3108726. |
| 7 | 乔志城, 刘永强, 廖英英. 改进经验小波变换与最小熵解卷积在铁路轴承故障诊断中的应用[J]. 振动与冲击, 2021, 40(2): 81-90, 118. |
| Qiao Zhi-cheng, Liu Yong-qiang, Liao Ying-ying. Application of improved wavelet transform and minimum entropy deconvolution in railway bearing fault diagnosis[J]. Journal of Vibration and Shock, 2021, 40(2): 81-90, 118. | |
| 8 | 陈是扦, 彭志科, 周鹏. 信号分解及其在机械故障诊断中的应用研究综述[J]. 机械工程学报, 2020, 56(17): 91-107. |
| Chen Shi-qian, Peng Zhi-ke, Zhou Peng. Review of signal decomposition theory and its applications in machine fault diagnosis[J]. Journal of Mechanical Engineering, 2020, 56(17): 91-107. | |
| 9 | Pietro B, Joël M, Karel H, et al. Singular spectrum decomposition: a new method for time series decomposition[J]. Advances in Adaptive Data Analysis, 2014, 6(4): 107-109. |
| 10 | Movahedifar M, Yarmohammadi M, Hassani H. Bicoid signal extraction: another powerful approach[J]. Mathematical Biosciences, 2018, 303: 52-61. |
| 11 | Yi C, Lv Y, Ge M, et al. Tensor singular spectrum decomposition algorithm based on permutation entropy for rolling bearing fault diagnosis[J]. Entropy, 2017, 19(4): 19040139. |
| 12 | Endo H, Randall R B. Application of a minimum entropy deconvolution filter to enhance autoregressive model based gear tooth fault detection technique[J]. Mechanical Systems and Signal Processing, 2007, 1(2): 906-919. |
| 13 | 贺志远, 陈果, 何超, 等. 一种MED最优滤波长度选择新方法及其应用[J]. 航空学报, 2020, 41(10): 394-408. |
| He Zhi-yuan, Chen Guo, He Chao, et al. MED optimal filter length selection new method and applications[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(10): 394-408. | |
| 14 | Zhao W, Wang L, Zhang Z. A novel atom search optimization for dispersion coefficient estimation in groundwater[J]. Future Generation Computer Systems, 2019, 91: 601-610. |
| 15 | Mcdonald G L, Zhao Q. Multipoint optimal minimum entropy deconvolution and convolution fix: application to vibration fault detection[J]. Mechanical Systems & Signal Processing, 2017, 82: 461-477. |
| 16 | Li J, Li M, Zhang J. Rolling bearing fault diagnosis based on time-delayed feedback monostable stochastic resonance and adaptive minimum entropy deconvolution[J]. Journal of Sound Vibration, 2017, 401: 139-151. |
| 17 | Liu Z, He Z, Guo W, A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery[J]. ISA Transactions, 2016, 61: 211-220. |
| 18 | Zhang Z, Zhang X, Zhang P, et al. Gearbox composite fault diagnosis method based on minimum entropy deconvolution and improved dual-tree complex wavelet transform[J]. Entropy, 2018, 21(1): 21010018. |
| 19 | He D, Wang X, Li S, et al. Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis[J]. Mechanical Systems and Signal Processing, 2016, 81: 235-249. |
| [1] | 欧阳天成, 陈南, 牛亚峰. 不同粗糙面渐开线齿轮混合弹流润滑[J]. 吉林大学学报(工学版), 2016, 46(6): 1933-1939. |
| [2] | 宋宝玉，解志杰，张锋，王瑞泽，郝明晖，苏代忠. 基于角度域同步平均和阶次分析的低速斜齿轮故障诊断[J]. 吉林大学学报(工学版), 2015, 45(2): 454-459. |
| [3] | 李文良, 王黎钦, 常山, 戴光昊. 齿面摩擦对齿轮系统谐波共振的影响[J]. 吉林大学学报(工学版), 2013, 43(05): 1290-1294. |
| [4] | 洪肇斌, 杨兆军, 张学成, 王佰超. 基于齿面发生线的弧齿锥齿轮铣削加工仿真分析[J]. 吉林大学学报(工学版), 2013, 43(02): 334-339. |
| [5] | 李栎楠, 杨兆军, 王彦鹍, 张学成. 产形线切齿法加工准双曲面齿轮的产形线建模及代用方法[J]. 吉林大学学报(工学版), 2011, 41(增刊1): 127-133. |
|
||