Adaptive separation method for impact features based on multiscale morphological filtering and recursive subtraction
HE Dan1,2,QUAN Wei1,TANG Mingjun3,LIU Hui1
1.College of Mechanical Engineering,Xi’an Polytechnic University,Xi’an 710048,China; 2.Xi’an Municipal Key Lab of Modern Intelligent Textile Equipment,Xi’an 710600,China; 3.School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,China
Abstract：Aiming at the difficult problem that the coupled impact feature extraction and separation in fault diagnosis, a method of adaptive extraction and separation of impact features based on multiscale morphological filtering (MMF) and recursive differencing was proposed. Firstly, the CMFH morphological operator suitable for impact feature separation is selected from the typical combinatorial operators using EA index and frequency response characteristic analysis; Secondly, the periodic impact features are extracted using the CMFH morphological operator and the weighted harmonic-to-noise ratio (WHNR) index; Then, the SOSO technique is used to suppress harmonic interference and white noise to further enhance the periodic impact features; Finally, the cyclic filter is constructed by iterative difference idea to extract and separate the periodic impact features at multiple scales. The simulation data and the analysis results of the traction motor bearing fault data show that the proposed method is better than the latest blind deconvolution (CYCBD) method and the classical spectral kurtosis method in extracting periodic impact under random impacts and harmonic disturbances.
和丹1,2，权伟1，汤明军3，刘晖1. 基于多尺度形态滤波和递归求差的冲击特征自适应分离方法[J]. 振动与冲击, 2024, 43(5): 149-158.
HE Dan1,2,QUAN Wei1,TANG Mingjun3,LIU Hui1. Adaptive separation method for impact features based on multiscale morphological filtering and recursive subtraction. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(5): 149-158.
[1] Li Y, Cheng G, Liu C . Research on bearing fault diagnosis based on spectrum characteristics under strong noise interference[J]. Measurement, 2021, 169(1):108509. [2] Zhang J , Zhong M . Detection for weak fault in planetary gear trains based on an improved maximum correlation kurtosis deconvolution[J]. Measurement Science and Technology, 2020, 31(2):025101 (8pp). [3] Li T, Kou Z, Wu J, et al. Multipoint optimal minimum entropy deconvolution adjusted for automatic fault diagnosis of hoist bearing[J]. Shock and Vibration, 2021, 2021: 1-15. [4] Zhang Q, Pan H, Fan Q, et al. Research on fault extraction method of CYCBD based on seagull optimization algorithm[J]. Shock and Vibration, 2021, 2021: 1-11. [5] 张忠强, 张新, 王家序, 刘治汶. 基于重加权谱峭度方法的航空发动机故障诊断. 航空学报, 2013, 34(8): 1865-1874. Zhang ZQ, Zhang X, Wang JX, Liu ZW. Aero-engine fault diagnosis based on reweighted spectral cliffness method. Journal of Aeronautics, 2013, 34(8): 1865-1874. [6]Miao Y, Zhang B, Lin J, et al. A review on the application of blind deconvolution in machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2022, 163: 108202. [7] Xiao G, Bai Z J, Ching W K. A modified orthogonal matching pursuit for construction of sparse probabilistic boolean networks[J]. Applied Mathematics and Computation, 2022, 424. [8] Yan R , Gao R X , Chen X . Wavelets for fault diagnosis of rotary machines: A review with applications[J]. Signal Processing, 2014, 96:1–15. [9]Feng Z, Zhou Y, Zuo M J, et al. Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples[J]. Measurement, 2017, 103: 106-132. [10] 梁黎明, 王茂芝, 徐文皙,等. 综合斜率和三次样条的EMD端点效应抑制方法[J]. 振动与冲击, 2022, 41(14):7. LIANG Liming, WANG Maozhi, XU Wenxi, et al. A combined slope and cubic spline method for EMD endpoint effect suppression[J]. Vibration and Shock, 2022, 41(14):7. [11]Yongbo L I, Shubin S I, Zhiliang L I U, et al. Review of local mean decomposition and its application in fault diagnosis of rotating machinery[J]. Journal of Systems Engineering and Electronics, 2019, 30(4): 799-814. [12] Zhang X , Miao Q , Zhang H , et al. A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery[J]. Mechanical Systems and Signal Processing, 2018, 108:58-72. [13] Randall R B, Antoni J. Rolling element bearing diagnostics—A tutorial[J]. Mechanical systems and signal processing, 2011, 25(2): 485-520. [14] Li Y, Liang X, Zuo M J . A new strategy of using a time-varying structure element for mathematical morphological filtering[J]. Measurement, 2017, 106:53-65. [15] Chen B, Song D, Zhang W, et al. A performance enhanced time-varying morphological filtering method for bearing fault diagnosis[J]. Measurement, 2021, 176:109163. [16] Duan R, Liao Y, Yang L, et al. Minimum entropy morphological deconvolution and its application in bearing fault diagnosis[J]. Measurement, 2021(5):109649. [17] Wang S, Mei G, Chen B, et al. Fault diagnosis of rolling bearings based on enhanced optimal morphological gradient product filtering[J]. Measurement, 2022. 196. [18] Guo, Junchao, et al. An iterative morphological difference product wavelet for weak fault feature extraction in rolling bearing fault diagnosis.Structural Health Monitoring. 2023, 22.1: 296-318. [19] 苗宝权, 陈长征, 罗园庆,等. 基于自适应增强差分积形态滤波器的滚动轴承故障特征提取方法[J]. 机械工程学报, 2021, 57(9):11. MIAO Baoquan, CHEN Changzheng, LUO Yuanqing, et al. An adaptive augmented differential product morphology filter-based rolling bearing fault feature extraction method[J]. Journal of Mechanical Engineering, 2021, 57(9):11. [20] 鄢小安. 基于数学形态学的滚动轴承故障诊断方法研究[D]. 东南大学，2019. YAN Xiaoan. Research on rolling bearing fault diagnosis method based on mathematical morphology [D]. Southeast University, 2019. [21]Huang H, Baddour N. Bearing vibration data collected under time-varying rotational speed conditions[J]. Data in brief, 2018, 21: 1745-1749. [22] Zeng M , Chen Z. SOSO Boosting of the K-SVD Denoising Algorithm for Enhancing Fault-Induced Impulse Responses of Rolling Element Bearings[J]. IEEE Transactions on Industrial Electronics, 2019:1-1. [23] 李兵, 张培林, 刘东升, 等. 基于自适应多尺度形态梯度变换的滚动轴承故障特征提取[J]. 振动与冲击, 2011, 30(10): 104-108. LI B, ZHANG Pei-Lin, LIU D-S, et al. Fault feature extraction of rolling bearings based on adaptive multi-scale morphological gradient transform[J]. Vibration and shock, 2011, 30(10): 104-108. [24] 吕明珠, 刘世勋, 苏晓明,等. 基于自适应变分模态分解和包络谐噪比的滚动轴承早期退化检测[J]. 振动与冲击, 2021, 40(13):10. LV Mingzhu, LIU Shixun., SU Xiaoming., et al. Early degradation detection of rolling bearings based on adaptive variational modal decomposition and envelope harmonic-to-noise ratio[J]. Vibration and Shock, 2021, 40(13):10. [25] 林云,郭瑜,陈鑫.参数优化最大二阶循环平稳盲解卷积行星轮轴承故障提取[J].振动与冲击,2023,42(02):321-328. LIN Yun,GUO Yu,CHEN Xin. Parameter-optimized maximum second-order cyclic smooth blind deconvolution planetary wheel bearing fault extraction[J]. Vibration and Shock,2023,42(02):321-328. [26] Dang P F, Yang Z X, Wen B G, et al. Fault severity assessment of rolling bearing based on optimized multi-dictionaries matching pursuit and Lempel–Ziv complexity[J]. ISA transactions, 2021,116: 191-202. [27] Rabah A , Abdelhafid K ,Azeddine B, et al. Rolling Bearing Fault Diagnosis Based on an Improved Denoising Method Using the Complete Ensemble Empirical Mode Decomposition and the Optimized Thresholding Operation[J].IEEE Sensors Journal, 2018, 18:7166-7172. [28] Chen H . Compound fault identification of rolling element bearing based on adaptive resonant frequency band extraction[J]. Mechanism and Machine Theory: Dynamics of Machine Systems Gears and Power Trandmissions Robots and Manipulator Systems Computer-Aided Design Methods, 2020, 154(1). [29] Meng J, Wang H, Zhao L, et al. Compound fault diagnosis of rolling bearing using PWK-sparse denoising and periodicity filtering[J]. Measurement, 2021, 181: 109604. [30]王华庆, 王梦阳, 宋浏阳,等. 双约束非负矩阵分解的复合故障信号分离方法[J]. 振动工程学报, 2020, 33(3):7. WANG, H. Q., WANG, M. Y., SONG, L. Y., et al. Double-constrained non-negative matrix decomposition for composite fault signal separation[J]. Journal of Vibration Engineering, 2020, 33(3):7.
