廖志强,宋雪玮,贾宝柱,尹建川,孔德峰

• 1:广东海洋大学船舶与海运学院/广东省船舶智能与安全工程技术研究中心

摘要(Abstract)：

【目的】滤除振动信号中的背景噪声、增强故障冲击特征，实现船舶推进轴系轴承故障诊断。【方法】提出一种基于加权峭度自适应滤波和对称差分能量谱的信号处理方法：设计加权峭度自适应高通滤波算法，以加权峭度指标作为迭代优选条件，解决传统最佳截止频带选取只能依靠先验知识的缺点；在传统Teager能量算子的基础上引入希尔伯特变换和对称差分求导方式，解调滤波后信号从而得到能量谱。【结果】在仿真实验和工程实验中，振动信号中的背景噪声被滤除，故障冲击更加突出，能正确识别轴承外圈故障、内圈故障及滚动体故障。【结论】加权峭度自适应滤波具有算法复杂度低、运行时间短的优点；对称差分能量谱能够有效提升故障冲击信号的能量占比。本方法能够准确诊断船舶推进轴系轴承故障。

关键词(KeyWords)： 船舶推进轴系;轴承故障诊断;故障特征提升;自适应滤波;对称差分能量谱

Abstract:

Keywords:

基金项目(Foundation): 国家自然基金项目（52201355,52071090）;; 广东省教育厅重点领域资金项目（2020ZDZX3063,2021ZDZX1008）;; 广东海洋大学科研启动经费资助项目

作者(Author): 廖志强,宋雪玮,贾宝柱,尹建川,孔德峰

参考文献(References)：

• [1]祁立波,周瑾,余越,等.磁轴承在船舶推进轴系振动控制中的应用现状与展望[J].船舶力学, 2022, 26(3):448-459.
• [2]赖国军,刘金林,雷俊松,等.船舶推进轴系方案设计的关键技术研究进展[J].中国舰船研究, 2019, 14(5):10-21.
• [3]刘晓健.基于船体结构振动的推进轴系故障诊断方法研究[J].船舶物资与市场, 2021, 29(6):39-42.
• [4] YE X R, HU Y F, SHEN J X, et al. An adaptive optimized TVF-EMD based on a sparsity-impact measure index for bearing incipient fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70:2001009.
• [5]张涵,万振刚.基于压缩感知与VMD的船舶推进轴系轴承振动故障分析[J].舰船电子工程, 2020, 40(1):157-161.
• [6] YUAN Z, PENG T T, AN D, et al. Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method[J]. Journal of Low Frequency Noise, Vibration and Active Control, 2020, 39(4):968-986.
• [7] HAN M H, WU Y M, WANG Y M, et al. Roller bearing fault diagnosis based on LMD and multi-scale symbolic dynamic information entropy[J]. Journal of Mechanical Science and Technology, 2021, 35(5):1993-2005.
• [8] LIANG K X, ZHAO M, LIN J, et al. Maximum average kurtosis deconvolution and its application for the impulsive fault feature enhancement of rotating machinery[J].Mechanical Systems and Signal Processing, 2021, 149:107323.
• [9]张宝,刘波,张郑华,等.基于Peakvue技术的滚动轴承故障诊断[J].计算机系统应用, 2017, 26(3):255-259.
• [10] WANG L, LIU Z W, MIAO Q, et al. Time-frequency analysis based on ensemble local Mean decomposition and fast kurtogram for rotating machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2018, 103:60-75.
• [11] XU X F, QIAO Z J, LEI Y G. Repetitive transient extraction for machinery fault diagnosis using multiscale fractional order entropy infogram[J]. Mechanical Systems and Signal Processing, 2018, 103(15):312-326.
• [12]尹进田,唐杰,刘丽,等.参数同步优化随机共振在牵引传动系统早期微弱故障诊断中的应用[J].振动与冲击,2021, 40(17):234-240.
• [13]赵德尊,王天杨,褚福磊.基于自适应广义解调变换的滚动轴承时变非平稳故障特征提取[J].机械工程学报,2020, 56(3):80-87.
• [14] JIA F, LEI Y G, LIN J, et al. Deep neural networks:a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J].Mechanical Systems and Signal Processing, 2016, 72/73:303-315.
• [15] XIA M, LI T, XU L, et al. Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks[J]. IEEE/ASME Transactions on Mechatronics,2018, 23(1):101-110.
• [16] PEI X L, ZHENG X Y, WU J L. Intelligent bearing fault diagnosis based on Teager energy operator demodulation and multiscale compressed sensing deep autoencoder[J].Measurement, 2021, 179:109452.
• [17] HAN T, LIU Q N, ZHANG L, et al. Fault feature extraction of low speed roller bearing based on Teager energy operator and CEEMD[J]. Measurement, 2019, 138:400-408.
• [18]钟美鹏,袁巨龙,姚蔚峰,等.改进高通滤波的圆柱滚子缺陷图像非线性反锐化掩模[J].轴承, 2018(4):51-54.