基于NIC-DWT-WOASVM的齿轮箱混合故障诊断

张鑫1,赵建民1,李海平1,倪祥龙2,孙富成2

振动与冲击 ›› 2020, Vol. 39 ›› Issue (11) : 146-151.

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (11) : 146-151.
论文

基于NIC-DWT-WOASVM的齿轮箱混合故障诊断

  • 张鑫1,赵建民1,李海平1,倪祥龙2,孙富成2
作者信息 +

Compound fault diagnosis for gearbox based on NIC-DWT-WOASVM

  • ZHANG Xin1, ZHAO Jianmin1, LI Haiping1, NI Xianglong2, SUN Fucheng2
Author information +
文章历史 +

摘要

由于齿轮箱中振动信号的复杂性和非平稳性,致使齿轮箱混合故障诊断工作具有一定难度。针对这一问题提出基于NIC-DWT-WOASVM的齿轮箱混合故障诊断方法。首先通过窄带干扰消除(Narrow Band Interference Canceller, NIC)滤除原始信号中齿轮啮合和转轴等窄带干扰信号,接着对信号进行离散小波变换(Discrete Wavelet Transform, DWT),重构小波系数得到小波分量,提取分量的方差作为特征参数构成特征矩阵样本。针对传统优化支持向量机收敛速度慢及容易局部最优等问题,提出鲸鱼算法优化的支持向量机(Whale Optimization Algorithm Support Vector Machine, WOASVM),运用训练样本对WOASVM进行训练得到优化分类模型,将测试样本输入到优化模型中得到诊断结果。为验证方法的有效性,开展了变工况下齿轮箱混合故障实验,通过实验分析及与其他方法的比较,证明方法对于齿轮箱混合故障诊断是有效的。

Abstract

The compound fault diagnosis of gearbox is challenging because of its complexity and non-stationarity of the vibration signal. In this work, a novel hybrid method based on narrow band interference canceller (NIC), discrete wavelet transform (DWT) and support vector machine optimized by whale optimization algorithm (WOASVM) is presented for compound fault diagnosis of gearbox. Firstly, the raw signal is processed by NIC to filter the deterministic signal which interfere the fault signal. Then the signal is processed by discrete wavelet transform, the wavelet coefficients are reconstructed and the variances of the wavelet components are calculated as the characteristic parameters. Aiming at the problems of slow convergence speed and easy local optimization of traditional optimized support vector machines, the WOASVM is proposed for fault pattern recognition. The training samples is used to train WOASVM and obtain the optimized classification model, and input the test samples into the optimization model to get the diagnosis results. In order to verify the effectiveness of the proposed method, a compound fault experiment of gearbox under variable conditions is carried out. Via experimental analysis and comparison with other methods, it is proved that the method is effective for the compound fault diagnosis of gearbox.

关键词

齿轮箱 / 混合故障诊断 / 窄带干扰消除 / 离散小波变换 / WOASVM

Key words

Gearbox / Compound fault diagnosis / Narrow band interference canceller / Discrete wavelet transform / WOASVM

引用本文

导出引用
张鑫1,赵建民1,李海平1,倪祥龙2,孙富成2. 基于NIC-DWT-WOASVM的齿轮箱混合故障诊断[J]. 振动与冲击, 2020, 39(11): 146-151
ZHANG Xin1, ZHAO Jianmin1, LI Haiping1, NI Xianglong2, SUN Fucheng2. Compound fault diagnosis for gearbox based on NIC-DWT-WOASVM[J]. Journal of Vibration and Shock, 2020, 39(11): 146-151

参考文献

[1] 秦毅,张清亮,赵月. 基于自适应奇异值分解的行星齿轮箱故障诊断方法[J]. 振动与冲击, 2018, 37(17): 122-127.
   QIN Yi, ZHANG Qingliang, ZHAO Yue. Fault diagnosis method for planetary gearboxes based on adaptive SVD [J]. Journal of Vibration and Shock, 2018, 37(17): 122-127.
[2] 葛江华,刘奇,王亚萍,许迪,卫芬. 支持张量机与KNN-AMDM决策融合的齿轮箱故障诊断方法[J]. 振动工程学报, 2018, 31(6): 1093-1101.
   GE Jianghua, LIU Qi, WANG Yaping, XU Di, WEI Fen. Fault diagnosis method of gearbox supporting tension machine and KNN-AMDM decision fusion [J]. Journal of Vibration Engineering, 2018, 31(6): 1093-1101.
[3] 李红贤,汤宝平,韩延,邓蕾. 迭代广义解调齿轮信号分离的变转速滚动轴承故障诊断[J]. 振动与冲击, 2018, 37(23): 38-44.
   LI Hongxian, TANG Baoping, HAN Yan, DENG Lei. Fault diagnosis of rolling bearings under a variable rotating speed based on iterative generalized demodulation gear signal separation [J]. Journal of Vibration and Shock, 2018, 37(23): 38-44.
[4] 李清蕾,万小金,徐增丙,王凯,赵乾坤. 基于特征选择与软竞争ART的轴承故障诊断[J]. 振动、测试与诊断, 2018, 38(6): 1199-1204.
   LI Qinglei, WAN Xiaojin, XU Zengbing, WANG Kai, ZHAO Qiankun. Soft-competitive learning ART network for bearing fault diagnosis [J]. Journal of Vibration, Measurement & Diagnosis, 2018, 38(6): 1199-1204.
[5] 李蓉,于德介,陈向民,刘坚. 基于线调频小波路径追踪算法与EEMD的齿轮箱复合故障诊断方法[J]. 振动与冲击, 2014, 33(3): 51-56.
   LI Rong, YU Dejie, CHEN Xiangmin, LIU Jian. A compound fault diagnosis method for gearboxs based on chirplet path pursuit and EEMD [J]. Journal of Vibration and Shock, 2014, 33(3): 51-56.
[6] Zhiwen Liu, Wei Guo, Jinhai Hu and Wensheng Ma. A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and Twin SVM [J]. ISA Transactions, 2017, 66: 249-261.
[7] 陈海周,王家序,汤宝平,李俊阳. 基于最小熵解卷积和Teager能量算子直升机滚动轴承复合故障诊断研究[J]. 振动与冲击, 2017, 36(9): 45-50.
   CHEN Haizhou, WANG Jiaxu, TANG Baoping, LI Junyang. Helicopter rolling bearing hybrid faults diagnosis using minimum entropy deconvolution and Teager energy operator [J]. Journal of Vibration and Shock, 2017, 36(9): 45-50.
[8] 郑近德,姜战伟,代俊习,潘紫微. 基于VMD的自适应复合多尺度模糊熵及其在滚动轴承故障诊断中的应用[J]. 航空动力学报, 2017, 32(7): 1683-1689.
   ZHENG Jinde, JIANG Zhanwei, DAI Junxi, PAN Ziwei. VMD based adaptive composite multiscale fuzzy entropy and its application to fault diagnosis of rolling bearing [J]. Journal of Aerospace Power, 2017, 32(7): 1683-1689.
[9] 马新娜,杨绍普. 滚动轴承复合故障诊断的自适应方法研究[J]. 振动与冲击, 2016, 35(10): 145-150.
   MA Xinna, YANG Shaopu. Adaptive compound fault diagnosis of rolling bearings [J]. Journal of Vibration and Shock, 2016, 35(10): 145-150.
[10] 丁锋,栗祥,韩帅. EEMD与NRS在涡轮发动机转子故障诊断中的应用[J]. 航空动力学报, 2018, 33(6): 1423-1431.
   DING Feng, LI Xiang, HAN Suai. Application of EEMD and NRS in turboprop engine rotor fault diagnosis [J]. Journal of Aerospace Power, 2018, 33(6): 1423-1431.
[11] 姜万录,郑直,胡浩松. 基于EEMD形态谱和支持向量机复合的滚动轴承故障诊断方法[J]. 工程科学学报, 2015, 37: 72-77.
   JIANG Wanlu, ZHENG Zhi, HU Haosong. Fault diagnosis of ball bearing based on EEMD morphological spectrum and support vector machine [J]. Chinese Journal of Engineering, 2015, 37: 72-77.
[12] 刘畅,伍星,刘韬,柳小勤. 基于近似等距投影和支持向量机的滚动轴承故障诊断[J]. 振动与冲击, 2018, 37(5): 234-239.
   LIU Chang, WU Xing, LIU Tao, LIU Xiaoqin. Fault diagnosis of rolling bearings based on near-isometric projection and support vector machine [J]. Journal of Vibration and Shock, 2018, 37(5): 234-239.
[13] 李状,柳亦冰,滕伟,林杨. 基于粒子群优化KFCM的风电齿轮箱故障诊断[J]. 振动、测试与诊断, 2017, 37(3): 484-488.
   LI Zhuang, LIU Yibing, TENG Wei, LIN Yang. Fault diagnosis of wind turbine gearbox based on KFCM optimized by particle swarm optimization [J]. Journal of Vibration, Measurement & Diagnosis, 2017, 37(3): 484-488.
[14] 李鑫滨,陈云强,张淑清. 基于改进ABC算法优化的LSSVM多分类器组机械故障诊断模型[J]. 中国机械工程, 2013, 24(16): 2157-2164.
   LI Xinbin, CHEN Yunqiang, ZHANG Shuqing. Mechanical fault diagnosis model based on IABC algorithm optimized multiple LSSVM classifier group [J]. China Mechanical Engineering, 2013, 24(16): 2157-2164.
[15] 骆志高,陈保磊,庞朝利,陈鹏. 基于遗传算法的滚动轴承复合故障诊断研究[J]. 振动与冲击, 2010, 29(6): 174-177.
   LUO Zhigao, CHEN Baolei, PANG Chaoli, CHEN Peng. Rolling bearing complex fault diagnosis based on genetic algorithm[J]. Journal of Vibration and Shock, 2010, 29(6): 174-177.
[16] E. Bechhoefer, R. Y. Li and D. He. Quantification of Condition Indicator Performance on a Split Torque Gearbox[J]. Journal of Intelligent Manufacturing, 2012, 23(2): 213-220.
[17] Xinghui Zhang, Jianshe Kang, E. Bechhoefer and Hongzhi Teng. Enhanced bearing fault detection and degradation analysis based on narrowband interference cancellation[J]. International Journal of System Assurance Engineering and Management, 2014, 5(4):645-650.
[18] Seyedali Mirjalili, Andrew Lewis. The whale optimization algorithm [J]. Advances in Engineering Software, 2016, 95: 51-67.

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