基于量子Hadamard变换的滚动轴承振动信号分析方法

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摘要针对传统的滚动轴承振动信号分析方法在对信号降噪过程中容易产生的噪声信号与非噪声信号混杂的问题，提出一种基于量子Hadamard变换的信号分析方法。该方法通过将单个采样点信号量子化，深入分析每一个采样点内部信息，实现信号降噪的同时，完成对振动信号故障信息的突出表达，改善了振动信号的分析效果。实验结果表明，与传统的基于数学形态学的分析方法相比，基于量子Hadamard变换的分析方法能够有效提升滚动轴承信号的降噪效果，故障特征信号得到有效凸显。

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	王怀光1
	陈彦龙2
	杨望灿1
	王强1

关键词 ：信号分析, 振动信号, 量子Hadamard变换

Abstract：Based on the traditional analysis method for rolling bearing vibration signals,in the process of de-noising it is easy to confuse noises and non-noises.To solve the problem,an analysis method based on the quantum Hadamard transform was proposed.In the method the inner information was analysed deeply at every sampling point by using the quantum transform.Then the fault information in fault vibration signals was highlighted during the signal de-noising.In the data processing,the analysis effects were improved by virtue of the quantum theory.The experiment results show that comparing with the traditional mathematical morphology analysis algorithm,the method based on the quantum Hadamard transform can better improve the effect of de-noising and enhance the fault signals for rolling bearings.

Key words： signal analysis vibration signal quantum Hadamard transform

收稿日期: 2017-06-07 出版日期: 2019-01-15

引用本文:

王怀光1，陈彦龙2，杨望灿1，王强1. 基于量子Hadamard变换的滚动轴承振动信号分析方法[J]. 振动与冲击, 2019, 38(2): 116-122.
WANG Huaiguang1，CHEN Yanlong2，YANG Wangcan1，WANG Qiang1. Analysis method based on the quantum Hadamard transform for rolling bearing vibration signals. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(2): 116-122.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I2/116

[1] 康守强, 王玉静, 崔历历, 等. 基于CFOA-MKHSVM的滚动轴承健康状态评估方法[J]. 仪器仪表学报, 2016,37(9):2029-2035.
Kang Shouqiang, Wang Yujing, Cui Lili, et al. Health state assessment of a rolling bearing on CFOA-MKHSVM method[J]. Chinese Journal of Scientific Instrument, 2016,37(9):2029-2035.
[2] 向丹，岑建. 基于EMD熵特征融合的滚动轴承故障诊断方法[J]. 航空动力学报, 2015,30(5):1149-1155.
Xiang dan, Cen jian. Method of bearing fault diagnosis based on feature fusion of EMD entroy[J]. Journal of Aerospace Power, 2015,30(5):1149-1155.
[3] 范博楠, 王海斗, 徐滨士, 等. 强噪声背景下微弱声发射信号提取及处理研究现状[J]. 振动与冲击, 2015,34(16):147-155.
Fan Bonan, Wang Haidou, Xu Binshi, et al. Present research situation of the exaction and processing of weak acoustic emission signals understrong background noise[J]. Journal of Vibration and Shock, 2015,34(16):147-155.
[4] 孙海亮, 訾艳阳, 何正嘉. 多小波自适应分块阈值降噪及其在轧机齿轮故障诊断中的应用[J]. 振动工程学报, 2013, 26 (01): 127-134.
Sun Hailiang, Zi Yanyang, He Zhengjia. Multiwavelet denosising with adaptive block thresholding and its application in gearbox diagnosis of rolling mills[J]. Journal of Vibration Engineering, 2013, 26 (01): 127-134.
[5] 张文斌, 杨辰龙, 周晓军. 形态滤波方法在振动信号降噪中的应用[J]. 浙江大学学报(工学版), 2009, 43 (11): 2096-2099.
    Zhang Wenbin, Yang Chenlong, Zhou Xiaojun. Application of morphology filtering method in vibration signal de-noiseing[J]. Journal of Zhejiang University(Engineering Science), 2009, 43 (11): 2096-2099.
[6] 吴定海, 王怀光, 张培林, 等. 基于双树复小波包变换的空域和邻域联合降噪[J]. 华中科技大学学报(自然科学版), 2015, 43 (04): 17-21.
    Wu Dinghai, Wang Huaiguang, Zhang Peilin, et al. Denoising combined with spatial domain and neighborhood based on dual-tree complex wavelet packet transform[J]. J. Huazhong Univ. of Sci. & Tech. (Nature Science Edition), 2015, 43 (04): 17-21.
[7] 胥永刚, 赵国亮, 马朝永, 等. 基于双树复小波与非线性时间序列的降噪方法[J]. 振动与冲击, 2015, 34 (16): 135-140.
    Xu Yonggang, Zhao Guoliang, Ma Chaoyong, et al. New denoising method based on dual-tree complex wavelet transform and nonlinear time series[J]. Journal of Vibration and Shock, 2015, 34 (16): 135-140.
[8] 曹莹, 段玉波, 刘继承, 等. 多尺度形态录播混叠抑制方法[J]. 电机与控制学报, 2016,20(9):110-116.
Cao Ying, Duan Yubo, Liu Jicheng, et al. Multi-scale morphological filtering method for mode mixing suppession[J]. Electric Machines and Control, 20(9): 110-116.
[9] 李辉, 郑海起, 唐力伟. 基于广义形态分量分析的降噪技术研究[J]. 振动与冲击, 2013, 32 (01): 145-149.
    Li Hui, Zheng Haiqi, Tang Liwei. De-noising method based on generalized morphological component analysis[J]. Journal of Vibration and Shock, 2013, 32 (01): 145-149.
[10] Eldar Y C, Oppenheim A V. Quantum signal processing[J]. IEEE Signal Process Mag, 2002, 19 (6): 12-32.
[11] Quan X Q, Liu H, Lu Z W, et al. Correction and analysis of noise in Hadamard transform spectrometer with digital micro-mirror device and double sub-gratings[J]. Optics Communications, 2016, 359 (1): 95-101.
[12] Tsai J, Hsiao F Y, Li Y J, et al. A quantum search algorithm for future spacecraft attitude determination[J]. Acta Astronautica, 2011, 68 (7): 1208-1218.
[13] Mangone F, He J, Tang J, et al. A PAPR reduction technique using Hadamard transform combined with clipping and filtering based on DCT/IDCT for IM/DD optical OFDM systems[J]. Optical Fiber Technology, 2014, 20 (4): 384-390.
[14] 陈彦龙, 张培林, 李兵, 等. 采用非线性QB的形态滤波及其应用[J]. 机械工程学报, 2015,51(2):127-133.
Chen Yanlong, Zhang Peilin, Li Bing, et al. Morphological filtering using nonlinear quantum bit and its application[J]. Journal of Mechanical Engineering, 2015,51(2):127-133.