基于无迹卡尔曼滤波的动态贝叶斯小波变换在轴承故障诊断中的应用

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摘要针对工程实际中滚动轴承发生故障的类型具有典型性和故障信号具有冲击性，且振动信号的频率成分因外界环境的影响而变得极其复杂的特点，提出了一种基于负熵和无迹卡尔曼滤波的动态贝叶斯小波变换方法。该方法将SE(Squared Envelope) Infogram方法应用到无迹卡尔曼滤波方法(Unscented Kalman Filter, UKF)中，利用SE Infogram确定滤波器参数初值，即中心频率与带宽的初值，结合UKF对中心频率与带宽进行优化，以最优中心频率与带宽对振动信号进行滤波分析，对滤波后的信号进行包络解调分析，实现轴承微弱故障特征的提取。利用负熵指标代替以往研究所用的峭度指标，可以有效消除或削弱高峰值干扰的影响。最后，通过对仿真信号和轮对轴承试验信号对提出的方法进行了验证。结果表明，该方法能够有效提取强背景噪声下轴承外圈、内圈故障和滚动体故障，验证了该方法对轴承微弱故障诊断的有效性。

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	赵靖1
	2
	廖英英2
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	杨绍普1
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	刘永强1
	2
	顾晓辉1
	2

关键词 ：故障诊断, 负熵, 无迹卡尔曼滤波, 动态贝叶斯小波变换

Abstract：In engineering practice, the types of rolling bearing failure are typical and the fault signals are impulsive, and the frequency component of the vibration signals are extremely complex due to the influence of the external environment. A dynamic bayesian wavelet transform method based on negentropy and unscented kalman filter is proposed. The method applies SE(Squared Envelope) Infogram method to Unscented Kalman Filter (UKF) method. This method uses SE Infogram to determine the initial value of filter parameters, that is initial values of center frequency and bandwidth. Then, the center frequency and bandwidth are optimized with UKF, the vibration signal is filtered by optimal center frequency and bandwidth, and the envelope demodulation of the filtered signal is analyzed, so the weak fault characteristics of bearing can be extracted. This method replaces the kurtosis index used in previous studies with negentropy index. which can effectively eliminate or weaken the influence of peak value interference. Finally, the simulation signal and wheelset bearing test signal are validated. The results indicate that this method can effectively extract the fault of bearing outer and inner and roller under strong background noise, the effectiveness of this method in the diagnosis of weak bearing faults is verified.

Key words： fault diagnosis negentropy unscented kalman filter dynamic bayesian wavelet transform

收稿日期: 2018-11-07 出版日期: 2020-05-28

引用本文:

赵靖1,2，廖英英2,3，杨绍普1,2，刘永强1,2，顾晓辉1,2. 基于无迹卡尔曼滤波的动态贝叶斯小波变换在轴承故障诊断中的应用[J]. 振动与冲击, 2020, 39(11): 53-62.
ZHAO Jing1,2， LIAO Yingying2,3， YANG Shaopu1,2， LIU Yongqiang1,2， GU Xiaohui1,2. An extension of unscented Kalman filter to dynamic Bayesian wavelet transform in fault diagnosis of rolling element bearings. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(11): 53-62.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I11/53

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