自适应TQWT滤波器算法及其在冲击特征提取中的应用

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摘要微弱故障的冲击特征提取，对于旋转机械设备平稳工况下的状态监测与诊断至关重要。针对强背景噪声下机械故障微弱冲击特征有效提取的难题，提出基于自适应可调品质因子小波变换（Tunable Q-factor Wavelet Transform, TQWT）滤波器的冲击特征提取算法。TQWT作为新兴的频域显式小波构造理论，具有匹配特定振荡行为信号成分、可利用FFT算法快速实现的优点。所提自适应TQWT滤波器算法，主要涉及TQWT参数（品质因子Q、冗余度r以及分解层数J）的优化选择以及最优特征子带的自适应选择，不依赖于先验知识。算法根据所提出的中心频率比指标以及能量加权归一化小波熵，分别对分解层数以及品质因子和冗余度进行优化选择，构造出适合揭示冲击信号成分振荡行为的优化可调品质因子小波基函数，进而利用冲击特征指标引导含冲击特征信息的最优特征子带选择，最后利用TQWT逆变换实现信号的重构与降噪，提取周期性微弱冲击特征。仿真试验与实测轴承信号的分析结果表明，算法能够自适应选择TQWT参数并实现微弱冲击特征的有效提取。

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	孔运
	王天杨
	褚福磊

关键词 ：可调品质因子小波变换, 参数选择, 能量加权归一化小波熵, 冲击特征指标, 微弱故障特征

Abstract：It is essential to extract the weak faults’ impact features for condition monitoring and fault diagnosis of rotating machine equipment under stationary conditions. Aiming at the difficult problem to extract weak impact features of mechanical faults under strong background noise, an impact feature extraction algorithm based on the adaptive tunable Q-factor wavelet transform (TQWT) filter was proposed. TQWT, as an emerging wavelet construction theory developed in frequency domain explicitly, has advantages of matching the specific oscillatory behavior of signal components and being able to be realized efficiently using FFT algorithm. The proposed adaptive TQWT filter algorithm mainly involved the optimization selection of TQWT parameters (quality factor Q, redundancy r and number of decomposition levels J) and the adaptive selection of the optimal feature sub-band, not relying on prior knowledge. According to the proposed center frequency ratio index and the energy-weighted normalized wavelet entropy, optimization selections were performed for the number of decomposition level, Q-factor and redundancy, respectively, to construct the optimized tunable Q-factor wavelet base function being appropriate to reveal the impact signal components’ oscillatory behavior. Furthermore, the impact feature index was used to guide the selection of the optimal feature sub-band containing impact feature information. Finally, the inverse transform of TQWT was used to realize the signal reconstruction and de-noising to extract periodic weak impact features. Simulation tests and analysis results of actually measured bearing vibration signals showed that the proposed algorithm can be used to adaptively select TQWT parameters and realize the effective extraction of weak faults’ impact features.

Key words： Tunable Q-factor wavelet transform Parameter selection Energy-weighted normalized wavelet entropy Impulse feature index Incipient fault feature

收稿日期: 2017-12-20 出版日期: 2019-05-28

引用本文:

孔运，王天杨，褚福磊. 自适应TQWT滤波器算法及其在冲击特征提取中的应用[J]. 振动与冲击, 2019, 38(11): 9-16.
KONG Yun, WANG Tianyang, CHU Fulei. Adaptive TQWT filter algorithm and its application in impact feature extraction. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(11): 9-16.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I11/9

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