摘要
提出了自适应最优化窄带分解(adaptive optimization narrow-band decomposition，AONBD)方法。该方法将信号分解转化为对滤波器参数的优化问题，以得到信号的最优化解为优化目标，在优化过程中将信号自适应的分解成多个内禀窄带分量(intrinsic narrow-band components，INBC)。AONBD分为两步，首先通过优化得到最优的滤波器，然后使用该滤波器对信号进行滤波以得到信号的最优化解。阐述了AONBD的基本原理及分解步骤。采用仿真信号将AONBD方法与自适应最优化时频分析(adaptive sparsest time-frequency analysis，ASTFA)方法及经验模态分解(empirical mode decomposition，EMD)方法进行对比。结果表明，AONBD在抑制端点效应和模态混淆、抗噪声性能、提高分量的正交性和准确性等方面具有一定的优越性。对转子振动信号的分析结果表明，AONBD能有效应用于机械故障诊断。
Abstract
Adaptive and sparsest time-frequency analysis (AONBD) method is proposed. Signal decomposition is translated into optimizing the parameters of the filter. The optimization objective is obtaining the sparsest solution of signals. The original signal is adaptively decomposed into several intrinsic narrow-band components (INBC) via optimization. AONBD method has two steps. First，the best filter is obtained by optimization. Secondly，the sparsest solution is derived by filtering the original signal using the designed filter. The basic theory and decomposition steps of AONBD are described. Comparisons are made between AONBD，adaptive sparsest time-frequency analysis (ASTFA) and empirical mode decomposition (EMD) by utilizing a simulation signal. The results show that AONBD method is superior to the other two methods in restraining the end effect and mode mixing，anti-noise performance，improving the orthogonality and accuracy of components. AONBD method is applied to analyzing the vibration signal of rotor. And the results indicate that AONBD can be effectively applied to mechanical fault diagnosis.
关键词
自适应最优化窄带分解 /
内禀窄带分量 /
局部窄带信号 /
奇异局部线性算子 /
转子故障诊断
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Key words
Adaptive optimization narrow-band decomposition /
Intrinsic narrow-band components /
Local narrow-band signal /
Singular local linear operator /
Rotor fault diagnosis
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彭延峰 程军圣 杨宇 李宝庆.
自适应最优化窄带分解方法及其应用[J]. 振动与冲击, 2016, 35(15): 1-6
PENG Yanfeng CHENG Junsheng YANG Yu LI Baoqing.
Adaptive optimization narrow-band Decomposition Method and Its Application[J]. Journal of Vibration and Shock, 2016, 35(15): 1-6
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