本文研究一种基于源信号高阶统计信息的矩阵联合近似对角化独立元分析(JADE-ICA)方法,并将其应用于滚动轴承故障声发射(AE)信号的盲源分离。滚动轴承的声发射源信号一般具有衰减性和准周期性,多组信号间还具有时差性,信号被多个传感器接收。通过最大程度的联合近似对角化,可以使源信号与分离信号有效的一一对应,克服非线性和时差的影响;通过高阶统计的高斯噪声不敏感性可以有效抑制随机观测噪声对分离结果的影响。选用相关系数、二次残差、性能指数和频谱特征构成系列时频域评价指标对分离结果进行较为全面的验证。仿真结果证明了本文方法的可行性和有效性。
Abstract
Here,a joint approximate diagonalization of eigen-matrix and independent component analysis (JADE-ICA) method based on the high-order statistics of source signals was studied.It was applied to the blind source separation of rolling bearing faults’ acoustic emission (AE) signals.The multi-AE source signals of rolling bearings were collected with multi-sensor.It was shown that the bolling bearing multi-AE source signals have characteristics,such as,time difference among multi-signals,with decay and quasi periodicity.Through the maximum joint approximate diagonalization,the one-to-one match between source signals and separated signals was realized to overcome the influence of nonlinearity and time difference.With the insensitivity of high-order statistics to Gaussian noise,the effects of random measured noise on the separated results were effectively suppressed.Correlation coefficient,quadratic residual,performance index and spectral characteristics were chosen to form a set of time-frequency domain evaluation indexes to verify the separated results.The simulation results verified the feasibility and effectiveness of the proposed method.
关键词
JADE /
滚动轴承 /
故障诊断 /
声发射
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Key words
JADE /
rolling bearing /
fault diagnosis /
acoustic emission
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参考文献
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脚注
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