统计时频谱驱动的高速自动机损伤预示方法

Statistical Time⁃Frequency Spectrum⁃Driven Method for Damage Prediction of High⁃Speed Automata

提出一种统计时频谱驱动的高速自动机损伤预示方法,利用瞬态冲击信号的时频分布变化检测瞬时冲击类故障。首先,从高速自动机服役性能跟踪试验中收集瞬态冲击信号;其次,计算瞬时频率特征用于表征非平稳瞬态冲击信号的演化过程;然后,建立瞬时频率特征的规范变量分析模型,提取瞬时频率间的最大相关信息,减少瞬态冲击信号间的信息歧义;最后,基于核密度估计方法确定健康状态阈值,对高速自动机的瞬时冲击响应信号进行监测。对某12.7 mm高速自动机的不同缺陷状态进行监测,验证了上述方法对瞬时冲击故障预示的有效性。

A statistical time-frequency spectrum-driven method for damage prediction of high-speed automata is proposed,and then the faults caused by transient shocks can be characterized with changes in time-frequency distributions of the response signals.Firstly,transient shock signals are collected from the performance tracking test of high-speed automata.Secondly,instantaneous frequency features of the signals are calculated to reflect the evolution of frequencies with time.Thirdly,canonical variate analysis model is built on those features to extract the maximal correlation information,such that the information ambiguity among the transient shock signals can be largely reduced.Finally,the threshold for healthy states of high-speed automata is determined with the kernel density estimation,for the aim of monitoring the transient impulse response signals.Different defective conditions of a 12.7 mm automaton are monitored with the proposed work,experimental results confirm the efficiency of the suggested work in detecting transient shock damage.