针对不同转速下,不同损伤程度的滚动轴承内、外圈故障,提出一种基于局域均值分解(Local Mean De-composition,LMD)和Lempel-Ziv指标的滚动轴承损伤程度识别方法。LMD方法是一种新的自适应时频分析方法,将轴承振动信号分解为若干个瞬时...针对不同转速下,不同损伤程度的滚动轴承内、外圈故障,提出一种基于局域均值分解(Local Mean De-composition,LMD)和Lempel-Ziv指标的滚动轴承损伤程度识别方法。LMD方法是一种新的自适应时频分析方法,将轴承振动信号分解为若干个瞬时频率有物理意义的乘积函数(Production Function,PF),再结合峭度条件找出蕴含故障信息的最优PF分量,计算其PF函数和包络的Lempel-Ziv的归一化值,再加权求和得到最终的Lempel-Ziv综合指标,表征了不同故障的损伤程度。同时还研究了在不同转速下的内、外圈故障轴承的Lempel-Ziv指标的分布规律,使结论更具有普遍性。经实验结果验证,此方法能有效地应用于滚动轴承的故障程度的诊断。展开更多
The objective was to study changes in EEG time-domain Kolmogorov complexity under different mental fatigue state and to evaluate mental fatigue using Lempel-Ziv complexity analysis of spontaneous EEG in healthy human ...The objective was to study changes in EEG time-domain Kolmogorov complexity under different mental fatigue state and to evaluate mental fatigue using Lempel-Ziv complexity analysis of spontaneous EEG in healthy human subjects. EEG data for healthy subjects were acquired using a net of 2 electrodes (Fp1 and Fp2) at PM 4:00, AM 12:00 and AM 3:00 in the 24 hours sleep-deprived mental fatigue experiments. It was presented that initial results for eight subjects examined in three different mental fa-tigue state with 2-channel EEG time-domain Lempel-Ziv complexity computations. It was found that the value of mean Lempel-Ziv com-plexity corresponding to a special mental state fluctuates within the special range and the value of C(n) increases with mental fatigue increasing for the total frequency spectrum. The result in-dicates that the value of C(n) is strongly cor-relative with the mental fatigue state. These re-sults suggest that it may be possible to nonin-vasively differentiate different mental fatigue level according to the value of C(n) for particular mental state from scalp spontaneous EEG data. This method may be useful in further research and efforts to evaluate mental fatigue level ob-jectively. It may also provide a basis for the study of effects of mental fatigue on central neural system.展开更多
文摘针对不同转速下,不同损伤程度的滚动轴承内、外圈故障,提出一种基于局域均值分解(Local Mean De-composition,LMD)和Lempel-Ziv指标的滚动轴承损伤程度识别方法。LMD方法是一种新的自适应时频分析方法,将轴承振动信号分解为若干个瞬时频率有物理意义的乘积函数(Production Function,PF),再结合峭度条件找出蕴含故障信息的最优PF分量,计算其PF函数和包络的Lempel-Ziv的归一化值,再加权求和得到最终的Lempel-Ziv综合指标,表征了不同故障的损伤程度。同时还研究了在不同转速下的内、外圈故障轴承的Lempel-Ziv指标的分布规律,使结论更具有普遍性。经实验结果验证,此方法能有效地应用于滚动轴承的故障程度的诊断。
文摘The objective was to study changes in EEG time-domain Kolmogorov complexity under different mental fatigue state and to evaluate mental fatigue using Lempel-Ziv complexity analysis of spontaneous EEG in healthy human subjects. EEG data for healthy subjects were acquired using a net of 2 electrodes (Fp1 and Fp2) at PM 4:00, AM 12:00 and AM 3:00 in the 24 hours sleep-deprived mental fatigue experiments. It was presented that initial results for eight subjects examined in three different mental fa-tigue state with 2-channel EEG time-domain Lempel-Ziv complexity computations. It was found that the value of mean Lempel-Ziv com-plexity corresponding to a special mental state fluctuates within the special range and the value of C(n) increases with mental fatigue increasing for the total frequency spectrum. The result in-dicates that the value of C(n) is strongly cor-relative with the mental fatigue state. These re-sults suggest that it may be possible to nonin-vasively differentiate different mental fatigue level according to the value of C(n) for particular mental state from scalp spontaneous EEG data. This method may be useful in further research and efforts to evaluate mental fatigue level ob-jectively. It may also provide a basis for the study of effects of mental fatigue on central neural system.
