Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of ...Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.展开更多
针对风电机组滚动轴承工作环境恶劣、工况多变且振动信号成分复杂等特点,将33项时域和频域特征参数及其特性应用于风电机组滚动轴承状态监测和故障诊断中,利用奇异值分解重构法(Singular Value Decomposition,SVD)将滚动轴承振动故障信...针对风电机组滚动轴承工作环境恶劣、工况多变且振动信号成分复杂等特点,将33项时域和频域特征参数及其特性应用于风电机组滚动轴承状态监测和故障诊断中,利用奇异值分解重构法(Singular Value Decomposition,SVD)将滚动轴承振动故障信号中的噪声等干扰成分去除,降噪重构后的信号经过基于经验模式分解法(Empirical Mode Decomposition,EMD)的希尔伯特-黄变换,实现故障冲击信号的共振解调处理,将低频周期故障调制信号筛选出来,最终结合滚动轴承各部件故障特征频率、振动信号时频分析结果和时频特征参数诊断结果实现滚动轴承的状态监测和故障识别。并通过振动测试信号分析,验证了该方法对提取风电机组滚动轴承故障特征的有效性。展开更多
文摘Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.
文摘针对风电机组滚动轴承工作环境恶劣、工况多变且振动信号成分复杂等特点,将33项时域和频域特征参数及其特性应用于风电机组滚动轴承状态监测和故障诊断中,利用奇异值分解重构法(Singular Value Decomposition,SVD)将滚动轴承振动故障信号中的噪声等干扰成分去除,降噪重构后的信号经过基于经验模式分解法(Empirical Mode Decomposition,EMD)的希尔伯特-黄变换,实现故障冲击信号的共振解调处理,将低频周期故障调制信号筛选出来,最终结合滚动轴承各部件故障特征频率、振动信号时频分析结果和时频特征参数诊断结果实现滚动轴承的状态监测和故障识别。并通过振动测试信号分析,验证了该方法对提取风电机组滚动轴承故障特征的有效性。