A time frequency de-noising method is presented in the frequency response function （FRF） preprocessing based on the continuous wavelet transform. Morlet wavelet is employed to construct a filter bank to reduce the n...A time frequency de-noising method is presented in the frequency response function （FRF） preprocessing based on the continuous wavelet transform. Morlet wavelet is employed to construct a filter bank to reduce the noise. The filter bank is a finite impulse response （FIR） linear phase filter thus maintaining phase consistency. A modified Morlet base function is proposed to meet the time frequency resolution by using transient excitation. Numerical simulation is conducted using a Group for Aeronautical Research and Technology in Europe （GARTEUR） aircraft model excited by the transient input. The white noise is added to the simulated data. Results show that the accuracy of the system identification is improved. The estimated error of the mode damping is decreased by 30% compared with that obtained from the noise-corrupted signal.展开更多
For the issue of deterioration in detection performance caused by dynamically changing environment in ultra-wideband（UWB） multiple input multiple output（MIMO） radar, this paper proposes a novel adaptive waveform d...For the issue of deterioration in detection performance caused by dynamically changing environment in ultra-wideband（UWB） multiple input multiple output（MIMO） radar, this paper proposes a novel adaptive waveform design which is aimed to improve the ability of discriminating target and clutter from the radar scene. Firstly, a sequence of Morlet wavelet pulses with frequency hopping and pulse position modulation by Welch-Costas array is designed. Then a waveform optimization solution is proposed which is achieved by applying the minimization mutual-information（MI） strategy. After that, with subsequent iterations of the algorithm, simulation results demonstrate that the optimal waveform design method brings an improvement in the target detection ability in the presence of noise and clutter.展开更多
针对滚动轴承故障诊断过程中样本处理、故障识别等技术问题,提出一种基于Morlet小波和分类回归树(Classification and Regression Tree,CART)的滚动轴承故障诊断方法。首先,利用Morlet小波分析方法和移动窗方法对轴承振动信号进行样本...针对滚动轴承故障诊断过程中样本处理、故障识别等技术问题,提出一种基于Morlet小波和分类回归树(Classification and Regression Tree,CART)的滚动轴承故障诊断方法。首先,利用Morlet小波分析方法和移动窗方法对轴承振动信号进行样本处理。其次,对提取的短样本进行变分模态分解与特征提取,完成训练集和测试集的构建。然后,使用训练集训练CART决策树分类模型,同时引入随机搜索和K折交叉验证用于模型关键参数优化,以获取理想的轴承故障分类模型。测试集验证结果表明,该方法不但能实现多种轴承故障的有效诊断、在含噪测试集中表现良好,而且单个样本的数据长度和采样时长的缩短效果明显。展开更多
文摘A time frequency de-noising method is presented in the frequency response function （FRF） preprocessing based on the continuous wavelet transform. Morlet wavelet is employed to construct a filter bank to reduce the noise. The filter bank is a finite impulse response （FIR） linear phase filter thus maintaining phase consistency. A modified Morlet base function is proposed to meet the time frequency resolution by using transient excitation. Numerical simulation is conducted using a Group for Aeronautical Research and Technology in Europe （GARTEUR） aircraft model excited by the transient input. The white noise is added to the simulated data. Results show that the accuracy of the system identification is improved. The estimated error of the mode damping is decreased by 30% compared with that obtained from the noise-corrupted signal.
基金supported by the National Natural Science Foundation of China(6107114561271331)
文摘For the issue of deterioration in detection performance caused by dynamically changing environment in ultra-wideband（UWB） multiple input multiple output（MIMO） radar, this paper proposes a novel adaptive waveform design which is aimed to improve the ability of discriminating target and clutter from the radar scene. Firstly, a sequence of Morlet wavelet pulses with frequency hopping and pulse position modulation by Welch-Costas array is designed. Then a waveform optimization solution is proposed which is achieved by applying the minimization mutual-information（MI） strategy. After that, with subsequent iterations of the algorithm, simulation results demonstrate that the optimal waveform design method brings an improvement in the target detection ability in the presence of noise and clutter.
文摘针对滚动轴承故障诊断过程中样本处理、故障识别等技术问题,提出一种基于Morlet小波和分类回归树(Classification and Regression Tree,CART)的滚动轴承故障诊断方法。首先,利用Morlet小波分析方法和移动窗方法对轴承振动信号进行样本处理。其次,对提取的短样本进行变分模态分解与特征提取,完成训练集和测试集的构建。然后,使用训练集训练CART决策树分类模型,同时引入随机搜索和K折交叉验证用于模型关键参数优化,以获取理想的轴承故障分类模型。测试集验证结果表明,该方法不但能实现多种轴承故障的有效诊断、在含噪测试集中表现良好,而且单个样本的数据长度和采样时长的缩短效果明显。
