滚动轴承的早期故障信号能量小,频带分布广泛;而传统包络谱分析技术直接在强干扰影响下对滚动轴承的故障特征提取经常失效。提出一种基于短时傅里叶变换(short time Fourier transform,STFT)的能量谱和独立分量分析(independent compone...滚动轴承的早期故障信号能量小,频带分布广泛;而传统包络谱分析技术直接在强干扰影响下对滚动轴承的故障特征提取经常失效。提出一种基于短时傅里叶变换(short time Fourier transform,STFT)的能量谱和独立分量分析(independent component analysis,ICA)的抗干扰滚动轴承包络分析新方法。该方法首先对获取的滚动轴承振动信号进行STFT能量谱分析,获取信号采样频带下的能量分布,采用带通滤波器获得高频带能量信号,并提取该包络波形,再通过ICA实现包络波形按源分离去噪,最后通过比较各独立分量的包络频谱与滚动轴承理论计算故障特征频率的匹配性,实现滚动轴承故障的精确诊断。仿真数据和试验验证该方法的可行性。展开更多
The Multilayer Perceptron Neural Network (MLPNN) induction technique has been successfully applied to a variety of machine learning tasks, including the extraction and classification of image features. However, not mu...The Multilayer Perceptron Neural Network (MLPNN) induction technique has been successfully applied to a variety of machine learning tasks, including the extraction and classification of image features. However, not much has been done in the application of MLPNN on images obtained by remote sensing. In this article, two automatic classification systems used in image feature extraction and classification from remote sensing data are presented. The first is a combination of two models: a MLPNN induction technique, integrated under ENVI (Environment for Visualizing Images) platform for classification, and a pre-processing model including dark subtraction for the calibration of the image, the Principal Components Analysis (PCA) for band selections and Independent Components Analysis (ICA) as blind source separator for feature extraction of the Landsat image. The second classification system is a MLPNN induction technique based on the Keras platform. In this case, there was no need for pre-processing model. Experimental results show the two classification systems to outperform other typical feature extraction and classification methods in terms of accuracy for some lithological classes including Granite1 class with the highest class accuracies of 96.69% and 92.69% for the first and second classification system respectively. Meanwhile, the two classification systems perform almost equally with the overall accuracies of 53.01% and 49.98% for the first and second models respectively </span><span style="font-family:Verdana;">though the keras model has the advantage of not integrating the pre-processing</span><span style="font-family:Verdana;"> model, hence increasing its efficiency. The application of these two systems to the study area resulted in the generation of an updated geological mapping with six lithological classes detected including the Gneiss, the Micaschist, the Schist and three versions of Granites (Granite1, Granite2 and Granite3).展开更多
文摘The Multilayer Perceptron Neural Network (MLPNN) induction technique has been successfully applied to a variety of machine learning tasks, including the extraction and classification of image features. However, not much has been done in the application of MLPNN on images obtained by remote sensing. In this article, two automatic classification systems used in image feature extraction and classification from remote sensing data are presented. The first is a combination of two models: a MLPNN induction technique, integrated under ENVI (Environment for Visualizing Images) platform for classification, and a pre-processing model including dark subtraction for the calibration of the image, the Principal Components Analysis (PCA) for band selections and Independent Components Analysis (ICA) as blind source separator for feature extraction of the Landsat image. The second classification system is a MLPNN induction technique based on the Keras platform. In this case, there was no need for pre-processing model. Experimental results show the two classification systems to outperform other typical feature extraction and classification methods in terms of accuracy for some lithological classes including Granite1 class with the highest class accuracies of 96.69% and 92.69% for the first and second classification system respectively. Meanwhile, the two classification systems perform almost equally with the overall accuracies of 53.01% and 49.98% for the first and second models respectively </span><span style="font-family:Verdana;">though the keras model has the advantage of not integrating the pre-processing</span><span style="font-family:Verdana;"> model, hence increasing its efficiency. The application of these two systems to the study area resulted in the generation of an updated geological mapping with six lithological classes detected including the Gneiss, the Micaschist, the Schist and three versions of Granites (Granite1, Granite2 and Granite3).
文摘提出了利用频域的独立成分分析(Independent components analysis)算法分离语音信号和噪声信号,达到抑制噪声的效果。并且,针对ICA算法在噪声源集中的环境中效果较好,在噪声源分散的环境中性能有所退化的情况,基于时域带噪信号的ICA算法提出频域带噪信号的ICA算法。最后利用最小均方误差估计谱幅度算法(Minimum mean square error)去除残留噪声,达到较好的语音增强效果。通过大量的实验数据测试,文中提出的基于ICA和MMSE短时谱幅度估计的双麦克语音增强算法在不同信噪比(Signal to Noise Ratio)下,都取得了良好的降噪效果。
