基于Transformer和卷积神经网络的齿轮故障诊断方法

针对部分齿轮的运行环境复杂,导致采集的样本数据不够的问题,提出了一种基于Transformer和卷积神经网络(CNN)的迁移学习齿轮故障诊断方法。首先,采用高斯滤波对原始振动信号进行了预处理,使信号变得平滑,降低了噪声信号的干扰;再将信号处理成带有位置信息的补丁序列以作为Transformer的输入,并增强了Transformer特征提取的能力,提高了诊断精度;然后,将信号输入到CNN继续提取特征信息,在模型中添加了一个残差块以防止网络退化;接着,划分了实验室采集的齿轮数据集和东南大学齿轮箱数据集的源域和目标域,采用了源域数据预训练模型,选择了每种类型的齿轮各100个样本为目标域;最后,以不同数据集为源域共进行了4组10次重复实验,测试了模型的准确率。研究结果表明:以不同数据集为源域的4组10次迁移实验的齿轮故障诊断准确率较高,均在90%以上,最高准确率可达100%;与其他不含Transformer的卷积神经网络、多尺度卷积神经网络和二维卷积神经网络相比,Transformer-CNN的齿轮故障诊断平均准确率更高,其平均准确率可达到99.64%。因此,基于Transformer-CNN的迁移学习方法能在小样本下诊断齿轮的故障。

海杂波环境下慢速小目标检测方法

海杂波环境下慢速小目标检测一直是雷达目标识别的难点,在分析海杂波特性的基础上,设计了脉间信号平滑处理和时频分析两种实用的海杂波抑制和慢速小目标检测方法,通过实验验证,在雷达长时间照射下采取一定时间间隔的扫描间信号平滑处理,而在短时间照射下采取时频分析方法均能有效地区分出淹没于海杂波环境下的慢速小目标。

A fuzzy adaptive smoothing approach to robust endpoint detection based on MDL using sub-band speech

To develop a more robust endpoint detection algorithm, this paper first proposes a fuzzy adaptive smoothing algorithm. The general idea underlying adaptive smoothing is to adapt the short-term sub-band mean of the amplitude to the local attributes of speech on the basis of discontinuity measures. The adaptive smoothing algorithm in this paper utilizes a scale-space framework through the minimal description length （MDL）. We recommend using the fuzzy muhi-attribute decision making approach to select the proper sub-bands where the word boundary can be more reliably detected. The process and simulation of the fuzzy adaptive smoothing algorithm are given. The parameters utilize the mean amplitude of the audible frequency range （300 -3 700 Hz） and the sub-band mean of the amplitude （16 band filter-bank）. We selected the audible band energy because of its usefulness in detecting high-energy regions and making the distinction between speech and noise. Otherwise, the fuzzy adaptive smoothing algorithm is processed in sub-band speech to utilize the full range of frequency information.