Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases

Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features, as well as its easy burying in the complex, non-stationary structural vibrations and strong background noises. In this paper, a method based on the flexible analytical wavelet transform (FAWT) possessing fractional scaling and translation factors is proposed to identify multiple faults occurred in different components of rolling bearings. During the route of the proposed method, the proper FAWT bases are constructed via genetic optimization algorithm (GA) based on maximizing the spectral correlated kurtosis (SCK) which is firstly presented and proved to be efficient and effective in indicating interested fault mode. Via using the customized FAWT bases for each interested fault mode, the original vibration measurements are decomposed into fine frequency subbands, and the sensitive subband which enhances the signal-to-noise ratio (SNR) is selected to exhibit the fault signature on its envelope spectrum. The proposed method is tested via simulated signals, and applied to analyze the experimental vibration measurements from the running roller bearings subjected to outrace, inner-race and roller defects. The analysis results validate the effectiveness of the proposed method in identifying multi-faults occurred in different components of rolling bearings.     

基于图像识别的机械振动信号特征提取与寿命预测方法研究

滚动轴承作为许多机械设备的关键组件,被广泛应用于机械制造、航空航天等领域,其健康状态直接影响了相应设备的剩余寿命,因此在设备故障预测与健康管理(Prognostics and Health Management, PHM)领域,滚动轴承寿命预测具有很高的研究价值。目前基于数据驱动的轴承寿命预测方法主要利用特征提取并构造健康因子(Health Indicator, HI),然而在这一过程中特征的选择与融合依然依赖于专家先验知识,并且健康因子也很难从复杂的时序数据中进行提取。因此,提出了一种新型的数据驱动寿命预测算法,在特征提取方面,通过连续小波变换(Continuous Wavelet Transform,CWT)将传感器振动信号转换为时频谱图,再通过深度残差网络(Deep residual network, ResNet)结合时空卷积网络(Temporal Convolutional Network, TCN)将时频谱图中的时域频域特征构造成为健康因子,最后完成剩余寿命预测。本研究在PRONOSTIA数据集上与现有的数据驱动算法进行了对比,证明了该算法可以更准确地完成剩余寿命预测。     

Analysis of the data processing strategies of spherical harmonic expansion model on global ionosphere mapping for moderate solar activity

Spherical harmonic (SH) expansion is widely used to model the global ionosphere map (GIM) of vertical total electron content (VTEC). According to the impact of different data processing methods of the SH expansion model on the VTEC maps, we specifically performed comprehensive analysis in terms of the data sampling rate, the time resolution, the spherical harmonic degree, and the relative constraint. One month of GPS data (January in 2016) from the International GNSS (Global Navigation Satellite System) Service (IGS) network in a moderate ionospheric activity period at the descending phase of Solar Cycle 24 was processed. To improve the computational efficiency of the daily GIM generation, the data sampling rate of 5?min was recommended allowing the GIM precision loss within 0.10 TECU (total electron content unit). The global VTEC map could be better represented in temporal and spatial domains with higher time resolution and higher spherical harmonic degree, especially at low latitude bands and in the southern hemisphere. The GIM precision improvement was about 10.91% for 1-h and about 15.15% for 0.5-h compared with the commonly used 2-h time resolution. The use of spherical harmonic degree 17 or 20 instead of 15 could improve the precision by 3.19% or 6.06%. We also found that an optimal relative constraint had to be found experimentally considering both the GIM precision and the GIM root mean square (RMS) map.     

周向非均匀流动引起的转子动力学载荷分析

以轴流涡轮为分析对象,分析了流动沿周向分布不均匀的条件下涡轮转子受到的气动载荷。假设涡轮级内的流动沿周向呈1次谐波形式的余弦函数分布,考虑转子叶片叶顶间隙,利用双耦合激励盘模型描述叶片通道中的流动,采用谐波分析方法求得涡轮转子受到的气动载荷。分析了不同轴向位置处、不同非均匀流动参数的影响,讨论了转子偏心所产生的非均匀流动,给出了非稳态流动条件下的气动载荷。结果表明:1次谐波形式的非均匀流动会产生附加载荷作用于转子,从而影响转子的动力学性能;非稳态不均匀流动会影响附加载荷的大小,但不改变其在动坐标系中的方向。     

民航行李X光安检图像自适应小波去噪算法

对X光安检图像进行去噪处理可以更清楚地显示出危险物。针对传统小波软硬阈值去噪方法的缺陷．给出了一种自适应阈值的去噪方法,在此方法中,首先进行软硬阈值的折中处理,然后分别对大于和小于阈值的两部分进行自适应处理。利用Tennessee大学提供的实际采集的X光安栓图像进行实验,结果表明该方法能有效地去除安检图像中的噪声,图像去噪后的信噪比、峰值信噪比分别是25．2229dB、27．708dB．与含噪图像相比分别增加了7．7253dB和7．739dB。因此该方法表现出较高的去噪性能。     

基于小波分析与支持向量机的时间序列预测

时间序列广泛存在于工业、经济、军事等各个领域,时间序列预测是数据分析处理的一个重要方面。目前提出的预测模型大多基于＂原始时间序列是无噪的＂这一假定,而实际应用中,对时间序列去噪处理的好坏将直接影响预测的准确率,针对这一事实,使用小波分析对原始时间序列去噪。利用小波变换对时间序列进行多尺度分解,对各尺度上的细节序列使用阀值法去噪;使用支持向量机对重构后的各组小波系数进行预测并将结果融合,得到预测结果。实验结果表明,用于时间序列预测,能及时反应序列的变化趋势并具有较高的预测精度。     

模糊聚类方法在PIO探测中的应用

用于驾驶员诱发振荡(PIO)探测的特征参数可以有效地反映出PIO时飞机在频域和时域的特征,利用模糊逻辑方法对特征参数进行辨识可以有效地识别出是否发生了PIO.模糊逻辑方法中的隶属函数直接反映了特征参数的特点,其设计决定了是否能有效地对PIO进行识别.为了设计有效的隶属函数,根据特征数据的特点采用球壳型模糊聚类算法对数据...     

基于热敏电阻的气体温度测量误差分析

挤压气体内部温度的精确测量对确定贮箱内推进剂剩余量和提高氧化剂和燃烧剂的混合比控制精度有重要意义.提出了一种基于热敏电阻的矢量测温方法,通过测量气瓶壁面温度推算气体内部真实温度,分析温度电阻非线性特性、温度—电压处理电路、A/D采样非线性等因素导致的温度测量误差,最终获得系统自身的温度采集误差,在此基础上分析了影响此误差的主要因素.最后分析了外界干扰引起的测量噪声对温度测量的影响,并采用小波降噪的方法对此进行降噪处理,使温度测量精度得到较大改善,并最终确定了内部气体温度的测量误差.     

SVM和神经网络在电能质量扰动分类应用中的对比

提出了一种电能质量动态扰动特征向量的提取方法,分析比较了多种分类器对电能质量动态扰动的分类能力。首先采用小波包分解算法对电能质量信号某一频段内的信息进行精细分解从而提取出特征向量,然后针对该特征向量构造了相应的BP神经网络、学习向量量化(Learning vector quantization,LVQ)神经网络、自组织特征映射(Self-organizing map,SOM)神经网络及支持向量机(Support vector machine,SVM)分类器,并模拟实际电网中的复杂扰动信号提取其特征向量集,对多种分类器的分类能力进行对比。仿真结果表明,在较复杂的电能质量扰动情况中,支持向量机分类器仍能实现对信号的精确分类,对电能质量监测具有很好的应用价值。     

自由段弹道扰动引力计算的球谐函数极点变换

为提高弹道导弹自由段扰动引力赋值速度,提出了自由段扰动引力计算的球谐函数极点变换一般方法。重点针对轨道动量矩与天球交点为极点及主动段关机点为极点两种情况,推导了考虑侧向偏差和地球旋转修正的扰动引力计算的球谐函数极点变换模型。仿真分析表明:从落点偏差来看,前者扰动引力计算相对误差为1.89%,后者为6.07%,满足诸元和制导精度要求。