Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis

The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.

TIME-VARYING AR MODELING AND ADAPTIVE IIR NOTCH FILTER FOR ANTI-JAMMING DSSS RECEIVER

Using Time-Varying AR (TVAR) model and adaptive notch filter is a new method for the non-stationary jammer suppression in Direct Sequence Spread Spectrum (DSSS). The performance of TVAR model for Instantaneous Frequency (IF) estimation will be affected by some factors such as basis functions. Focusing on this problem, the optimal basis function of TVAR model for the IF estimation of the LFM signal is obtained in this paper. Besides the depth and width of notching, the phase properties of notch filter affect the Signal-to-Interference plus-Noise Ratio (SINR) of correlation output to the narrowband jammer suppression in DSSS, in response to the problem the closed solution of correlation output SINR improvement has been derived when a single frequency jammer passes through direct IIR notch filter, and its performance has been compared with those of five coefficient FIR filters. Later, a novel method for LFM jammer suppression based on Fourier basis TVAR model and direct IIR notch filter is proposed. The simulation results show the effectiveness of the proposed method.

基于聚类求解TVAR模型的目标微多普勒分析

现有时频分析方法对目标进行微多普勒分析时的时频分辨率不足。针对该问题,提出了基于聚类先验求解前后向时变自回归(time-varying autoregressive,TVAR)模型的时频分析算法,来进行空间锥体目标的微多普勒分析。使用基于扩展块稀疏贝叶斯学习(extended block sparse Bayesian learning,EBSBL)的改进算法对TVAR模型的时不变块稀疏系数采用了聚类结构的先验,通过适当处理邻域的超参数来促进相邻稀疏系数之间的相关性,并结合刚体目标的时不变块稀疏系数的块边界已知的先验信息来求解时不变系数。电磁仿真和实测数据实验结果表明,所提算法在微多普勒分析时能够得到较传统方法更高的时频分辨率,时频聚集性更高,并且抗噪声性能较好。

TVAR在非平稳工况转子故障诊断中的应用

研究了基于BP网络(ANN)的时变参数自回归模型(TVAR)及其在非平稳工况旋转机械故障诊断中的应用。首先提出了一种基于BP算法的TVAR参数辨识方法,然后利用TVAR方法对一非线性调频仿真信号进行时频分析,并与典型时频分析方法短时傅里叶变换(STFT)及Choi-Williams分布(CWD)的分析结果进行比较。结果表明,TVAR方法具有时频分辨率高、无交叉干扰项及计算速度快等优点。最后利用TVAR方法分析了转子启动过程正常及故障工况下转子实验台的非平稳振动信号。研究表明,TVAR不但能够有效地分析非平稳振动信号,而且具有较强的故障特征提取和抗噪声能力,是在时频域上进行故障诊断的有效方法。

时变参数模型及其在非平稳振动分析中的应用

对时变参数模型TVAR(Tim e-varying Autoregressive Model)进行了研究,并将其应用于转子实验台非平稳振动信号的分析。TVAR是模型参数随信号统计特性而变化的变参数AR(Autoregressive Model)模型,适用于分析非平稳信号。利用TVAR对调频仿真信号进行分析并与典型时频分析方法进行比较,结果表明TVAR具有时频分辨率高、无交叉干扰项以及对噪声不敏感等优点。基于TVAR分析了转子实验台正常及故障工况下连续变速过程中采集的振动信号,实验表明TVAR能够有效地分析非平稳振动信号,并具有较强的信号特征提取能力,为非平稳工况下转子故障诊断及模态分析等提供了一种有效的分析方法。

联合运动补偿的逆合成孔径雷达成像方位定标方法

在雷达识别系统中,确定目标的尺寸对于提高目标识别能力具有重要的意义。针对逆合成孔径雷达(inverse synthetic aperture radar,ISAR)成像的定标问题,考虑了等效转台和残余平动引入的相位误差,提出了一种ISAR目标有效转动速度估计的新方法。通过在距离多普勒图像中自适应提取若干目标特征点,对其建立时变自回归模型(time-varying autoregressive,TVAR)。利用多项式拟合极点的多普勒历程,解算有效转动速度与残余相位误差系数。该方法不仅能可靠实现方位定标,残余的相位误差补偿可进一步提高图像的聚焦性能。仿真和实测数据实验验证了所提方法的有效性。

基于改进时变自回归模型的滚动轴承故障诊断

针对现有的时变自回归(Time-Varying Autoregressive,TVAR)模型应用于滚动轴承故障诊断中的问题,提出一种前向估计与后向估计相结合的改进模型。该模型在引入时变遗忘因子的基础上,定义了前后向联合估计的均方误差并对基函数的加权系数求偏导,得到加权系数的计算公式,然后利用递推最小二乘(Recursive Least Squares,RLS)方法推导了该计算公式的递推形式。针对滚动轴承内圈故障的仿真和实验信号,使用改进前后的模型进行时频分析。仿真和实验结果表明,改进后的模型有效地克服了现有模型无法获得初始时刻频率估计的缺点,具有更高的时频估计精度、更强的抗噪声能力,能够更加有效地提取滚动轴承的故障特征频率。

空间锥体目标的平动补偿与微动特征提取方法

微动特征对空间锥体目标识别与参数估计等有着重要意义,而目标的平动会破坏微多普勒谱的结构,影响微动特征的提取.针对这一问题,提出了一种基于时变自回归(Time-Varying Autoregressive,TVAR)模型的平动补偿与微动特征提取方法.算法首先分析了锥体目标的散射特性,在此基础上推导了微动和平动引起的回波瞬时频率的变化规律;利用TVAR模型估计目标回波的瞬时频率,并对估计结果作解模糊和重新关联处理,从而获得回波的瞬时频率分量;最后,对瞬时频率分量包络进行多项式拟合,利用拟合结果补偿目标平动,进而提取出微动特征.基于电磁计算数据的实验验证了所提算法的有效性及精确性.

Gearbox Deterioration Detection under Steady State,Variable Load, and Variable Speed Conditions

Multiple dominant gear meshing frequencies are present in the vibration signals collected from gearboxes and the conventional spiky features that represent initial gear fault conditions are usually difficult to detect. In order to solve this problem, we propose a new gearbox deterioration detection technique based on autoregressive modeling and hypothesis testing in this paper. A stationary autoregressive model was built by using a normal vibration signal from each shaft. The established autoregressive model was then applied to process fault signals from each shaft of a two-stage gearbox. What this paper investigated is a combined technique which unites a time-varying autoregressive model and a two sample Kolmogorov-Smimov goodness-of-fit test, to detect the deterioration of gearing system with simultaneously variable shaft speed and variable load. The time-varying autoregressive model residuals representing both healthy and faulty gear conditions were compared with the original healthy time-synchronous average signals. Compared with the traditional kurtosis statistic, this technique for gearbox deterioration detection has shown significant advantages in highlighting the presence of incipient gear fault in all different speed shafts involved in the meshing motion under variable conditions.