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-i...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.展开更多
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...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.展开更多
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...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。展开更多
基金This paper is supported by National Natural Science Foundation of China under Grant No.50675209 InnovationFund for Outstanding Scholar of Henan Province under Grant No. 0621000500
文摘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.
基金supported by National Natural Science Foundation of China (Grant No. 50675232)Key Project of Ministry of Education of ChinaChongqing Municipal Natural Science Key Foundation of China (Grant No. 2007BA6021)
文摘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.
文摘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。
