基于改进开关卡尔曼滤波的轴承故障特征提取方法

Feature Extraction of Bearing Fault Based on Improved Switching Kalman Filter

提出了一种基于改进开关卡尔曼滤波的滚动轴承故障特征提取新方法,与传统卡尔曼滤波算法相比,该方法每次迭代只需当前监测数据测量值和上一时刻最优估计值,计算效率高,具有较强实时性。首先将故障轴承振动信号分为故障冲击振动和正常振动两种成分;其次,针对故障冲击振动和正常振动两种状态,分别建立基于轴承质量-弹簧-阻尼系统动力学脉冲响应的卡尔曼滤波器及线性卡尔曼滤波器模型;然后,应用基于贝叶斯估计的开关卡尔曼滤波算法对振动信号进行状态估计;最终,通过时域迭代滤波,滤除噪声并识别故障冲击成分,实现轴承故障特征提取。仿真和试验信号分析结果表明了所提方法的可行性和有效性。

A new method of fault feature extraction for rolling bearings based on improved switching Kalman filter is proposed. Compared with the traditional Kalman filter algorithm, this method only needs the current measurement value and the optimal estimation of the previous moment in each iteration, so it has high computational efficiency and strong real-time performance. Firstly, the vibration signals of fault bearings are divided into two parts: Fault impulse vibration and normal vibration. Secondly, the Kalman filter model based on the dynamic impulse response of the bearing mass-spring-damper system and the linear Kalman filter model are established respectively for the fault impulse vibration and the normal vibration. Then, the state estimation of vibration signals is carried out by using the switching Kalman filter algorithm based on Bayesian estimation. Finally, the bearing fault feature extraction is realized by filtering noise and identifying fault impulse components through time domain iteration filtering. The simulation and experimental results show the feasibility and effectiveness of the proposed method.