基于多传感信号融合处理的滚动轴承故障定位诊断方法

Rolling Bearing Fault Diagnosis and Localization Based on Multi-Sensor Signal Fusion Processing

针对传统滚动轴承动力学模型忽略了滚动体接触形貌的问题,以滚珠进入-离开缺陷全过程分析为基础,结合滚动轴承几何-运动原理建立局部缺陷的等效轮廓量化表征函数,由此构建了一种改进的滚动轴承系统故障动力学模型。基于动力学模型的理论分析和数值仿真,研究了滚动轴承外圈局部缺陷定位尺寸与多通道振动信号融合特征之间的映射关系,为定位诊断特征和指标提取提供了机理指引。针对实际信号中存在噪声干扰影响定位公式诊断精度的问题,研究了多通道时间序列自适应分解算法对含噪仿真信号和试验信号的处理性能。结果表明,张量奇异谱分解方法能够很好地提取出隐藏在原始多通道信号中的定位诊断特征。

In traditional rolling bearing dynamic models,the contact profile of rolling elements is often neglected.Based on the comprehensive analysis of a rolling ball entering and leaving a defect,an equivalent profile quantitative characterization function for localized defects is established,integrating the geometric-motion principles of rolling bearings.From this,an enhanced dynamic model for system failures in rolling bearings is constructed.Using theoretical analysis and numerical simulations based on the dynamic model,the mapping relationship between the location dimensions of outer raceway defect for rolling element bearings and the characteristics of vibration signals is explored,offering a mechanistic foundation for the construction and extraction of quantitative diagnostic indicators.To address the challenge presented by noise interference affecting the diagnostic accuracy of location formulas in real signals,a new algorithm for adaptively decomposing multi-channel time series is used in this paper.In analyses of both simulated and experimental signals,it is shown that the subtle fault quantification features hidden within the original multi-channel signals are more effectively extracted using tensor singular spectrum decomposition.