Bearing load estimation would form a valuable addition to the fields of condition monitoring and system control. Despite effort spend on its development by all major bearing manufacturers, no product solution has come to market yet. This can be attributed to both the complexity in conditioning of the strain measurement as well as its nonlinearity with respect to the bearing loading. To address these issues, this paper proposes a novel approach based on modeling of the physical behavior of the bearing. An extended Kalman filter including a novel strain model is applied for signal conditioning, whereas an unscented Kalman filter including a semianalytical bearing model is proposed for reconstruction of the bearing load. An experimental study in both laboratory and field conditions shows that the proposed cascaded Kalman filtering approach leads to accurate estimates for all four considered bearings loads in various loading conditions. Besides an improvement on accuracy, the novel approach leads to a reduction in calibration effort.

中文翻译：

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利用外圈变形估算轴承力和力矩的基于模型的方法


轴承载荷估计将为状态监测和系统控制领域提供有价值的补充。尽管各大轴承制造商都投入了大量精力进行开发，但尚未有产品解决方案上市。这可以归因于应变测量条件的复杂性及其相对于轴承载荷的非线性。为了解决这些问题，本文提出了一种基于轴承物理行为建模的新方法。包括新颖应变模型的扩展卡尔曼滤波器用于信号调节，而包括半解析轴承模型的无迹卡尔曼滤波器被提议用于轴承载荷的重建。实验室和现场条件下的实验研究表明，所提出的级联卡尔曼滤波方法可以准确估计各种负载条件下所有四个考虑的轴承负载。除了提高准确性之外，这种新颖的方法还可以减少校准工作。