Khadim Qasim, Kiani-Oshtorjani Mehran, Jaiswal Suraj, Matikainen Marko K, Mikkola Aki
Department of Mechanical Engineering, LUT School of Energy Systems, Lappeenranta University of Technology, 53850 Lappeenranta, Finland.
Department of Energy Technology, LUT School of Energy Systems, Lappeenranta University of Technology, 53850 Lappeenranta, Finland.
Sensors (Basel). 2021 Jul 24;21(15):5029. doi: 10.3390/s21155029.
The estimation of the parameters of a simulation model such that the model's behaviour matches closely with reality can be a cumbersome task. This is due to the fact that a number of model parameters cannot be directly measured, and such parameters might change during the course of operation in a real system. Friction between different machine components is one example of these parameters. This can be due to a number of reasons, such as wear. Nevertheless, if one is able to accurately define all necessary parameters, essential information about the performance of the system machinery can be acquired. This information can be, in turn, utilised for product-specific tuning or predictive maintenance. To estimate parameters, the augmented discrete extended Kalman filter with a curve fitting method can be used, as demonstrated in this paper. In this study, the proposed estimation algorithm is applied to estimate the characteristic curves of a directional control valve in a four-bar mechanism actuated by a fluid power system. The mechanism is modelled by using the double-step semi-recursive multibody formulation, whereas the fluid power system under study is modelled by employing the lumped fluid theory. In practise, the characteristic curves of a directional control valve is described by three to six data control points of a third-order B-spline curve in the augmented discrete extended Kalman filter. The results demonstrate that the highly non-linear unknown characteristic curves can be estimated by using the proposed parameter estimation algorithm. It is also demonstrated that the root mean square error associated with the estimation of the characteristic curve is 0.08% with respect to the real model. In addition, all the errors in the estimated states and parameters of the system are within the 95% confidence interval. The estimation of the characteristic curve in a hydraulic valve can provide essential information for performance monitoring and maintenance applications.
要使仿真模型的参数估计结果能让模型行为与实际情况紧密匹配，可能是一项繁琐的任务。这是因为许多模型参数无法直接测量，而且这些参数在实际系统运行过程中可能会发生变化。不同机器部件之间的摩擦就是这些参数的一个例子。这可能是由多种原因造成的，比如磨损。然而，如果能够准确地定义所有必要参数，就可以获取有关系统机械性能的关键信息。反过来，这些信息可用于特定产品的调整或预测性维护。为了估计参数，可以使用带有曲线拟合方法的增强离散扩展卡尔曼滤波器，本文将对此进行说明。在本研究中，所提出的估计算法被应用于估计由流体动力系统驱动的四杆机构中方向控制阀的特性曲线。该机构采用双步半递归多体公式进行建模，而所研究的流体动力系统则采用集总流体理论进行建模。在实际应用中，在增强离散扩展卡尔曼滤波器中，方向控制阀的特性曲线由三阶B样条曲线的三到六个数据控制点来描述。结果表明，使用所提出的参数估计算法可以估计高度非线性的未知特性曲线。还表明，与特性曲线估计相关的均方根误差相对于实际模型为0.08%。此外，系统估计状态和参数中的所有误差都在95%置信区间内。液压阀特性曲线的估计可为性能监测和维护应用提供关键信息。
