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Stochastic model updating of rotor support parameters using Bayesian approach

机译：使用贝叶斯方法转子支撑参数的随机模型更新

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摘要

Uncertainties due to assembling, installation, and operational conditions are extensively involved in rotating systems' parameters. Stochastic characteristics of these parameters may seriously affect rotating systems' vibrational characteristics, such as their critical speeds and vibration amplitudes. These effects make the variability of parameters in the modeling of rotary machines inevitable. In rotating machinery, material properties and geometric parameters of the rotor, bearing characteristics and supports stiffness determine the system's dynamic response. Stochastic model updating methods consider model response variability and allocate them to the model parameters; however, they are not commonly employed in rotor dynamics, and deterministic approaches are still prevalent in this field. Due to the cost and efforts needed to set up experiments and obtain outcomes that reflect the machine's actual characteristics, stochastic updating practices of industrial rotating systems are rarely reported in the literature. This paper adopts an appropriate parameter selection procedure and suitable sampling strategy for stochastic model updating to investigate variability in the dynamic behavior of a complex turbo compressor rotor-bearing-support system, leading to successful parameter identification results. The compressor rotor is mounted on hydrodynamic journal bearings with speed-dependent stiffness and damping. Due to the rotating system complex model, a variance-based global sensitivity method is employed for parameter selection to eliminate non-influential parameters in the model updating and to alleviate updating complexity and computational burden. The Bayesian approach in the stochastic model updating is applied to estimate parameter uncertainty in the rotor with speed-dependent characteristics. Advanced Markov chain Monte Carlo sampling method using delayed rejection adaptive Metropolis algorithm is employed in the stochastic model updating. The updating procedure obtains marginal posterior probabilities of parameters, and uncertain parameter distributions are evaluated using the maximum entropy criterion.
机译：由于组装，安装的不确定性，以及操作条件广泛参与旋转系统的参数。这些参数中的随机特征可能会严重影响旋转系统的振动特性，如它们的临界速度和振动振幅。这些影响使得参数的变异不可避免的旋转机械的造型。在旋转机械，材料特性和转子的几何参数，轴承的特点和支撑刚度确定系统的动态响应。随机模型更新方法考虑模型响应变化，并将其分配给模型参数;然而，他们中不常见的转子动力学使用，并确定方法在该领域仍然盛行。由于成本和建立实验并获得体现了机器的实际性能结果所需的努力，工业旋转系统的随机更新的做法在文献中鲜有报道。本文采用了随机模型更新的合适参数选择过程和合适的采样策略来调查在一个复杂的涡轮压缩机转子轴承支撑系统的动态行为的变化，导致成功的参数识别结果。压缩机转子被安装在与速度相关的刚度和阻尼的流体动力轴颈轴承。由于旋转系统复杂模型，被用于参数选择一个基于方差的全局灵敏度的方法来消除在模型更新非有影响的参数，并减轻更新的复杂性和计算量。在随机模型更新贝叶斯方法被应用来估计与速度相关的特性的转子参数的不确定性。采用在随机模型更新使用延迟拒绝自适应Metropolis算法先进马尔可夫链蒙特卡洛抽样方法。更新过程获得的参数边缘后验概率，以及不确定参数分布使用最大熵判据评价。

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