Rotating machines are widely used in industry. They are composed of rotative components such as shaft and blades, which are connected to a static support structure by bearings. Rolling bearings and fluid lubricated bearings are commonly used for this function. However, in the last decades, active magnetic bearings (AMB) have gained importance in some applications. These bearings can support the shaft of such machines without contact and apply active control through electromagnetic forces. On the other hand, uncertainties are inherent to engineering systems and they should be quantified to obtain better models. Bayesian inference is an interesting option to identify or update the probability distributions of a random variable. Monte Carlo via Markov chains is usually implemented to solve the inference, but its processing time can be long. By using generalized polynomial chaos expansion, the solution process is accelerated. This work aims to identify the AMB parameters and unbalance force. After the identification, the stochastic response is evaluated and compared with experimental data from a test rig supported by AMB. The robustness of the identification is evaluated by inserting noise in the signal. A sensitivity analysis is performed through Sobol indices to evaluate if the AMB uncertainties should be considered in future analyses.

旋转机械广泛应用于工业领域。它们由轴和叶片等旋转部件组成，它们通过轴承连接到静态支撑结构。滚动轴承和流体润滑轴承通常用于此功能。然而，在过去的几十年中，主动磁轴承 (AMB) 在某些应用中变得越来越重要。这些轴承可以无接触地支撑此类机器的轴，并通过电磁力进行主动控制。另一方面，不确定性是工程系统固有的，应该对其进行量化以获得更好的模型。贝叶斯推理是识别或更新随机变量概率分布的一个有趣选项。通常通过马尔可夫链实现蒙特卡罗来解决推理，但其处理时间可能很长。通过使用广义多项式混沌展开，加速了求解过程。这项工作旨在识别 AMB 参数和不平衡力。识别后，随机响应被评估并与来自 AMB 支持的测试台的实验数据进行比较。通过在信号中插入噪声来评估识别的鲁棒性。通过 Sobol 指数进行敏感性分析，以评估是否应在未来分析中考虑 AMB 不确定性。通过在信号中插入噪声来评估识别的鲁棒性。通过 Sobol 指数进行敏感性分析，以评估是否应在未来分析中考虑 AMB 不确定性。通过在信号中插入噪声来评估识别的鲁棒性。通过 Sobol 指数进行敏感性分析，以评估是否应在未来分析中考虑 AMB 不确定性。