Dynamic unbalance identification in steady-state rotating machinery: A hybrid methodology integrating physical and data-driven techniques

IF 4.3 2区 工程技术 Q1 ACOUSTICS
Miguel Angelo de Carvalho Michalski, Italo Skovroski de Melo, Gilberto Francisco Martha de Souza
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Abstract

Rotating machinery plays a strategic role in key industrial sectors, making its analysis a subject of great interest for both academia and industry. Effective maintenance planning for this equipment is essential for asset management and for meeting current industrial requirements. To address this demand, this work presents a novel unbalance identification approach based on a digital representation of a rotating machine's dynamic behavior in relation to the development of specific faults. A hybrid methodology is proposed, integrating Finite Element Modeling, a Kalman Filter for parameter estimation, and Referenced Moving Window Principal Component Analysis. This Principal Component Analysis extension enhances vibration pattern recognition, enabling accurate fault quantification directly from slight changes in the system's steady-state behavior. The methodology uniquely eliminates the need for phase angle measurements, facilitating continuous monitoring of unbalance progression in steady-state conditions. Two case studies demonstrate the methodology's potential: one utilizing synthetic data from a Floating Production Storage and Offloading centrifugal compressor unit, and the other based on real data from a hydroelectric turbine-generator. These studies illustrate the integration of computational modeling, data-driven analysis, and monitored vibration data, achieving robust and accurate unbalance identification. This approach provides valuable insights into current capabilities and opens promising pathways for future applications, particularly in the digital twin domain for rotating machinery.
稳态旋转机械的动态不平衡识别:综合物理和数据驱动技术的混合方法
旋转机械在主要工业部门中发挥着战略性作用,因此其分析成为学术界和工业界都非常感兴趣的课题。对这些设备进行有效的维护规划对于资产管理和满足当前的工业要求至关重要。为满足这一需求,本研究提出了一种新颖的不平衡识别方法,该方法基于旋转机器动态行为与特定故障发展的关系的数字表示。本文提出了一种混合方法,将有限元建模、用于参数估计的卡尔曼滤波器和参考移动窗主成分分析法融为一体。这种主成分分析扩展增强了振动模式识别能力,可直接从系统稳态行为的细微变化中准确量化故障。该方法无需进行相位角测量,便于在稳态条件下持续监测不平衡的发展。两个案例研究证明了该方法的潜力:一个是利用浮式生产储卸油离心压缩机组的合成数据,另一个是基于水力涡轮发电机的真实数据。这些研究说明了计算建模、数据驱动分析和监测振动数据的整合,实现了稳健而准确的不平衡识别。这种方法为当前的能力提供了宝贵的见解,并为未来的应用开辟了前景广阔的道路,尤其是在旋转机械的数字孪生领域。
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来源期刊
Journal of Sound and Vibration
Journal of Sound and Vibration 工程技术-工程:机械
CiteScore
9.10
自引率
10.60%
发文量
551
审稿时长
69 days
期刊介绍: The Journal of Sound and Vibration (JSV) is an independent journal devoted to the prompt publication of original papers, both theoretical and experimental, that provide new information on any aspect of sound or vibration. There is an emphasis on fundamental work that has potential for practical application. JSV was founded and operates on the premise that the subject of sound and vibration requires a journal that publishes papers of a high technical standard across the various subdisciplines, thus facilitating awareness of techniques and discoveries in one area that may be applicable in others.
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