ERI: Hydraulic Cylinder Diagnostics Using Nonlinear Inverse Model Estimation and Frequency Domain Analysis

ERI：使用非线性逆模型估计和频域分析进行液压缸诊断

• 批准号: 2301535
• 负责人: Yongsoon Yoon
• 金额: $ 19.99万
• 依托单位: Oakland University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2301535&HistoricalAwards=false
• 关键词: ERI; Hydraulic; Cylinder; Diagnostics; Using

This Engineering Research Initiation (ERI) grant will fund research that enables reliable operation and cost-effective maintenance of machinery in which hydraulic cylinder actuators play a central role—of critical importance to the transportation, manufacturing, construction, and agricultural industries—thereby promoting the progress of science and advancing the national prosperity. Given its high power density and large force capacity, the hydraulic cylinder is a trusted workhorse across many industrial applications. By its design, it is also vulnerable to a variety of critical faults, such as seal failures, leakage, fluid contamination, and reduced load-carrying capacity, that may negatively affect performance and efficiency, and result in high costs from downtime, permanent damage, or operator injury. To ensure that such faults can be accurately detected and identified, this project relies on a combination of physics-based modeling and innovative frequency domain analysis to develop a novel diagnostic methodology that accounts for system nonlinearities and closed-loop operation. Such a diagnostic paradigm is also anticipated to find application in a variety of energy and power transmission systems, including electric vehicles. Efforts to disseminate research outcomes to local industry in Southeast Michigan, undergraduate research opportunities, and integration of modeling and systems diagnostics principles in coursework will produce broader societal impact.This research aims to develop the foundations for a comprehensive systems diagnostics methodology for hydraulic cylinders that relies on physics-based modeling rather than statistics and machine learning techniques, as has been common in recent years. Such foundational contributions will be made through the design of a new residual generator that extracts faulty features from sensor measurements, as well as of a residual evaluator that makes a diagnostic decision by comparing the residual with the prescribed threshold under a certain operating condition. Several tasks will be pursued through a combination of theoretical modeling, numerical simulations, and physical experiments, including comprehensive fault analysis using a nonlinear, inverse, frequency domain representation of the system dynamics, and formulation and testing of a parameter estimation algorithm for such a nonlinear inverse model under closed-loop operation using an adaptive digital twin. Hardware-in-the-loop simulations will be performed to validate the methodology and investigate its robustness also in the presence of multiple simultaneous faults.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项工程研究启动 (ERI) 拨款将资助研究，使液压缸执行器发挥核心作用的机械能够可靠运行和经济高效地维护，对运输、制造、建筑和农业行业至关重要，从而促进科学进步和国家繁荣。由于其高功率密度和大作用力，液压缸是许多工业应用中值得信赖的主力。由于其设计，它还容易受到各种严重故障的影响，例如密封失效、泄漏、流体污染和承载能力降低，这些故障可能会对性能和效率产生负面影响，并导致因停机、永久性损坏或操作员受伤而造成的高昂成本。为了确保能够准确地检测和识别此类故障，该项目依靠基于物理的建模和创新的频域分析的结合来开发一种新颖的诊断方法，该方法可以解释系统非线性和闭环操作。这种诊断范例也有望在各种能源和电力传输系统中找到应用，包括电动汽车。向密歇根州东南部当地行业传播研究成果、本科生研究机会以及将建模和系统诊断原理整合到课程中的努力将产生更广泛的社会影响。这项研究旨在为液压缸的综合系统诊断方法奠定基础，该方法依赖于基于物理的建模，而不是近年来常见的统计和机器学习技术。这种基础性的贡献将通过设计一个新的残差生成器来实现，该残差生成器从传感器测量中提取错误特征，以及一个残差评估器，该残差评估器通过在特定操作条件下将残差与规定阈值进行比较来做出诊断决策。将通过理论建模、数值模拟和物理实验的结合来完成多项任务，包括使用系统动力学的非线性逆频域表示进行综合故障分析，以及使用自适应数字孪生在闭环操作下制定和测试这种非线性逆模型的参数估计算法。将进行硬件在环仿真来验证该方法并研究其在同时存在多个故障的情况下的稳健性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。