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

这笔工程研究启动(ERI)拨款将资助研究，以实现机械的可靠运行和具有成本效益的维护，其中液压缸执行器在其中发挥核心作用-对运输、制造业、建筑业和农业至关重要-从而促进科学进步和国家繁荣。由于其高功率密度和大的受力能力，液压缸在许多工业应用中都是值得信赖的主力。根据其设计，它还容易受到各种关键故障的影响，例如密封故障、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，并因停机、永久性损坏或操作员受伤而导致高昂的成本。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相结合的方法来开发一种新的诊断方法，该方法考虑了系统的非线性和闭环系统运行。这种诊断范式还有望在包括电动汽车在内的各种能源和电力传输系统中得到应用。努力将研究成果传播到密歇根州东南部的当地工业，本科生的研究机会，以及在课程工作中整合建模和系统诊断原理，将产生更广泛的社会影响。本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学和机器学习技术。这种基础性的贡献将通过设计一种新的残差产生器来从传感器测量中提取故障特征，以及设计残差评估器，该残差评估器通过将残差与特定操作条件下的规定阈值进行比较来做出诊断决定。几项任务将通过理论建模、数值模拟和物理实验相结合来进行，包括使用系统动力学的非线性、逆、频域表示进行全面的故障分析，以及使用自适应数字孪生模型在闭环系统操作下对这种非线性逆模型的参数估计算法的公式和测试。将进行硬件在环仿真，以验证该方法，并调查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，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.