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

这项工程研究启动（ERI）赠款将资助能够可靠运行和具有成本效益的机械的研究，其中液压缸执行器对运输，制造业，建筑业和农业行业至关重要，从而促进科学进步和促进国家繁荣。由于其高功率密度和大力容量，液压缸是许多工业应用中值得信赖的主力。由于其设计，它也容易受到各种关键故障的影响，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，并导致停机、永久性损坏或操作人员受伤造成的高成本。为了确保能够准确地检测和识别此类故障，该项目依赖于基于物理的建模和创新的频域分析相结合，以开发一种考虑系统非线性和闭环操作的新型诊断方法。这种诊断范例也有望在各种能源和电力传输系统中得到应用，包括电动汽车。努力将研究成果传播到密歇根东南部的当地产业，为本科生提供研究机会，并在课程中整合建模和系统诊断原则，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和机器学习技术。这样的基础贡献将通过设计一个新的残差发生器，从传感器测量中提取故障特征，以及残差评估器，在一定的操作条件下，通过将残差与规定的阈值进行比较来做出诊断决策。几个任务将通过理论建模、数值模拟和物理实验的结合来进行，包括使用系统动力学的非线性、逆、频域表示进行综合故障分析，以及使用自适应数字孪生在闭环操作下为这种非线性逆模型制定和测试参数估计算法。将进行硬件在环仿真来验证该方法，并研究其在存在多个同时故障时的鲁棒性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.