Hybrid monitoring, diagnostics and prognostics on machines and structures

机器和结构的混合监控、诊断和预测

• 批准号: RGPIN-2016-05859
• 负责人: Vu, VietHung
• 金额: $ 1.68万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709894
• 关键词: Hybrid; monitoring; diagnostics; prognostics; machines

Vibrations are the most critical source of structural and mechanical damage and failure. In modern engineering, while static loading has been thoroughly investigated and resolved by advanced computing and finite element methods, dynamic problems with nonlinearities, uncertainty and non-stationarity have not yet been solved explicitly, especially for existing mechanisms and structures. The best approach to understand and predict the dynamic behavior of such real-life systems is to conduct experimental analysis, diagnostics and prognostics. The core of this experimental investigation is modal analysis. Over the past two decades, knowledge of modal analysis has developed to the “in operation” level, taking into account actual structural and mechanical conditions of systems and their operating loads. Consequently, the challenges faced in operational modal analysis (OMA) are the variations in geometry, mass and stiffness during operation, the existing mechanical and structural damage, local modes, unknown harmonic and random excitations, and high noise levels. The research program proposed is designed to study and elucidate the problems above using the finite element method, time series vector autoregressive (VAR) model, Kalman filter and artificial intelligence. The objective of the program is to develop projects to resolve research issues and spawn innovations in OMA for the analysis and monitoring of existing mechanical systems subject to non-stationary vibrations. Through initial researches on stationary and short-time non-stationary vibrations, this method has demonstrated extraordinary performance and led to efficient applications in terms of precision, stability and robustness. Moreover, this methodology supports the handling of a large number of sensors and has huge potential for the development of advanced functionalities. Under this program, various research topics will be explored on non-stationary vibrations: optimization of sensor number and location, localization and estimation of damage; identification of damping; updating the finite element model; development of a time-frequency-damping technique for the detection of damage initiation and propagation; and adaptive model updating for signal processing. A new technique is proposed called “hybrid operational monitoring” (HOM), which combines operational modal monitoring and finite model updating. On this basis, a diagnostic-prognostic-health monitoring technology is developed for machine and structure management, prediction and maintenance. The program will focus on applications for different machines, e.g., rotating machines (hydraulic turbines and wind turbines with harmonic excitations) and processing machines (grinding robots with variations in geometry and stiffness), and will consider specific operating conditions and the various types of damage.

振动是结构和机械损坏和故障的最关键来源。在现代工程中，虽然静态载荷已经被先进的计算和有限元方法彻底研究和解决，但具有非线性、不确定性和非平稳性的动态问题还没有明确解决，特别是对于现有的机构和结构。理解和预测这种真实系统的动态行为的最佳方法是进行实验分析、诊断和预测。本实验研究的核心是模态分析。在过去的二十年里，模态分析的知识已经发展到“运行”的水平，考虑到系统的实际结构和机械条件及其工作载荷。因此，在操作模态分析（OMA）所面临的挑战是在操作过程中的几何形状，质量和刚度的变化，现有的机械和结构损伤，局部模式，未知的谐波和随机激励，以及高噪声水平。 本研究计画旨在利用有限元素法、时间序列向量自回归（VAR）模型、卡尔曼滤波器及人工智能等方法，研究并阐明上述问题。该计划的目标是开发项目，以解决研究问题，并在OMA中产生创新，以分析和监测现有的机械系统受到非平稳振动。通过对平稳和短时非平稳振动的初步研究，该方法在精度、稳定性和鲁棒性方面表现出了优异的性能，并得到了有效的应用。此外，这种方法支持处理大量传感器，并具有开发高级功能的巨大潜力。根据这一计划，各种研究课题将探讨非平稳振动：优化传感器的数量和位置，定位和估计损坏;识别阻尼;更新有限元模型;开发一个时间频率阻尼技术检测损坏的启动和传播;和自适应模型更新信号处理。提出了一种新的技术，称为“混合运行监测”（HOM），它结合了运行模式监测和有限的模型更新。在此基础上，发展了一种诊断-预测-健康监测技术，用于机器和结构的管理、预测和维护。该计划将侧重于不同机器的应用，例如，旋转机器（具有谐波激励的水轮机和风力涡轮机）和加工机器（具有不同几何形状和刚度的磨削机器人），并将考虑特定的操作条件和各种类型的损坏。