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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=667902
• 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伤和故障的最关键来源。在现代工程中，静载问题已被先进的计算和有限元方法深入研究和解决，但具有非线性、不确定性和非平稳性的动力问题还没有得到明确的解决，特别是对于现有的机构和结构。理解和预测此类真实系统的动态行为的最好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“运行中”的水平，考虑到系统的实际结构和机械条件及其运行负荷。因此，工作模式分析(OMA)面临的挑战是工作过程中几何、质量和刚度的变化、存在的机械和结构损伤、局部振型、未知的谐波和随机激励以及高噪声水平。*提出的研究方案旨在利用有限元方法、时间序列向量自回归(VAR)模型、卡尔曼滤波和人工智能来研究和阐明上述问题。该计划的目标是开发项目，以解决OMA中的研究问题并产生创新，用于分析和监测受非平稳振动影响的现有机械系统。通过对平稳和短时非平稳振动的初步研究，该方法表现出了优异的性能，并在精度、稳定性和鲁棒性方面得到了有效的应用。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。在该计划下，将探索关于非平稳振动的各种研究课题：传感器数量和位置的优化、损伤的定位和估计、阻尼的识别、有限元模型的更新、用于检测损伤起始和传播的时频阻尼技术的开发以及用于信号处理的自适应模型更新。提出了一种结合运行模式监测和有限元模型修正的混合运行监测(HOM)技术。在此基础上，开发了一种诊断-预测-健康监测技术，用于机械和结构的管理、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(几何形状和刚度不同的研磨机器人)，并将考虑特定的运行条件和各种类型的损坏。**