Hybrid Data-driven Physics-based Modeling for Machine Fault Detection, Diagnosis, and Prediction

用于机器故障检测、诊断和预测的混合数据驱动的基于物理的建模

• 批准号: RGPIN-2019-03967
• 负责人: Mechefske, Christopher
• 金额: $ 2.84万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759862
• 关键词: Hybrid; Data; driven; Physics; based

Optimization of operation and maintenance activities would result in huge efficiency and productivity improvements across most industrial and commercial sectors in Canada. However, this requires the collection and appropriate use of meaningful parameters that correlate with system performance, degradation, and failure. Existing monitoring and maintenance decision support strategies for most mechanical and structural components and systems still require human supervision and decision making, especially when the system being considered is complex, mobile, remote and/or operates in non-steady state modes. Automation of significant parts of this activity is urgently needed. When large amounts of historical data are available, fault detection and diagnosis is possible. Data-driven methods demonstrate huge potential here because of their ability to sort data and recognize patterns representing faulty conditions. However, when only limited data is available that represents failure and/or degradation, these methods are severely constrained. New recursive data processing strategies (particularly appropriate for dynamic signals collected from rotating machinery) will be explored to improve the robustness of these methods when data is scarce. Additionally, prediction is more challenging when using data-based methods because they only represent past experience. New techniques will be developed that can integrate new data collected on-line allowing for rapidly updated models for improved prognostics. Physics-based models are excellent tools for prediction. These models may range dramatically in size and complexity, but modification to allow incorporation of component faults or system degradation is relatively easy. This facilitates system or component performance prediction. New models will be developed for gear teeth, planetary gear systems, and motor/generator systems. Combining information from multiple sources significantly improves the confidence level. Hybrid data-driven and physics-based protocols will allow the advantages of both to be enhanced and the disadvantages to be minimized. Such hybrid approaches will facilitate the optimization of system operation and maintenance. Preliminary work in this vane has already shown that dramatic improvements in accuracy are possible. Further development could result in huge improvements in system degradation detection, fault diagnosis and failure prediction. A breakthrough in hybrid strategy designs and their application across a wider array of industries and commercial applications is critically needed to service the rapidly expanding adoption of autonomous systems (cars, light rail trains, wind turbine generators).

优化运营和维护活动将导致加拿大大多数工业和商业领域的效率和生产率提高。但是，这需要收集和适当使用与系统性能，降解和失败相关的有意义的参数。对于大多数机械和结构组件和系统的现有监视和维护决策支持策略仍然需要人类的监督和决策，尤其是在考虑到系统复杂，移动，远程和/或以非稳态状态模式运行时。迫切需要进行此活动的重要部分的自动化。当有大量的历史数据可用时，可以进行故障检测和诊断。数据驱动的方法在这里显示出巨大的潜力，因为它们能够对数据进行分类和识别代表错误条件的模式。但是，只有有限的数据可用来代表失败和/或降解，这些方法将受到严格限制。将探讨新的递归数据处理策略（特别适合从旋转机械收集的动态信号），以提高数据稀缺时这些方法的鲁棒性。此外，在使用基于数据的方法时，预测更具挑战性，因为它们仅代表过去的经验。将开发新的技术，可以集成在线收集的新数据，以允许快速更新的模型以改善预后。 基于物理的模型是预测的绝佳工具。这些模型的尺寸和复杂性可能很大，但是修改以允许组合故障或系统降解相对容易。这有助于系统或组件性能预测。新型号将用于齿轮齿，行星齿轮系统和电动机/发电机系统。将来自多个来源的信息结合起来可显着提高置信度。混合数据驱动和基于物理的协议将允许增强两者的优点，并可以最大程度地减少缺点。这种混合方法将促进系统操作和维护的优化。该叶片的初步工作已经表明，准确性的急剧提高是可能的。进一步的发展可能会导致系统降解检测，故障诊断和故障预测的巨大改善。为了迅速扩大自主系统的采用（汽车，轻轨列车，风力涡轮机发电机），至少需要在更广泛的行业和商业应用中进行混合策略设计及其在更广泛的行业和商业应用中的突破。