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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737285
• 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).

操作和维护活动的优化将大大提高加拿大大多数工业和商业部门的效率和生产力。然而，这需要收集和适当使用与系统性能、退化和故障相关的有意义的参数。大多数机械和结构部件和系统的现有监测和维护决策支持策略仍然需要人工监督和决策，特别是当系统被考虑为复杂，移动，远程和/或在非稳态模式下运行时。这一活动的重要部分的自动化是迫切需要的。当有大量的历史数据可用时，才有可能进行故障检测和诊断。数据驱动的方法在这里展示了巨大的潜力，因为它们能够对数据进行排序并识别表示错误条件的模式。然而，当只有有限的数据可用来表示故障和/或退化时，这些方法受到严重限制。新的递归数据处理策略（特别适用于从旋转机械收集的动态信号）将被探索，以提高这些方法在数据稀缺时的鲁棒性。此外，当使用基于数据的方法时，预测更具挑战性，因为它们只代表过去的经验。将开发新技术，整合在线收集的新数据，以便快速更新模型以改进预测。基于物理的模型是极好的预测工具。这些模型的大小和复杂性可能相差很大，但是修改以允许合并组件故障或系统退化相对容易。这有利于系统或组件的性能预测。新的模型将开发齿轮齿，行星齿轮系统，和电机/发电机系统。将多个来源的信息组合在一起可以显著提高置信度。混合数据驱动和基于物理的协议将使两者的优点得到增强，缺点最小化。这种混合方式有利于优化系统运维。在这个风向标上的初步工作已经表明，精确度有可能得到显著提高。进一步的发展可能会导致系统退化检测、故障诊断和故障预测方面的巨大改进。为了满足自动驾驶系统（汽车、轻轨列车、风力发电机）的快速普及，迫切需要在混合策略设计及其在更广泛的行业和商业应用中取得突破。