Machinery Diagnostics Using Mechanistic and Data-Driven Models

使用机械和数据驱动模型进行机械诊断

• 批准号: RGPIN-2017-04788
• 负责人: Lipsett, Michael
• 金额: $ 3.21万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688794
• 关键词: Machinery; Diagnostics; Using; Mechanistic; Data

All machines are prone to some kind of failure. Machinery diagnostics allows maintenance to be done before a catastrophic failure occurs, by observing some aspect of machine behaviour that indicates an impending problem and then using signal processing and feature extraction to determine whether a fault is present. Some techniques can also predict the remaining useful life of the machine in certain conditions. Unfortunately, limited observability and machine-specific approaches to condition monitoring make data-driven models difficult to apply in general applications. New sensor technologies and embedded sensor network standards hold promise to significantly improve the observability of faults in machinery, both for new equipment and for retrofits to existing components and systems. The new instalment of Discovery-Grant based research will test the hypothesis that improving observability of damage mechanisms will enable system reliability improvements. Individual methods and combined methods of fault classification will be assessed for laboratory-based fault diagnosis case studies of impact faults in rotating machinery and process equipment. Physics-based modeling will be used to evaluate how to measure faults sensitively in rolling-element bearings and process equipment subject to impact wear. The laboratory systems will be publicly available so that others can test the performance of their method using benchmark datasets. Fault models of chronic impact events apply to a wide range of rotating equipment & process units, for process plants, wind turbine generators, and vehicles. Parametric fault identification models will be of direct benefit to industry and to other researchers. Comparative methods for ranking fault severity will yield better methods to predict equipment life and improve maintenance effectiveness. Datasets for machine damage cases will allow researchers to compare diagnostic techniques definitively, contributing to best practices. Improved fault diagnostics will contribute to more durable repairable equipment and products with longer service life, which is crucial for a more sustainable technological future.

所有的机器都容易出某种故障。机器诊断允许在灾难性故障发生之前进行维护，通过观察机器行为的某些方面，表明即将发生的问题，然后使用信号处理和特征提取来确定是否存在故障。一些技术还可以预测机器在某些条件下的剩余使用寿命。不幸的是，有限的可观察性和特定于机器的状态监测方法使得数据驱动模型难以应用于一般应用。新的传感器技术和嵌入式传感器网络标准有望显着提高机器故障的可观察性，无论是新设备还是对现有组件和系统的改造。新一批的发现补助金为基础的研究将测试的假设，提高损坏机制的可观察性将使系统的可靠性提高。将评估故障分类的单个方法和组合方法，用于旋转机械和过程设备中的冲击故障的基于实验室的故障诊断案例研究。基于物理的建模将用于评估如何在滚动轴承和受冲击磨损的过程设备中灵敏地测量故障。实验室系统将公开提供，以便其他人可以使用基准数据集测试其方法的性能。慢性冲击事件的故障模型适用于广泛的旋转设备和过程单元，用于加工厂，风力涡轮机发电机和车辆。参数化故障识别模型将直接有益于工业和其他研究人员。故障严重度排序的比较方法将产生更好的方法来预测设备寿命和提高维护效率。机器损坏案例的数据集将使研究人员能够明确地比较诊断技术，为最佳实践做出贡献。改进的故障诊断将有助于更耐用的可维修设备和产品，具有更长的使用寿命，这对于更可持续的技术未来至关重要。