Creation of LIVE Digital Twin for Prognostics and Predictive Maintenance of Rotating Machineries (LIVE for 2PM)

创建用于旋转机械的预测和预测性维护的实时数字孪生（LIVE for 2PM）

• 批准号: 570439-2021
• 负责人: Barari, Ahmad
• 金额: $ 12.39万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 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=723390
• 关键词: Creation; LIVE; Digital; Twin; Prognostics

Due to the pandemic, the regular in-person inspection of the critical Rotating Machineries (RM) in hydropower, wind energy, and transportation were interrupted. Unfortunately, RM typically deteriorate their balance condition dynamically, due to wear, thermal bending, dirt collection, insulation failure, partial discharge, fault currents, thermal stress due to overloading, etc., and gradually develop concerning vibratory unbalance responses affecting RM performance and lifespan. Failure of RM in applications such as turbomachines, electromotors, generators, pumps, jet fans, etc. creates enormous unscheduled repair costs and more importantly may put human life in danger. This project aims to develop methodologies required to employ the concept of LIVE Digital Twin for Prognostics and Predictive Maintenance (LIVE for 2PM) of RM highly critical assets. The term "LIVE" refers to the first letters of the four phases in the methodology, i.e. Learn, Identify, Verify, and Extend. The key feature in LIVE digital Twins is multi-physics simulation solutions developed in alternative levels and modes, which are supported by artificial neural network and deep learning tools. LIVE Digital Twin allows Learning the behavioral patterns of individual RM considering their specifications, environment and customized factors. It Identifies the locations and types of sensors and the sensing procedures to Verify the possibilities for progressively increasing defects. It Extends the life of RM by advising predictive maintenance avoiding its failure. Implementation of LIVE Digital Twin not only reduces the needs for in-person inspection during the pandemic, but also can result in significant improvement in asset management of the critical Canadian infrastructures for energy and transportation sectors with predictive maintenance, reduced cost of repair, reduced frequency in replacing parts, shorter down-time of the corresponding highly critical infrastructures, reduced waste, and increased life-span of critical machines. All of these result in significant economic growth for a chain of industries and also a noticeable welfare of the society.

受疫情影响，水电、风能、交通运输等关键旋转机械（RM）的定期现场检查被中断。不幸的是，由于磨损、热弯曲、污垢收集、绝缘故障、局部放电、故障电流、过载引起的热应力等，RM 通常会动态地恶化其平衡条件，并逐渐发展出影响 RM 性能和寿命的振动不平衡响应。涡轮机、电动机、发电机、泵、射流风机等应用中的 RM 故障会造成巨大的计划外维修成本，更重要的是可能危及人类生命。该项目旨在开发采用实时数字孪生概念对 RM 高度关键资产进行预测和预测维护（LIVE for 2PM）所需的方法。术语“LIVE”是指该方法中四个阶段的第一个字母，即学习、识别、验证和扩展。 LIVE 数字孪生的关键特征是以替代级别和模式开发的多物理场仿真解决方案，并得到人工神经网络和深度学习工具的支持。 LIVE Digital Twin 允许学习个体 RM 的行为模式，考虑他们的规格、环境和定制因素。它确定传感器的位置和类型以及传感程序，以验证缺陷逐渐增加的可能性。它通过建议预测性维护避免发生故障来延长 RM 的使用寿命。 LIVE Digital Twin的实施不仅减少了大流行期间的现场检查需求，而且还可以通过预测性维护、降低维修成本、降低频率，显着改善加拿大能源和运输部门关键基础设施的资产管理在更换零件时，可以缩短相应高度关键基础设施的停机时间、减少浪费并延长关键机器的使用寿命。所有这些都带来了一系列产业链的显着经济增长，也带来了显着的社会福利。