MEMS for diagnostic and health monitoring of complex systems

用于复杂系统诊断和健康监测的 MEMS

• 批准号: RGPIN-2014-06532
• 负责人: Stiharu, Ion
• 金额: $ 1.97万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=567871
• 关键词: MEMS; diagnostic; health; monitoring; complex

The proposed research program is targeting the development of methodologies that will enable to develop models of complex systems that are used to establish their state and condition. Complex systems range from engines to plants and from cells to beings. This is the rationale of the proposed research. Thus, some specific features of the systems need to be defined and based on the findings a methodology that enables development of a model will be formulated. The research has been inspired by the so much kept silent experiment carried out by Google on self-driving vehicles. A recent project performed by the applicant and his last graduated PhD student yield very interesting output that could be linked to the driver-less vehicle approach. The project focused on modeling vehicle and impaired driver. Hence, the modeling in this case will target the condition of the vehicle in the context of traveling through the traffic. A self-driving system is a regular vehicle that is equipped with a set of sensors and pre-determined information (the map) about the road to be traveled, computation capabilities as well as actuators to perform the corrective action to avoid collision of any kind. The driver-less vehicle may need to consider reduced set of sensors such that such car may become affordable. However, the required information needs to be made available and other means will be considered within this project. Thus, the information about the previewed road and the static or moving obstacles is planned to be collected form the adjacent vehicles. The model of the system will be different but the performances need to remain within the safety conditions. The applicant plans to approach certain aspects that lead to the long term research plan. This is the development of sensors that could operate in extremely harsh environment and that will enable real time prediction of the life of turbine engines. This involves the research on the development of new type of sensors. The program will have as a plan in short term to train five HQP – 2 PhD and 2 MASc students will be involved in the program during the following five years. The students will work on different projects but given the good research practice adopted by the applicant many years ago, they will be interacting (with the other students already in the program under the supervision/co-supervision of the applicant) and will be working together. They will acquire training that is to some extent scarcely available: engineers with skills in both mechanical and electrical engineering and with the capability to model and integrate concepts and ideas in products. The results of this research are easily predictable. Car accidents make more than 1 million victims while more than 50 million people are injured every year. Self-driving cars have proved to be extremely safe for the (un-revealed) conditions that they have been used. As there is no detail provided by Google for this type of vehicle rather than all cars are regular vehicles that have been modified, academic research is needed in this area as the applications definitely have huge potential. The proposed research starts with the concept of low cost instrumentation. An accurate model will be able to provide solutions for suitable set of sensors and collect information from adjacent vehicles. On another hand, development of sensors based on unexploited phenomena represents a subject of interest to the industry. The students who will acquire their training while working in the two topics will gain extremely valuable knowledge and skills that will make them real assents for their employers.

拟议的研究计划旨在开发方法，以便开发用于建立其状态和条件的复杂系统模型。复杂的系统从发动机到植物，从细胞到生物。这是拟议研究的基本原理。因此，需要界定这些系统的一些具体特征，并根据调查结果制定一种能够开发模型的方法。这项研究的灵感来自谷歌在自动驾驶汽车上进行的沉默实验。申请人和他最后一个毕业的博士生最近进行的一个项目产生了非常有趣的结果，可能与无人驾驶汽车方法有关。该项目的重点是建模车辆和受损的司机。因此，在这种情况下，建模将针对车辆在交通中行驶的情况。自动驾驶系统是一种常规车辆，配备有一组传感器和关于要行驶的道路的预定信息（地图），计算能力以及执行纠正措施以避免任何类型的碰撞的致动器。无人驾驶车辆可能需要考虑减少传感器的集合，使得这种汽车可以变得负担得起。然而，需要提供所需的信息，并将在本项目范围内考虑其他手段。因此，计划从相邻车辆收集关于预览道路和静态或移动障碍物的信息。系统的模型将有所不同，但性能需要保持在安全条件内。申请人计划接近导致长期研究计划的某些方面。这是传感器的发展，可以在极其恶劣的环境中工作，这将使真实的时间预测的寿命涡轮机发动机。这就涉及到新型传感器的开发研究。该计划将在短期内培养五名HQP - 2博士和两名MASc学生将在未来五年内参与该计划。学生将从事不同的项目，但鉴于申请人多年前采用的良好研究实践，他们将（与申请人监督/共同监督下已经参加该项目的其他学生）进行互动，并将共同工作。他们将获得在某种程度上几乎无法获得的培训：工程师拥有机械和电气工程方面的技能，并有能力在产品中建模和整合概念和想法。这项研究的结果很容易预测。每年有100多万人死于车祸，5000多万人受伤。事实证明，自动驾驶汽车在（未透露的）使用条件下非常安全。由于谷歌没有提供这种类型的车辆的细节，而不是所有的汽车都是经过改装的常规车辆，因此需要在这方面进行学术研究，因为应用程序肯定具有巨大的潜力。拟议的研究从低成本仪器的概念开始。一个精确的模型将能够为合适的传感器组提供解决方案，并从相邻车辆收集信息。另一方面，基于未开发现象的传感器的开发代表了行业感兴趣的主题。学生谁将获得他们的培训，而在这两个主题的工作将获得极其宝贵的知识和技能，这将使他们真实的赞同他们的雇主。