MEMS for diagnostic and health monitoring of complex systems

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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.