مؤسسه بین المللی توسعه دانش فردای ایرانیان با همکاری دانشگاه تهران برگزار می کند:
Diagnosis, Prognosis, and Health Monitoring of Complex Industrial Systems
Abstract: The increasing complexity of industrial systems such as gas turbines, aerospace systems, transportation systems, to name a few, and the cost reduction measures that have affected the manufacturers and maintenance operators are increasingly driving the need for more intelligence and autonomous capabilities and functionalities for diagnosis, prognosis, and health management (DPHM) of these systems. Maintenance cost accounts for a large part of the ownership cost and the current maintenance strategy for most industrial systems is preventive in which maintenance actions are managed along schedules suggested by manufactures. These schedules are based on historical data, empirical knowledge, and tests performed in design processes and have little to do with the actual condition of the system. To reduce the maintenance cost, predictive and condition-based maintenance is desirable in which maintenance actions are performed whenever they are actually needed.
In this talk, we provide a summary of the research outcomes and accomplishments that we have recently achieved and developed in the DPHM domain. The presented results are categorized into three main groups, namely i) model-based approaches, ii) data-driven and computational intelligence-based methods and iii) hybrid methodology, where a hybrid method refers to a scheme that invokes both model-based and data-driven/computational intelligence-based approaches.
Our main objectives have been to develop modularized concepts for autonomous and intelligent health monitoring, diagnosis, and prognosis to: 1) provide intelligent automated data analysis functionalities and capabilities, 2) reduce maintenance costs and minimize the chances of catastrophic failures through early detection and monitoring solutions, 3) provide a significant reduction in service engineering and maintenance operations that are labor intensive and involve error prone data analysis tasks, 4) address difficult to process problems that cannot be solved quickly or accurately with conventional diagnosis methods, and 5) to provide a robust, reliable, and accurate monitoring, diagnosis, and validation system that can operate with actual data. Applications to gas turbine engines and aerospace systems will be provided to demonstrate the capabilities of our proposed technologies.
Concordia University, Canada
مدت و زمان بندی:چهارشنبه 9 اسفند 1396 از ساعت 13:00 الی 14:30
آدرس محل برگزاری:
تهران، خیابان کارگر شمالی، پردیس دانشکدههای فنی دانشگاه تهران، دانشکده مهندسی برق و کامپیوتر