Developing innovative numerical/experimental schemes for monitoring and optimization of mechanical systems

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One of the main rivals to productivity in various industries is unplanned downtime. To avoid any potential interruption in operation and unnecessary downtime, maintenance engineers need to acquire and process real-time monitoring data of machine parts to be able to respond in a smart and planned way. My overall research program has addressed this concern with developing combined generic numerical and experimental schemes for monitoring and optimization that can be translated into practical design tools for various industrial sectors to increase productivity, improve quality and avoid catastrophic failures. Long term goal of this research program is to provide industry with novel and efficient online monitoring techniques for rotating equipment and to facilitate the optimization of mechanical processes. The developed method will be aided to customize monitoring online approach for early detection of defects and faulty operating conditions oriented for bearings. A second target application will be advanced optimization of cooling process in steel making industry for producing tailored steels, reducing energy consumption and for green environment. The specific objectives of this 5-year research program are: a) to develop online monitoring tools for rotary machines with emphasize on roller element bearings and journal bearings and b) to develop a simulation tool to model and optimize the cooling process of steel plates on runout tables in steel mills. Faulty bearings may lead to significant failures to rotating machinery and the demand for effective and reliable health monitoring techniques for various bearings continues to grow. We intend to utilize online measurements with advanced signal processing in producing a design tool suitable for early diagnose of incipient defects and estimation of remaining safe life of faulty bearings. Advanced techniques of signal processing and optimization such as wavelet packet transform (WPT) and fuzzy logic and genetic algorithms (FGA) will be investigated and modified for the condition monitoring that combines vibration/acoustic measurements, finite element modeling and inverse analysis for detection and identification of faulty bearing. The second specific objective is concerned with optimizing the process parameters for cooling of steel plates on runout tables (ROT) in order to obtain steels with novel mechanical properties. The cooling in ROT is typically associated with boiling heat transfer and turbulent boiling impingement pool of water on a hot moving plate passing through multiple impinging water jets. Problems in surface temperature measurements led to measuring sub-surface temperature (1 mm below the surface) and to the use of inverse heat conduction (IHC) analysis to quantify heat fluxes and cooling rates. Various gradient-free and stochastic optimization techniques such as particle swarm method will be modified and implement into the IHC analysis to calculate surface temperature and heat flux from readings of the impeded thermocouples. Our extensive experimental database obtained from testing at a pilot scale UBC ROT facility will be used for validation of simulations and constructing a modern computational engineering model for ROT cooling to assess effects of key ROT variables in steel production such as plate temperature, plate velocity, jet configuration, and jet-lines spacing.