Fault detection in FinFans – physics based modeling vs. machine learning

Daniel Marrable1, Mr Shiv Meka1, Mr Amir Najafi Amin2, Dr Kristoffer McKee2, Mr Nathan Jombwe3, Prof Ian Howard2

1Curtin Institute For Computation, Curtin University, Bentley, Australia 6102,

2School of Civil and Mechanical Engineering, Curtin University, Bentley, Australia 6102,

3Cisco Innovation Center, Bentley, Australia 6102

 

Air-cooled heat exchangers are commonly used in fractional distillation rigs. Finfans are a major component in these exchangers. A typical oil and gas refinery employs several tens of arrays of finfans – fans that are used to control temperature of the distillation column. Operating conditions, mechanical wear, and anomalies in operational procedures imply that finfans are more prone to faults. Given the complexity in detecting faults that are associated with finfans – bearing and corrosion, several physics based diagnostic approaches were proposed in the past. Rigorous in nature, these methods may have shortcomings owing to the challenges that are involved in the overall heat-exchanger setup.

In this talk, work related to a recently concluded project that was accomplished in collaboration with Curtin Institute for Computation, Woodside, and Faculty of Science and Engineering at Curtin University, would be presented. The research involved architecting methods to detect finfan faults, and comparing different design paradigms – one which is physics based and the other that is data driven. The talk also elaborates on the bottom-up technicalities and intricacies that are involved in – the choice of sensors/micro-controllers/communication protocols, designing energy efficient workflows, code structuring, embedded controller pleasant machine learning architectures, and testing.


Biography:

Shiv Meka works as a HPC Specialist for Curtin Institute for Computation – a Data and Computational science institute setup to help Curtin researchers with computational research problems. He has a bachelors degree in electrical engineering from India and received his masters in Materials Science and Engineering from Texas A&M University, College Station, in 2009. He has since been involved with research relating computational materials science, quantum transport (esp. transport in nanoscale junctions), and multiscale modeling. Although not part of his formal training, computational “experiments” have become his passion. As a HPC/”Catalyzing” Specialist, he will collaborate with faculty in Science and Engineering, and seek avenues  to accelerate the discovery process. He also periodically reviews journal articles in IEEE Transactions on Nanotechnology, Journal of Nanotechnology, and Journal of Chemical Physics.

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