Publication Type : Conference Paper
Publisher : Advances in Electrical and Computer Technologies,
Source : Advances in Electrical and Computer Technologies, Springer Singapore, Singapore (2020)
Url : https://link.springer.com/chapter/10.1007/978-981-15-5558-9_85
ISBN : 9789811555589
Keywords : Condition monitoring, fan, Machine learning, Radiator Cooling, Vibration signals
Campus : Coimbatore
School : School of Engineering
Department : Mechanical Engineering
Year : 2020
Abstract : Radiator cooling fan assembly in an internal combustion engine-driven automobile ensures that the heat evacuated by radiator from the engine is safely evacuated to the surrounding air. Any malfunction of this assembly is hence, likely to be detrimental to the functioning of the engine itself. In this paper, a low-cost machine learning-based technique to monitor and detect six operating conditions in an automotive radiator cooling fan assembly is presented. Twenty-six machine learning-based systems are empirically evaluated for their effectiveness. Results indicate that the proposed technique is capable of a detection accuracy in excess of 98%.
Cite this Research Publication : R. Meena, Nair, B. B., and Dr. Sakthivel N.R., “Machine Learning Approach to Condition Monitoring of an Automotive Radiator Cooling Fan System”, in Advances in Electrical and Computer Technologies, Singapore, 2020.