A Study On The Application Of AAKR Based Early Warning System For ICE



Hyungcheol Min Heesoo Kim Seokman Sohn Yongchae Bae


Internal Combustion Engine (ICE) is a major type of power generating plant in the islands area and Emergency Diesel Generator (EDG) for nuclear power plants. All the electricity workload in the islands area are supported by
diesel engine but due to harsh environmental conditions and lack of manpower, diesel engines in islands are not managed properly. The components of diesel engines are mostly affected by electrical/mechanical problems. Therefore, engines installed in islands area are vulnerable to unexpected failure and replaced earlier than the expected product life. In this paper, we suggest an early warning algorithm that detects machine breakdown before it happens to prevent unexpected machine failure. By applying this algorithm, we expect a life extension of the diesel engine through proactive and efficient maintenance. AAKR (Auto Associative Kernel Regression) is a multivariate state signal estimation model, which is a core algorithm of the early warning system. This algorithm compares saved normal state data with acquired present state data and calculates the weight to create estimation signals.
In this research, we analyzed 2 diesel engine operation data and confirmed that the algorithm works properly. In the future, based on this research result, we plan to build an IoT (Internet of Things) based diesel engine early warning system.

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