Today's data acquisition and storage technologies have enabled manufacturers to acquire product field performance
as well as manufacturing process information in the form of event logs containing massive datasets.
Events are recorded in response to system state or operation changes such as critical value changes in parametric data.
Failures can be defined as system faults or performance degradation, which are strongly related with events or directly parametric data.
Events are triggered by value changes in the corresponding sensor data.
Therefore, optimal strategies for the diagnosis of product field failures will be developed.
The advantages of the event-driven failure analysis approach are (i) cost effective for pattern extraction and matching as compared
to parametric data-driven approaches, (ii) capability of including functional interactions of complex systems and user behaviour for failure analysis
and (iii) systematic and hierarchical reasoning of the root causes of failures.
Development of sensor-based virtual plant engineering technology for plant O&M (2014-2018) Ministry of Trade, Industry & Energy, with e-MainTec Co. Ltd. (Co-PI):
Large sensor data manipulation
Sensor data relative importance and interaction analysis for a oil plant units
Development of a root cause of failure analysis method for ship telematics (2011-2013) Ministry of Education, Science and Technology, HYUNDAI HEAVY INDUSTRIES CO.,LTD (PI):
HiMSEN engine monitoring data acqusition
Sensor data preprocessing, Pattern analysis, Fault pattern extraction
