Cognitive artificial-intelligence for doernenburg dissolved gas analysis interpretation

Karel Octavianus Bachri (Bandung Institute of Technology)
Umar Khayam (Bandung Institute of Technology)
Bambang Anggoro Soedjarno (Bandung Institute of Technology)
Arwin Datumaya Wahyudi Sumari (Bandung Institute of Technology)
Adang Suwandi Ahmad (Bandung Institute of Technology)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 17, No 1: February 2019

This paper proposes Cognitive Artificial Intelligence (CAI) method for Dissolved Gas Analysis (DGA) interpretation adopting Doernenburg Ratio method. CAI works based on Knowledge Growing System (KGS) principle and is capable of growing its own knowledge. Data are collected from sensors, but they are not the information itself, and thus, data needs to be processed to extract information. Multiple information are then fused in order to obtain new information with Degree of Certainty (DoC). The new information is used to identify faults occurred at a single observation. The proposed method is tested using the previously published dataset and compared with Fuzzy Inference System (FIS) and Artificial Neural Network (ANN). Experiment shows CAI implementation on Doernenburg Ratio performs 115 out of 117 accurate identification, followed by Fuzzy Inference System 94.02% and ANN 78.6%. CAI works well even with small amount of data and does not require trainings.