http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
규칙기반과 딥러닝을 동시에 활용한 앙상블 회전체 이상진단
이남정(Namjeong Lee),김성민(Sungmin Kim),정일주(Iljoo Jeong),손석만(Seokman Sohn),이승철(Seungchul Lee) 한국소음진동공학회 2020 한국소음진동공학회 논문집 Vol.30 No.2
Unlike the major equipment used in power plants, auxiliary equipment usually does not possess a real-time system to analyze the machine condition. Therefore, detecting the fault of such auxiliary equipment in advance is difficult. Thus, the diagnosis of auxiliary equipment at a less cost is important for minimizing the downtime due to the fault of the equipment. In this paper, we introduce a diagnosis method for auxiliary equipment in power plants using rule-based and deep-learning algorithms. First, we calculate the probability of cause of a fault from current symptoms by using the rule-based algorithm. The rule used in this algorithm is established based on expert experience. We then conduct orbit detection using a convolution neural network. This algorithm self-learns the filter to classify orbit images as normal, rubbing, and unbalanced. The weakness of the deep-learning algorithm can be compensated by combining the results of the aforementioned methods.