Development of a sdms (Self-diagnostic monitoring system) with prognostics for a reciprocating pump system

• Main Authors: Wooshik Kim, Chanwoo Lim, Jangbom Chai
• Format: Article
• Language: English
• Published: Elsevier 2020-06-01
• Series: Nuclear Engineering and Technology
• Subjects: SDMS (self-diagnostic monitoring system); Reciprocating pump; Machine learning; LR(Logistic regression); ANN (artificial neural network); SVM (support vector machine)
• Online Access: http://www.sciencedirect.com/science/article/pii/S1738573319308204

In this paper, we consider a SDMS (Self-Diagnostic Monitoring System) for a reciprocating pump for the purpose of not only diagnosis but also prognosis. We have replaced a multi class estimator that selects only the most probable one with a multi label estimator such that we are able to see the state of each of the components. We have introduced a measure called certainty so that we are able to represent the symptom and its state. We have built a flow loop for a reciprocating pump system and presented some results. With these changes, we are not only able to detect both the dominant symptom as well as others but also to monitor how the degree of severity of each component changes. About the dominant ones, we found that the overall recognition rate of our algorithm is about 99.7% which is slightly better than that of the former SDMS. Also, we are able to see the trend and to make a base to find prognostics to estimate the remaining useful life. With this we hope that we have gone one step closer to the final goal of prognosis of SDMS.