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- Title
Random forest-based multi-faults classification modeling and analysis for intelligent centrifugal pump system.
- Authors
Chang, Kyuchang; Park, Seung Hwan
- Abstract
This paper proposes experimental results and a modeling procedure to analyze the factors affecting the defects of centrifugal pumps. The centrifugal pump is an essential mechanical element. Pump malfunction can cause problems in the entire system because most production facilities are equipped with various centrifugal pumps. Therefore, the diagnosis of pump failure is essential, and several studies have proposed data-driven methodologies for detecting faults in pump systems. Most studies have performed fault classification using only the vibration signal of the pump. However, this study approached the problem in three ways to improve FDC performance. Firstly, this study tried to collect various signals such as pressure, flow, and motor current as well as vibration signals. That is because various signals are likely to indicate the performance of the pump system considering the mechanical characteristics of the centrifugal pump. Secondly, this study presented a methodology for extracting features that can be used in machine learning models from the signals. For objective analysis, not only various statistics were calculated, but also features in the frequency domain were extracted. Using the extracted features, random forest modeling was applied and higher performance was achieved in experiments. Lastly, variables that significantly affected the anomaly detection of the pump were suggested. In order to analyze experimental results of feature importance, we have presented a procedure to introduce a correlation matrix of cosine similarity, which is a new method that has not been attempted in previous research.
- Subjects
CENTRIFUGAL pumps; MACHINE learning; RANDOM forest algorithms
- Publication
Journal of Mechanical Science & Technology, 2024, Vol 38, Issue 1, p11
- ISSN
1738-494X
- Publication type
Article
- DOI
10.1007/s12206-023-1202-2