[논문]LightGBM을 이용한 수력 펌프 유량 추정의 실험적 연구

정진범; 이지환; 강명철

doi:10.7839/ksfc.2023.20.4.123

LightGBM을 이용한 수력 펌프 유량 추정의 실험적 연구
An Experimental Study on the Estimation Flow-rate of Venturi Pump Using LightGBM 원문보기

드라이브ㆍ컨트롤 = Journal of drive and control, v.20 no.4, 2023년, pp.123 - 132

정진범 (Korea Construction Equipment Technology Institute) , 이지환 (Korea Construction Equipment Technology Institute) , 강명철 (Korea Construction Equipment Technology Institute)

Abstract ▼ AI-Helper

In disaster situations, to facilitate rapid drainage, electric underwater pumps are installed manually. This poses a high risk of electric shock accidents due to a short circuit, and a lot of time is required for hose connection and installation of electrical devices. To solve these problems, a Venturi pump using the venturi effect without external power is used. However, Venturi pumps that operate without external power make it difficult to install flow sensors such as electric devices; consequently, it is difficult to check the real-time flow rate. This paper proposes a flow estimation logic to replace the function of the flow sensor for the venturi pump . To develop the flow estimation logic, the flow characteristics of the venturi pump, according to the operating conditions, were checked. After that, the relationship with the flow rate of the venturi pump was defined using a pressure sensor corresponding to a low-cost sensor. Finally, an analysis of the estimation error was performed using the developed flow estimation logic.

주제어

표/그림 (36)

그림 Fig. 1 Circuit of the water drainage pump system
그림 Fig. 2 Circuit of the water drainage pump system
표 Table 1 Specification of DAQ System
표 Table 2 Level for each factor of the water drainage pump system
표 Table 3 Level for each factor of the water drainage pump system
그림 Fig. 3 Logging Period
그림 Fig. 4 Data changes According to noise reduction
표 Table 4 Outlier Condition
표 Table 5 Changes in Data Samples According to pre-processing
그림 Fig. 5 Distribution of Flow-rate according to Independent Variable
표 Table 6 Examination Results
표 Table 7 Flow-rate Status According to Independent Variable
표 Table 8 Specification of Dataset
표 Table 9 Ratio of Train and Validation Dataset
그림 Fig. 6 Heatmap of correlation coefficient
그림 Fig. 7 Distribution of Variable Density According to Data-set
그림 Fig. 8 Analysis of Venturi Pump Outlet Flow-rate Mode
표 Table 10 Dataset Status According to Dataset Type
그림 Fig. 9 Result of Flow Estimation using Multi-variable Regression
그림 Fig. 10 Result of Flow-rate Estimation using Initial LightGBM
표 Table 11 Variable of Multi-variable Regression Model
표 Table 12 Result of Multi-variable Regression Model
표 Table 13 Parameter of LightGBM for Regression
표 Table 14 Result of Initial LightGBM Model
그림 Fig. 11 Result of Flow-rate Estimation using Tuned LightGBM
그림 Fig. 12 Feature Important of Input Variable
그림 Fig. 13 Comparison of Estimation Models
표 Table 15 Error of Flow-rate According to Input Variable
표 Table 16 Comparison of flow estimation
그림 Fig. 14 Define of Transient and Steady-state Phase
그림 Fig. 15 Error of Transient States
그림 Fig. 16 Comparison of Error according to Variable Type and Flow-rate State
표 Table 17 Comparison of Transient and Steady-state Phase
표 Table 18 Estimation Error of Flow-rate by Variable Type
표 Table 19 Estimation of Flow-rate according to Variable Value Change
표 Table 20 Estimation Error according to Variable Value change and Flow-rate State Unit: Flow-rate(LPM)

참고문헌 (12)

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상세보기
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