RSF-based model for predicting pump failure trends in tunnels

Abstract

The water pump is a piece of crucial electromechanical equipment to ensure the safety of tunnels. Therefore, it’s essential to master the performance trend of pumps to prevent the occurrence of failure. In this paper, essential information and failure records of pumps in 15 operating tunnels for many years were collected. According to the data characteristics, a data-filling model based on XGBoost is developed to address the issue of the censored data. Considering that most pumps are still in operation, a failure prediction model based on Random Survival Forest (RSF) is designed by incorporating survival analysis principles. The proposed Pump Failure Trend Prediction Model (PFTPM) overcomes difficulties caused by the lack of previous data and the small number of old pumps. We identify two phases of failure: the first phase exhibits a bathtub-shaped failure rate curve, while the second phase is characterized by a lower failure risk. The importance of considering rainfall, pump operating time, and performance changes for effective maintenance planning is emphasized. Furthermore, we summarize the failure evolution law of various types of pumps to amend maintenance cycle in the existing specification. Overall, this paper integrates innovative big-data technologies into the traditional maintenance data of tunnel pumps.