基于 RSF 的隧道泵故障趋势预测模型

Xin Wu, Qianru Chen, Min Hu, Lining Gan, Li Teng
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摘要

水泵是确保隧道安全的关键机电设备。因此,掌握水泵的性能变化趋势,预防故障的发生至关重要。本文收集了 15 座运营多年的隧道水泵的基本信息和故障记录。根据数据特征,建立了基于 XGBoost 的数据填充模型,以解决数据删减的问题。考虑到大多数水泵仍在运行,结合生存分析原理,设计了基于随机生存森林(RSF)的故障预测模型。所提出的泵故障趋势预测模型(PFTPM)克服了因缺乏以往数据和旧泵数量较少而造成的困难。我们确定了两个故障阶段:第一阶段呈现浴缸形故障率曲线,而第二阶段的故障风险较低。我们强调了考虑降雨量、水泵运行时间和性能变化对有效制定维护计划的重要性。此外,我们还总结了各类水泵的故障演变规律,以修正现有规范中的维护周期。总之,本文将创新的大数据技术融入到隧道水泵的传统维护数据中。
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RSF-based model for predicting pump failure trends in tunnels
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.
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