基于特征分布对齐与多传感器融合的水泵异常检测方法  

Water Pump Anomaly Detection Method Based on Feature Distribution Alignment and Multi-sensor Fusion

作　　者：林朝晖 廖奕校 招智铭 万智勇 周松斌 LIN Zhaohui;LIAO Yixiao;ZHAO Zhiming;WAN Zhiyong;ZHOU Songbin(Shimen Waterworks,Guangzhou Water Supply Co.,Ltd.,Guangzhou 510000,China;Institute of Intelligent Manufacturing,Guangdong Academy of Sciences,Guangzhou 510070,China)

机构地区：[1]广州市自来水有限公司石门水厂,广东广州510000 [2]广东省科学院智能制造研究所,广东广州510070

出　　处：《自动化与信息工程》2025年第1期36-40,46,共6页Automation & Information Engineering

摘　　要：水泵是供水系统的重要加压设备,对其进行异常检测并及时发现运行异常,对保障供水安全具有重要意义。针对现有人工智能方法在水泵异常检测中存在的异常样本获取困难、检测精度低等问题,提出一种基于特征分布对齐与多传感器融合的水泵异常检测方法。该方法以多通道传感信号的对数梅尔谱为输入;利用卷积自编码网络来融合多传感器信息;以最小化信号重构损失和特征分布损失为目标,训练卷积自编码网络;基于重构损失计算样本的异常分数,实现水泵的异常检测。实验结果表明,该方法有效提高了水泵异常检测的性能。The water pump is an important pressurization device in the water supply system.Conducting anomaly detection and timely detection of operational abnormalities is of great significance for ensuring water supply safety.A water pump anomaly detection method based on feature distribution alignment and multi-sensor fusion is proposed to address the problems of difficulty in obtaining anomaly samples and low detection accuracy in existing artificial intelligence methods for water pump anomaly detection.This method takes the logarithmic Mel spectrum of multi-channel sensing signals as input;Using convolutional autoencoder networks to fuse multi-sensor information;Train a convolutional autoencoder network with the goal of minimizing signal reconstruction loss and feature distribution loss;Calculate the anomaly score of samples based on reconstruction loss to achieve anomaly detection of water pumps.The experimental results show that this method effectively improves the performance of water pump anomaly detection.

关 键 词：水泵异常检测 数据重构 特征分布对齐 多传感器融合 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]