水利信息化Issue(2)：31-38,44,9.DOI:10.19364/j.1674-9405.2025.02.006
基于深度学习的工业水系统异常检测算法研究
Research on deep learning-based anomaly detection algorithm for industrial water systems
摘要
Abstract
With the development of intelligent industrial water systems,the demand for operational safety and stability has increased,making anomaly detection a key research direction.Deep learning,with its advantages in modeling dynamic environments,has gradually become an important technology for anomaly detection.This study reviewed the application of deep learning in anomaly detection for industrial water systems,analyzing the research progress from four perspectives:supervised,semi-supervised,unsupervised,and self-supervised learning.The study focused on the characteristics and applicable scenarios of models such as AnomalyTrans,DCdetector,and THOC,and compared nine anomaly detection algorithms or models through experiments to evaluate their performance on industrial water system datasets.The results showed that deep learning algorithms have overall better detection accuracy in industrial water systems than traditional statistical and machine learning algorithms,demonstrating stronger robustness and higher detection accuracy.Specifically,AnomalyTrans model was suitable for high-stability scenarios,DCdetector model excelled in resource-constrained environments,and THOC model had significant advantages in real-time applications with low computational overhead.These findings confirm the practicality of deep learning in industrial water systems of different scales and provide innovative pathways for the engineering implementation of safety protection in industrial water systems.关键词
工业水系统/异常检测/深度学习/机器学习Key words
industrial water systems/anomaly detection/deep learning/machine learning分类
信息技术与安全科学引用本文复制引用
刘永利,郭明媛,晁浩..基于深度学习的工业水系统异常检测算法研究[J].水利信息化,2025,(2):31-38,44,9.基金项目
国家自然科学基金项目(62273290) （62273290）
河南理工大学基本科研业务费专项项目(NSFRF240310) （NSFRF240310）
